JP2019053737A - Prediction apparatus, prediction method, and prediction program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To appropriately predict the demand for a business target of a predetermined business operator. A prediction device according to the present application has an acquisition unit and a prediction unit. The acquisition unit acquires business information that is information about the business of a predetermined business operator and includes reservation information including attributes of a user who has made a reservation for an area where the predetermined business operator is located. The forecasting unit is based on which range of the ranges corresponding to each level of demand is the score output by the model by inputting data based on the business information acquired by the acquisition unit into the model. Therefore, the demand for the target of the business of the predetermined business and the target corresponding to the attribute of the user is predicted. [Selection diagram] FIG. 4

Description

  The present invention relates to a prediction device, a prediction method, and a prediction program.

  Conventionally, techniques for predicting various demands have been provided. For example, a technique for determining user's need information based on user search data including at least POI (Point of Interest) data is provided.

JP 2017-16625 A

  However, in the above-described conventional technology, it is not always possible to appropriately predict the demand for the business object of a predetermined business operator. For example, in the above prior art, since at least POI data is required, it may be difficult to predict demand appropriately when the POI data is insufficient or the POI data is inaccurate.

  The present application has been made in view of the above, and an object thereof is to provide a prediction device, a prediction method, and a prediction program that appropriately predict demand for a business object of a predetermined operator.

  The prediction apparatus according to the present application acquires business information that is business information that is information related to a business of a predetermined business and includes reservation information including an attribute of a user who made a reservation related to an area where the predetermined business is located. And the score output by the model by inputting data based on the business information acquired by the acquisition unit into the model is a value in any range among the ranges corresponding to each level of demand And a prediction unit that predicts a demand related to a target of the predetermined business and corresponding to the attribute of the user.

  According to the aspect of the embodiment, there is an effect that it is possible to appropriately predict the demand for the business target of a predetermined business operator.

FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. FIG. 2 is a diagram illustrating an example of a prediction process according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the prediction system according to the embodiment. FIG. 4 is a diagram illustrating a configuration example of the prediction device according to the embodiment. FIG. 5 is a diagram illustrating an example of a learning data storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of the model information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of a demand information storage unit according to the embodiment. FIG. 8 is a flowchart illustrating an example of the generation process according to the embodiment. FIG. 9 is a flowchart illustrating an example of the prediction process according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the prediction device.

  Hereinafter, a mode for carrying out a prediction device, a prediction method, a prediction program, learning data, and a model according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the prediction device, the prediction method, the prediction program, the learning data, and the model according to the present application are not limited by this embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(Embodiment)
[1. Generation process)
First, an example of the generation process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. In FIG. 1, the model of the model is determined using business information including information (reservation information) related to a reservation corresponding to the business content of the business where the prediction apparatus 100 is located and the location (area) of the business, search information (search information), and the like. The case of generating is shown. The business information is not limited to the above, but includes various information. Details of the business information will be described later.

  In addition, FIG. 1 illustrates a case where a model is generated with information indicating the level of demand at each date and time based on the performance of a business operator with a prediction device 100 as correct answer information. For example, the correct answer information may be information indicating the level of demand on the date and time (period) corresponding to the correct answer information with respect to the overall average demand. In the example of FIG. 1, the correct answer information may be information indicating the level of sales at the date and time corresponding to the correct information for the overall average sales as the level of demand. Hereinafter, the business information associated with the correct answer information is also referred to as “learning data”.

  In addition, the prediction device 100 uses the generated model based on business information including information on the business (business information) and analysis information corresponding to the business area of the business at a certain date and time. Predict the demand for the business object of the business operator at a predetermined date and time. The prediction device 100 provides a service based on information indicating the predicted demand of the business of the business operator, and this point will be described with reference to FIG.

[Configuration of prediction system]
First, prior to the description of FIG. 1, the prediction system 1 shown in FIG. 3 will be described. As illustrated in FIG. 3, the prediction system 1 includes an operator device 10, an information providing device 50, and a prediction device 100. The business entity apparatus 10, the information providing apparatus 50, and the prediction apparatus 100 are connected via a predetermined network N so as to be communicable by wire or wirelessly. FIG. 3 is a diagram illustrating a configuration example of the prediction system according to the embodiment. Note that the prediction system 1 illustrated in FIG. 3 may include a plurality of business entity apparatuses 10, a plurality of information providing apparatuses 50, and a plurality of prediction apparatuses 100.

  The business entity apparatus 10 is an information processing apparatus used by a business entity. Here, the business operator is not limited to a corporation but may be an individual, and is a concept including various business operators such as companies, administrative institutions, public organizations, and public transportation. In the example of FIGS. 1 and 2, the business entity apparatus 10 is a bakery MA located in the A area.

  The business entity apparatus 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. Hereinafter, the business entity apparatus 10 may be referred to as a business entity. That is, in the following, the administrator can be read as the business entity apparatus 10. The manager SA1 of the bakery MA shown in FIG. 2 may display information on the prediction device 100 or request processing from the prediction device 100 by operating the business entity device 10.

  The information providing apparatus 50 is an information processing apparatus that provides various services to the user. Note that the information providing apparatus 50 may be an information processing apparatus that provides information on various services provided to the user. For example, the information providing apparatus 50 may be a variety of services such as a means for reserving a predetermined service such as a transportation means such as a railroad or an airplane, an accommodation facility such as a hotel, or a restaurant (restaurant). Provide electronic commerce services.

  Further, the information providing apparatus 50 provides a search service that provides a search result for a query (search query) acquired from a terminal device (not shown) used by the user to the terminal device. Further, for example, the information providing apparatus 50 may be an information processing apparatus that provides an electronic commerce service related to shopping or auction for trading products. The information providing apparatus 50 collects various information related to the service as described above and provides it to the prediction apparatus 100. Further, for example, the information providing apparatus 50 may collect various information as described above from other external apparatuses and provide the collected information to the prediction apparatus 100. Further, the prediction device 100 and the information providing device 50 may be integrated.

  The prediction device 100 is an information processing device that predicts demand related to a business object of a predetermined business operator based on business information that is information related to the business of the predetermined business operator. In the example of FIG. 2, the prediction device 100 is a model used for prediction of demand related to a business target of a predetermined business, and based on the output of the model to which business information is input, the business of the predetermined business Predict the demand for the target.

  Moreover, the prediction apparatus 100 provides a service based on the information regarding the demand regarding the business object of the predetermined | prescribed predetermined provider. In the example of FIG. 2, the prediction device 100 provides information indicating the demand for bread that is a product of the bakery MA in the area A at the date and time DA21.

  In the example of FIGS. 1 and 2, for the sake of simplicity of explanation, the business operator generates a model based on the past business performance of the bakery MA located in the A area, and uses the generated model, a certain date and time. The case where the demand of the target “bread” is predicted is shown.

  First, the case where the prediction apparatus 100 produces | generates the model M1 used for demand prediction using FIG. 1 is demonstrated.

  The prediction device 100 acquires business operator information from the business operator (step S11). For example, the prediction device 100 acquires business operator information from the business operator device 10 used by the business operator. In the example of FIG. 1, the prediction device 100 acquires business information INF <b> 11 including business content, location where the business is located, past sales results, and the like. For example, the prediction device 100 acquires business information indicating that the business content of the bakery MA is a bakery and is located in the A area. In addition, for example, the prediction device 100 acquires business entity information including sales information indicating sales on the dates DA11 to DA14. The date and time here may be a predetermined period including the date and time. For example, the date and time may be a predetermined period before the date and time (for example, within 24 hours from the date and time). For example, the date and time DA11 and the date and time DA12 may include overlapping periods.

  Note that “* (* is an arbitrary number)” following “DA” in each date and time “DA *” shown in the example of FIG. 1 indicates the date and time when the corresponding action or the like was performed, and the date and time “DA *”. The date becomes newer as the value of “*” is larger. For example, the date and time “DA11” has a smaller numerical value following “DA” than the date and time “DA12”, so that the date and time “DA11” is a date and time corresponding to the past than the date and time “DA12”.

  The prediction apparatus 100 that has acquired the business operator information generates the analysis business operator information INF11-1 by analyzing the business operator information INF11 (step S12). In the example of FIG. 1, the prediction device 100 generates analysis provider information INF11-1 from the provider information INF11. For example, the prediction apparatus 100 generates the analysis company information INF11-1 including information indicating the level of demand on each date DA11 to DA14 based on the sales corresponding to each date DA11 to DA14. For example, the prediction apparatus 100 generates analysis business operator information INF11-1 including information indicating the level of demand for each date and time DA11 to DA14 based on the difference between the sales corresponding to each date and time DA11 to DA14 and the overall average sales. Generate. In the example of FIG. 1, the demand is indicated by “high”, “medium”, and “low”, but may be a score (numerical value). For example, the prediction device 100 may use a score that increases as the sales are higher than the average and decreases as the sales are lower than the average. For example, the prediction apparatus 100 may use a score (numerical value) obtained by normalizing the demand corresponding to each date and time so as to take a value from 0 to 1. In the example of FIG. 1, for the sake of explanation and simplicity, the description will be made assuming that the demand “high” = “1”, the demand “medium” = “0.5”, and the demand “low” = “0”.

  The prediction device 100 acquires information for analysis from the information providing device 50 (step S13). For example, the prediction device 100 acquires analysis information including reservation information, search information, and the like from the information providing device 50 that provides a reservation service or a search service. For example, the prediction device 100 acquires the analysis information INF21 corresponding to the business content and position of the bakery MA. In the example of FIG. 1, the prediction device 100 acquires analysis information INF21 including reservation information, search information, and the like at dates DA10 to DA13. In this way, the prediction device 100 acquires reservation information, search information, and the like that the information providing device 50 uses by other companies other than the predetermined company. That is, the prediction device 100 acquires information related to the bakery MA from the information providing device 50 used by a business other than the bakery MA that is the predetermined business.

  Here, the reservation information BDT10 to BDT13 shown in the analysis information INF21 indicates reservation information corresponding to each of the dates DA10 to DA13. In FIG. 1, the reservation information BDT10 is illustrated by reference numerals, but the reservation information includes various area-related information such as the reservation status of the moving means to the area (target area) including the location of the operator and the reservation status of the hotel in the target area Shall be included.

  For example, the reservation information BDT10 includes information indicating the reservation status of the moving means to the A area at the date and time DA10, the reservation status of the hotel in the target area, and the like. For example, the reservation information BDT10 includes information indicating the reservation status of the moving means that moves to the A area at the date and time DA10 during the date and time DA11. For example, the reservation information BDT10 includes information indicating a reservation status of staying at the date and time DA11 in the A area at the date and time DA10.

  For example, the reservation information BDT11 includes information indicating the reservation status of the moving means to the A area at the date and time DA11, the reservation status of the hotel in the target area, and the like. For example, the reservation information BDT11 includes information indicating the reservation status of the moving means that moves to the A area at the date and time DA11 during the date and time DA12. For example, the reservation information BDT11 includes information indicating a reservation status of staying at the date and time DA12 in the A area at the date and time DA11.

  For example, the reservation information BDT12 includes information indicating the reservation status of the means for moving to the A area at the date and time DA12, the reservation status of the hotel in the target area, and the like. For example, the reservation information BDT12 includes information indicating the reservation status of the moving means that moves to the A area at the date and time DA12 during the date and time DA13. For example, the reservation information BDT12 includes information indicating the reservation status of staying at the date and time DA13 in the A area at the date and time DA12.

  For example, the reservation information BDT13 includes information indicating the reservation status of the moving means to the A area at the date and time DA13, the reservation status of the hotel in the target area, and the like. For example, the reservation information BDT13 includes information indicating the reservation status of the moving means that moves to the A area at the date and time DA13 during the date and time DA14. For example, the reservation information BDT13 includes information indicating the reservation status of staying at the date and time DA14 in the A area at the date and time DA13.

  Here, the search information SDT10 to SDT13 shown in the analysis information INF21 indicates search information corresponding to each of the dates DA10 to DA13. In FIG. 1, the search information SDT10 is illustrated with reference numerals, but the search information includes various information such as the business contents of the business operator and the number of searches related to the target (bread) provided by the business operator.

  For example, the search information SDT10 includes information indicating the number of searches using a query related to the bakery MA at the date and time DA10. For example, the search information SDT10 includes information indicating the number of searches using a predetermined name related to bread at the date and time DA10.

  For example, the search information SDT11 includes information indicating the number of searches using a query related to the bakery MA at the date and time DA11. For example, the search information SDT11 includes information indicating the number of searches using a predetermined name related to bread at the date and time DA11.

  For example, the search information SDT12 includes information indicating the number of searches using a query related to the bakery MA at the date and time DA12. For example, the search information SDT12 includes information indicating the number of searches using a predetermined name related to bread at the date and time DA12.

  For example, the search information SDT13 includes information indicating the number of searches using a query related to the bakery MA at the date and time DA13. For example, the search information SDT13 includes information indicating the number of searches using a predetermined name related to bread at the date and time DA13.

  And the prediction apparatus 100 adds the data which combined analysis provider information INF11-1 and the information for analysis INF21 as learning data (step S14). In the example of FIG. 1, the prediction device 100 generates learning data that uses demands such as the dates DA11 to DA14 included in the analysis provider information INF11-1 as correct information. Specifically, the prediction device 100 adds learning data to the learning data storage unit 121.

  For example, as the correct answer information RDT1-1, the prediction device 100, as shown in the analysis operator information INF11-1, correct information RDT1- indicating that the demand for the bakery MA in the A area at the date and time DA11 is “high”. Data DT1-1 including 1 is generated as learning data. Further, the prediction device 100 generates, as learning data, data DT1-1 including, as input information, business content information EDT1 indicating the business content of the bakery MA and position information ADT1 indicating that the bakery MA is located in the A area. . Further, the prediction device 100 generates, as learning data, data DT1-1 including, as input information, reservation information BDT10 and search information SDT10 at a date and time DA10 that is earlier than the date and time DA11 corresponding to the correct answer information RDT1-1.

  As described above, when “data DT * (* is an arbitrary numerical value)” is described, it indicates that the data is data identified by the data ID “DT *”. For example, when “data DT1-1” is described, the data is data identified by the data ID “DT1-1”.

  Further, for example, as the correct answer information RDT1-2, the prediction device 100 has correct information indicating that the demand of the bakery MA in the A area at the date and time DA12 is “medium” as shown in the analysis company information INF11-1. Data DT1-2 including RDT1-2 is generated as learning data. Further, the prediction device 100 generates, as learning data, data DT1-2 including, as input information, business content information EDT1 indicating the business content of the bakery MA and position information ADT1 indicating that the bakery MA is located in the A area. . Further, the prediction device 100 generates, as learning data, data DT1-2 including, as input information, reservation information BDT11 and search information SDT11 at a date and time DA11 that is earlier than the date and time DA12 corresponding to the correct answer information RDT1-2.

  For example, as the correct answer information RDT1-3, the predicting apparatus 100 correct information RDT1- indicating that the demand for the bakery MA in the A area at the date DA13 is “medium” as indicated by the analysis operator information INF11-1. 3 is generated as learning data. Further, the prediction device 100 generates, as learning data, data DT1-3 including, as input information, business content information EDT1 indicating the business content of the bakery MA and position information ADT1 indicating that the bakery MA is located in the A area. . Further, the prediction device 100 generates, as learning data, data DT1-3 including, as input information, reservation information BDT12 and search information SDT12 at a date and time DA12 that is earlier than the date and time DA13 corresponding to the correct answer information RDT1-3.

  For example, as the correct answer information RDT1-4, the prediction apparatus 100 correct information RDT1- indicating that the demand for the bakery MA in the area A at the date and time DA14 is “medium” as indicated by the analysis company information INF11-1. Data DT1-4 including 4 is generated as learning data. Further, the prediction device 100 generates, as learning data, data DT1-4 including, as input information, business content information EDT1 indicating the business content of the bakery MA and position information ADT1 indicating that the bakery MA is located in the A area. . In addition, the prediction device 100 generates, as learning data, data DT1-4 including, as input information, reservation information BDT13 and search information SDT13 at a date and time DA13 that is earlier than the date and time DA14 corresponding to the correct answer information RDT1-4.

  And the prediction apparatus 100 produces | generates a model based on learning data (step S15). As described above, the prediction device 100 generates a model using the reservation information and search information on the date and time before the date and time corresponding to each correct answer information as input information. Thereby, the prediction apparatus 100 can generate a model for predicting future demand by inputting the current reservation status and search status. As described above, the prediction device 100 is a model that predicts demand related to a predetermined business based on reservation information and search information possessed by another business and sales information of a predetermined business (for example, bakery MA). Is generated.

  For example, the prediction device 100 generates a model by performing learning using the data DT1-1 to DT1-4 in the learning data storage unit 121 as learning data (teacher data). For example, the prediction device 100 generates a model using a feature amount related to the business content of the business operator. For example, the prediction device 100 generates a model using a feature amount related to an area where the business operator is located. Further, for example, the prediction device 100 generates a model using a feature amount related to reservation information. For example, the prediction device 100 generates a model by using feature quantities relating to moving means relating to areas, reservation status of accommodation facilities, and the like. In addition, for example, the prediction device 100 generates a model using the feature amount related to the search information related to the business of the business operator. For example, the prediction device 100 generates a model using a feature quantity related to the number of searches using a query related to a business of a business operator. As described above, the prediction device 100 learns the characteristics included in the business information, and generates a model for predicting the demand related to the target of the business. In the example of FIG. 1, the prediction device 100 learns characteristics related to information included in business information, and generates a model for predicting a target demand related to the business information. The features included in the business information learned by the prediction device 100 may be input to the prediction device 100 by a person such as an administrator of the prediction device 100, or may be automatically learned (extracted) by the prediction device 100. Also good.

  For example, the prediction device 100 generates a model M1 using learning data as shown in the learning data storage unit 121. For example, when the prediction apparatus 100 is “1” indicating that demand is high as in the correct answer information RDT1-1, when the business information included in the data DT1-1 is input to the model M1, the model M1 The learning process is performed so that the score output by “1” approaches “1”. For example, when the business content information EDT1, the position information ADT1, the reservation information BDT10, and the search information SDT10 of the data DT1-1 are input to the model M1, the prediction device 100 approaches the score output by the model M1 to “1”. Thus, the learning process is performed.

  Further, for example, when the prediction device 100 is “0.5” indicating that the demand is medium (average) as in the correct answer information RDT1-2, the business included in the data DT1-2 in the model M1 When the information is input, when the business information included in the data DT1-2 is input to the model M1, the learning process is performed so that the score output by the model M1 approaches “0.5”. For example, when the business content information EDT1, the position information ADT1, the reservation information BDT12, and the search information SDT12 of the data DT1-2 are input to the model M1, the prediction apparatus 100 has a score output by the model M1 of “0.5”. The learning process is performed so that

  Further, for example, when the prediction device 100 is “0” indicating that demand is low as in the correct answer information RDT1-4, when the business information included in the data DT1-4 is input to the model M1, When the business information included in the data DT1-4 is input to the model M1, the learning process is performed so that the score output by the model M1 approaches “0”. For example, when the business content information EDT1, the position information ADT1, the reservation information BDT14, and the search information SDT14 of the data DT1-4 are input to the model M1, the prediction device 100 has a score output by the model M1 of “0.5”. The learning process is performed so that

  Note that the model learning technique is not limited to the technique described above, and any known technique can be applied. Each model may be generated using various conventional techniques relating to machine learning as appropriate. For example, the model generation may be performed using a technique related to machine learning of supervised learning such as SVM (Support Vector Machine). Further, for example, the model generation may be performed using a technique related to machine learning of unsupervised learning. For example, the generation of the model may be performed using a deep learning technique. For example, the model generation may be performed by appropriately using various deep learning techniques such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network). The description relating to the generation of the model is merely an example, and the generation of the model may be performed by a learning method appropriately selected according to information that can be acquired. That is, the prediction device 100 can generate the model M1 by any method as long as the model M1 can be learned so as to output the score corresponding to the correct answer information when the business information included in the learning data is input. May be performed.

  With the above processing, in the example of FIG. 1, the prediction device 100 generates a model (model M1) identified by the model ID “M1” as shown in the model information storage unit 122. As described above, when “model M * (* is an arbitrary numerical value)” is described, it indicates that the model is a model identified by the model ID “M *”. For example, when “model M1” is described, the model is a model identified by the model ID “M1”. Further, as shown in the model information storage unit 122 in FIG. 1, the model M1 is a usage “demand forecast (sales)”, that is, a model used for forecasting demand. Data MDT1 ”. For example, the prediction device 100 inputs business information including analysis information corresponding to the date and time in the model M1, and thus the business after a predetermined period has elapsed from the date and time corresponding to the analysis information included in the input business information. A score indicating the demand related to the operator's target is output to the model M1, and the demand related to the operator's target is predicted based on the score output from the model M1.

  As described above, the prediction device 100 can generate a model that can appropriately predict demand in a certain area by learning using learning data in which business information and correct answer information are associated with each other. Therefore, the prediction apparatus 100 can make it possible to accurately predict the demand related to the target of a predetermined operator, for example, by using the model generated as described above. In the example described above, the correct information RDT1-1 of the data DT1-1 is information indicating the demand of the bakery MA in the A area at the date and time DA11. The data DT1-1 includes, as input information, reservation information BDT10 and search information SDT10 at a date and time DA10 that is earlier than the date and time DA11 corresponding to the correct answer information RDT1-1. As described above, the prediction device 100 generates a model using the reservation information and the search information on the date and time before the date and time corresponding to the correct answer information RDT1-1 as input information. Thereby, the prediction apparatus 100 can generate a model for predicting future demand by inputting the current reservation status and search status.

[2. (Prediction process)
An example of the prediction process according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram illustrating an example of a prediction process according to the embodiment. In FIG. 2, when the prediction apparatus 100 is requested by the bakery MA to predict the demand at the date and time DA21, the prediction device 100 predicts the demand related to the target “bread” of the bakery MA business at the date and time DA21, and displays information based on the prediction. Indicates the case where it is provided.

  First, the prediction device 100 acquires analysis information from the information providing device 50 (step S21). For example, the prediction device 100 acquires analysis information including reservation information, search information, and the like from the information providing device 50 that provides a reservation service or a search service. For example, the prediction device 100 acquires the analysis information INF22 corresponding to the business content and position of the bakery MA. In the example of FIG. 2, the prediction device 100 acquires analysis information INF22 including reservation information, search information, and the like at the date and time DA20.

  Here, the reservation information BDT20 shown in the analysis information INF22 indicates reservation information corresponding to each date and time DA20. For example, the reservation information BDT20 includes information indicating the reservation status of the moving means to the A area at the date and time DA20, the reservation status of the hotel in the target area, and the like. For example, the reservation information BDT20 includes information indicating the reservation status of the moving means that moves to the A area at the date and time DA20 during the date and time DA21. For example, the reservation information BDT20 includes information indicating a reservation status of staying at the date and time DA21 in the A area at the date and time DA20. For example, the search information SDT20 includes information indicating the number of searches using a query related to the bakery MA at the date and time DA20. For example, the search information SDT20 includes information indicating the number of searches using a predetermined name related to bread at the date and time DA20. Note that the process of step S21 may be performed in advance.

  And the prediction apparatus 100 acquires the request | requirement of the demand prediction in date DA21 from bakery MA (step S22). In the example of FIG. 2, the prediction device 100 acquires information including business information INF12 of the bakery MA as a demand prediction request at the date and time DA21. For example, the prediction device 100 acquires the business operator information INF12 from the business operator device 10 used by the manager SA1 of the bakery MA. For example, the prediction apparatus 100 acquires business information INF12 including business content information EDT1 indicating the business content of the bakery MA and location information ADT1 indicating that the bakery MA is located in the A area.

  And the prediction apparatus 100 produces | generates the input data which combined the provider information INF12 and the information for analysis INF22 (step S23). In the example of FIG. 1, the prediction device 100 generates, as input data, data DT21 including business content information EDT1 and position information ADT1 included in the business operator information INF11 as shown in the input data list IPD21. Further, as shown in the input data list IPD21, the prediction device 100 generates, as input data, data DT21 including reservation information BDT20 and search information SDT20 at a date and time DA20 that is earlier than the date and time DA21 that is the target of demand prediction.

  The prediction device 100 that has generated the data DT21 inputs the data DT21 into the model. For example, the prediction device 100 inputs the data DT21 to the model M1. As described above, the prediction apparatus 100 uses the reservation information and search information possessed by another business operator, the sales information of the predetermined business operator (for example, bakery MA), and the model M1 to determine the demand related to the predetermined business operator. Predict. For example, the prediction device 100 predicts demand related to a predetermined business by inputting reservation information and search information possessed by another business and sales information of the predetermined business to the model M1.

  In the example of FIG. 2, the prediction device 100 calculates a score indicating the demand related to the target “bread” of the business of the bakery MA at the date and time DA21 by the process as shown in the process group PS21. The prediction device 100 inputs the data DT21 to the model M1 (Step S24). Specifically, the prediction apparatus 100 inputs business information including business content information EDT1, position information ADT1, reservation information BDT20, search information SDT20, and the like to the model M1. The model M1 to which the data DT21 is input outputs a score (step S25). In the example of FIG. 2, the model M1 to which the data DT21 is input outputs a score “0.75” as indicated by the score SC11.

  And the prediction apparatus 100 estimates a demand based on the score which shows the demand regarding the object "bread" of the business of bakery MA in date DA21 (step S26). In the example of FIG. 2, when the score output by the model M1 to which the business information is input is less than 0.3, the prediction device 100 predicts that the target demand of the business corresponding to the business information is “low”. . Further, when the score output by the model M1 to which the business information is input is 0.3 or more and less than 0.7, the prediction device 100 predicts that the target demand of the business corresponding to the business information is “medium”. . Moreover, when the score output by the model M1 to which the business information is input is 0.7 or more, the prediction device 100 predicts that the target demand of the business corresponding to the business information is “high”.

  As described above, in the example of FIG. 2, the prediction device 100 determines whether the level of the target of the business corresponding to the business information at the requested date is “low”, “medium”, or “high”. Predict. As shown in the demand information storage unit 123 in FIG. 2, the prediction device 100 predicts the demand for bread of the bakery MA located in the A area at the date and time DA21 as “high”.

  Thereafter, the prediction device 100 provides information based on the predicted demand (step S27). In the example of FIG. 2, the prediction device 100 provides information indicating that the demand for bread of the bakery MA located in the A area at the date and time DA21 is high to the business entity device 10.

  As described above, the prediction device 100 predicts the demand related to the business target at the date and time that is the target of the demand prediction based on the business information including the analysis information before the date and time that is the target of the demand prediction. In the example of FIG. 2, the prediction device 100 inputs business information related to the bakery MA to the model M1, thereby causing the model M1 to output a score related to the demand of the business (bread) of the bakery MA. And the prediction apparatus 100 estimates that the demand of the object (bread) of the business of bakery MA in the date and time used as prediction object is so high that the score which model M1 outputs is high. Then, the prediction device 100 provides the predicted demand information to the business operator device 10. As described above, the prediction device 100 can provide appropriate information on demand prediction to the business operator.

(2-1. Business information)
(2-1-1. Type of business information)
In addition, said input information is an example and the prediction apparatus 100 is good also as not only said example but various information as input information. For example, the prediction apparatus 100 may generate a model using area-related information (hereinafter, also simply referred to as “area information”) related to an area corresponding to the position of the business operator corresponding to the correct answer information. In this case, the prediction device 100 may predict a demand related to a business target of a predetermined business using business information including area information.

  For example, the area information includes information on congestion of the area at the corresponding date and time (congestion information), information on the traffic jam of the area at the corresponding date and time (congestion information), and information on the weather of the area at the corresponding date and time (weather information) Etc. may be included. For example, the area information here may include various sensor information detected in each area. The sensor information includes, for example, information indicating congestion such as the flow of people detected by various sensors in each area, information indicating congestion such as the flow of cars, weather information such as temperature and humidity, and the like. May be. Note that the area information may include various information such as global information that does not depend on the location, such as information related to the day of the week or the date and time.

  For example, the congestion information may be information indicating the congestion level of the area based on the number of users located in the area at the corresponding date and time. For example, the congestion information may be information indicating the population density based on the number of users located in the area at the corresponding date and time.

  For example, the traffic jam information may be information indicating the traffic jam status of the area based on the number of vehicles located in the area at the corresponding date and time, the size of the road, or the like. For example, the traffic jam information may be information indicating an average speed at which a vehicle can move in the area based on the number of vehicles located in the area at the corresponding date and time.

  For example, the weather information may be information indicating the weather in the area at the corresponding date and time. For example, the weather information may be information indicating whether the weather in the area is sunny or rainy at the corresponding date and time.

  For example, the prediction device 100 generates a model using a feature amount related to area congestion information. For example, the prediction device 100 generates a model using a feature amount related to the density of people in the area. In addition, for example, the prediction device 100 generates a model using a feature amount related to traffic congestion information of an area. For example, the prediction device 100 generates a model using a feature amount related to the number of cars located in the area. Further, for example, the prediction device 100 generates a model using a feature amount related to the weather information of the area. For example, the prediction device 100 generates a model using the feature amount related to the weather in the area.

  Note that the prediction device 100 may generate a model that takes into account feature quantities relating to road traffic information of the area. For example, the prediction apparatus 100 may generate a model that takes into account the topographic feature amount of the area. As described above, the prediction device 100 learns the features included in the area information and generates a model for predicting a target (event) related to the area information. In the example of FIG. 1, the prediction device 100 learns characteristics related to the density of people in the area included in the area information, the road condition (the road thickness and the degree of traffic congestion), and the like of the predetermined operator. Generate a model that predicts demand for a business object. The features included in the area information learned by the prediction device 100 may be input to the prediction device 100 by a person such as an administrator of the prediction device 100 or may be automatically learned (extracted) by the prediction device 100. Also good.

(2-1-2. Business information of other companies)
1 and 2 show an example in which a model is generated based on information about the bakery MA, and the demand for the bakery MA is predicted by inputting the information about the bakery MA into the generated model. However, the prediction device 100 may predict the demand using information of another business operator. For example, the prediction device 100 may predict the demand using information on a business operator similar to the bakery MA. For example, the prediction device 100 generates a model using business information including sales information of a bakery located in the same area A as the bakery MA, and predicts demand for the bakery MA using the model. Also good. For example, the prediction device 100 may generate a model using business information of a plurality of bakers located in the same area A as the bakery MA, and may predict demand for the bakery MA using the model.

  In addition, for example, the prediction device 100 may generate a model using information on businesses of similar industries located in the same area A as the bakery MA. For example, the prediction device 100 may generate a model using business operator information including sales of business operators of similar industries located in the same area A as the bakery MA. For example, the prediction device 100 may generate a model using information of a plurality of businesses such as a convenience store and a coffee shop located in the area A, and may predict demand for the bakery MA using the model.

  For example, when predicting demand for one business operator, the prediction device 100 may use information such as the performance of other business operators on the condition that consent is obtained for using the information. For example, when predicting a demand for one operator, the prediction device 100 may use information on an operator located in the same area as the one operator or an operator located in an adjacent area. For example, when predicting the demand for one operator, the prediction device 100 is located in an operator located in the same area as the one operator or in an adjacent area among the operators who have obtained consent to use the information. You may use the information of the operator. For example, when predicting the demand for the bakery MA, the prediction device 100 may predict the demand based on the sales performance of the bakery in the adjacent area B that has agreed to use the information. For example, when the prediction apparatus 100 predicts demand for a bakery MA, the bakery MA allows other businesses to use information such as their sales performance, and so on. You may use the information of the operators. Thereby, the prediction apparatus 100 can promote the mutual use of the information between providers. Moreover, the prediction apparatus 100 can improve the accuracy of prediction by promoting the mutual use of information between operators.

(2-2. Target of prediction)
Note that the above is an example, and the prediction apparatus 100 may predict any demand as long as it is a demand related to a business. For example, the prediction device 100 may predict the demand for a predetermined target in the entire predetermined area. In this case, the prediction device 100 may predict the target demand for the entire area using the business information for the entire predetermined area. For example, the prediction device 100 may generate a model (area model) using information such as sales of the entire A area when predicting the demand for the target “bread” of the entire A area. And the prediction apparatus 100 may predict the demand regarding the object of the whole area in a predetermined date by inputting the business information corresponding to a predetermined date in the produced | generated area model.

  Further, for example, the prediction device 100 may generate a prediction model for any demand as long as it is a demand related to the business. For example, the prediction device 100 may generate a model that predicts demand related to a mobile service such as a railroad or an airplane.

  In the above example, the prediction apparatus 100 has shown a case of predicting demand related to sales, but the demand to be predicted is not limited to sales, but may be various types of information. For example, the prediction device 100 may predict a demand indicating the number of business targets of a predetermined business operator.

  For example, the prediction device 100 may predict a demand (fashion) related to a product or the like sold by a business operator based on a user search. For example, the prediction device 100 may predict a demand related to a product or the like sold by the operator based on the user's search query and the user's position. For example, the prediction device 100 may predict a demand related to a product or the like sold by a business operator based on a target related to a search query used by the user and the position of the user.

  For example, when the user uses a query related to a predetermined disease as a search query, the prediction device 100 may predict the epidemic related to the disease based on the position where the user searches. For example, when the number of times that a user uses a query related to a predetermined disease as a search query is rapidly increasing in a predetermined area, the prediction device 100 may predict that the disease corresponding to the query is prevalent in that area. For example, in the prediction device 100, the number of times that a user uses a query related to a predetermined disease as a search query in a predetermined area is a predetermined ratio (for example, twice or five times) in a predetermined period (for example, one week). If the number is increasing, it may be predicted that the disease corresponding to the query is prevalent in the area. For example, the prediction device 100 may predict an influenza epidemic in a predetermined area based on the number of times a query related to influenza used by a user in a predetermined area for a search.

  For example, the prediction device 100 may predict a demand related to a business such as an operator who sells pharmaceuticals by predicting a fashion related to a predetermined disease as described above. For example, the prediction device 100 may predict the demand for pharmaceuticals sold by a business operator based on information such as an area where a disease is predicted to be prevalent and a population in the area. For example, the prediction device 100 may predict the demand for pharmaceuticals sold by the business operator based on information such as the past morbidity and the like regarding artificial and illnesses in areas where illnesses are predicted to be prevalent.

  Moreover, the prediction apparatus 100 may use the information regarding another area, when forecasting the demand in a certain area. For example, when predicting the demand in the A area, the prediction device 100 may use information related to the area around the A area. For example, when predicting demand in a certain area for a target whose demand is geographically shifted, such as pollen or influenza, the prediction apparatus 100 may use information related to an area around the area.

(2-3. Prediction method)
In the example of FIG. 2, the case where the prediction is performed using the model is shown. However, the demand related to the business target of a predetermined operator may be predicted without using the model. For example, the prediction device 100 may predict a demand related to a business object of a predetermined business operator based on the analysis business operator information INF11-1. For example, the prediction device 100 extracts regularity of fluctuations in sales and demand in the analysis business operator information INF11-1 and the like, and predicts demand related to the business object of a predetermined business based on the regularity. Good. For example, the prediction device 100 extracts regularity related to the correlation between the change in sales and demand in the analysis company information INF11-1 and the like and the change in information included in the analysis information INF11, and based on the regularity, You may estimate the demand regarding the object of business of a predetermined | prescribed provider. For example, the prediction apparatus 100 extracts regularity related to the correlation between the sales and demand fluctuations in the analysis company information INF11-1 and the like and the fluctuations in the area information as described above, and based on the regularity, You may forecast the demand regarding the object of an operator's business.

(2-4. Information used for prediction)
The prediction device 100 is not limited to the above, and may perform demand prediction using various types of information. For example, the prediction device 100 may perform demand prediction in consideration of user information. For example, the prediction device 100 may perform demand prediction using attribute information of a user who has performed a predetermined action. For example, the prediction apparatus 100 may perform demand prediction using the attribute of the user who performed the action related to the reservation included in the reservation information, the attribute of the user who performed the action related to the search included in the search information, and the like.

  For example, the prediction device 100 may predict the contents of demand using the attribute of the user who performed the action related to the reservation included in the reservation information or the attribute of the user who performed the action related to the search included in the search information. Good. For example, the prediction device 100 predicts the demand for a bakery from a search query, and when the demand for bread is predicted to be high, if the percentage of women among the users who have searched is large, You may expect demand to increase. In addition, the prediction device 100 predicts the demand for bakery from the search query, and when the demand for bread is predicted to be high, if the percentage of women among the users who have searched is large, Information indicating an increase in demand may be provided to the operator.

  For example, the prediction device 100 predicts the demand for bakery from the search query, and when the demand for bread is predicted to be high, the percentage of users (old people) with a high age among the users who have performed the search is large. In this case, the demand for bread for the elderly may be expected to increase. In addition, the prediction device 100 predicts the demand for bakery from the search query, and when the demand for bread is predicted to be high, when the percentage of the elderly is large among the users who performed the search, the elderly prefers Information indicating that demand for such bread (for example, tofu bread) increases may be provided to the operator.

  For example, the prediction device 100 predicts the demand for bakery from the search query, and when the demand for bread is predicted to be high, among the users who performed the search, the user who is usually highly conscious of health (health-oriented) If the ratio of (users) is large, it may be predicted that the demand for bread for health-conscious users will increase. In addition, the prediction device 100 predicts the demand for bakery from the search query, and when the demand for bread is predicted to be high, if the proportion of the health-oriented users is large among the users who performed the search, the health-oriented users Businesses may be provided with information that recommends increasing the amount of material orders for breads that they are likely to like (for example, healthy breads).

[3. Configuration of prediction device]
Next, the configuration of the prediction apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram illustrating a configuration example of the prediction device according to the embodiment. As illustrated in FIG. 4, the prediction device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The prediction device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the prediction device 100, and a display unit (for example, a liquid crystal display) for displaying various types of information. May be.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network by wire or wireless, and transmits / receives information to / from the business operator apparatus 10.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As illustrated in FIG. 4, the storage unit 120 according to the embodiment includes a learning data storage unit 121, a model information storage unit 122, and a demand information storage unit 123.

(Learning data storage unit 121)
The learning data storage unit 121 according to the embodiment stores various types of information related to learning data. FIG. 5 is a diagram illustrating an example of a learning data storage unit according to the embodiment. For example, the learning data storage unit 121 stores teacher data used for generating a model. The learning data storage unit 121 illustrated in FIG. 5 includes items such as “data ID”, “correct information”, and “input information”. The “input information” includes items such as “business content information”, “location information”, “reservation information”, and “search information”.

  “Data ID” indicates identification information for identifying data. For example, the data identified by the data ID “DT1-1” corresponds to the data DT1-1 shown in the example of FIG.

  “Correct answer information” indicates correct information corresponding to data identified by the data ID. For example, “correct answer information” indicates actual demand corresponding to the data identified by the data ID. FIG. 5 shows an example in which conceptual information such as “RDT1-1” is stored in “correct answer information”, but in actuality, demand corresponding to the business content of a predetermined business operator at a predetermined date and time (period). Information indicating the degree of the above (for example, level relative to the average) or a file path name indicating the storage location is stored.

  “Business content information” in the “input information” indicates the business content of the business operator corresponding to the correct answer information. FIG. 5 shows an example in which conceptual information such as “EDT1” is stored in “business content information”. However, in actuality, various information such as information level indicating business content corresponding to correct information, or The file path name indicating the storage location is stored.

  “Position information” in the “input information” indicates the location of the business operator corresponding to the correct answer information. FIG. 5 shows an example in which conceptual information such as “ADT1” is stored in “location information”, but in reality, address and latitude / longitude information indicating a specific location of the operator, or its storage location The file path name indicating

  “Reservation information” in “input information” indicates reservation information included in the input information. FIG. 5 shows an example in which conceptual information such as “BDT10” is stored in “reservation information”, but in actuality, a means for moving to an area (target area) including the position of the business operator corresponding to the correct answer information Various information such as the reservation status of the hotel and the reservation status of the hotel in the target area, or a file path name indicating the storage location is stored.

  “Search information” in “input information” indicates search information included in the input information. FIG. 5 shows an example in which conceptual information such as “SDT10” is stored in “search information”, but in actuality, various information such as the number of searches related to the business contents of the business operator corresponding to the correct answer information, or The file path name indicating the storage location is stored.

  For example, in the example shown in FIG. 5, the data (data DT1-1) identified by the data ID “DT1-1” includes business content information EDT1, reservation information BDT10, search information SDT10, etc. as input information. Show. Further, the data DT1-1 indicates that the correct answer information RDT1-1 is included. For example, the correct information RDT1-1 of the data DT1-1 is information indicating the demand of the bakery MA in the A area at the date and time DA11. The data DT1-1 includes, as input information, reservation information BDT10 and search information SDT10 at a date and time DA10 that is earlier than the date and time DA11 corresponding to the correct answer information RDT1-1. As described above, the prediction device 100 generates a model using the reservation information and the search information on the date and time before the date and time corresponding to the correct answer information RDT1-1 as input information. Thereby, the prediction apparatus 100 can generate a model for predicting future demand by inputting the current reservation status and search status.

  Note that the learning data storage unit 121 is not limited to the above, and may store various information according to the purpose. For example, the learning data storage unit 121 may store information related to the date and time when learning data was added. Further, for example, the learning data storage unit 121 may store information indicating what kind of determination processing each learning data has been added. For example, the learning data storage unit 121 may store information indicating whether each learning data has been determined by an administrator's selection.

(Model information storage unit 122)
The model information storage unit 122 according to the embodiment stores information about the model. For example, the model information storage unit 122 stores model information (model data) generated by the generation process. FIG. 6 is a diagram illustrating an example of the model information storage unit according to the embodiment. The model information storage unit 122 illustrated in FIG. 6 includes items such as “model ID”, “use”, and “model data”. In FIG. 6, only the model M1 is illustrated, but a lot of model information such as M2, M3, M4, and M5 may be stored depending on each application (target of prediction).

  “Model ID” indicates identification information for identifying a model. For example, the model identified by the model ID “M1” corresponds to the model M1 illustrated in the example of FIG. “Use” indicates the use of the corresponding model. “Model data” indicates data of a corresponding model associated with the model data. For example, “model data” includes information including nodes in each layer, functions adopted by the nodes, connection relationships between the nodes, and connection coefficients set for connections between the nodes.

  For example, in the example shown in FIG. 6, the model (model M1) identified by the model ID “M1” has a usage of “demand prediction (sales)” and the demand of the business operator corresponding to the input business information. Indicates that it is used for prediction. The model data of the model M1 is model data MDT1.

  The model M1 (model data MDT1) is an input layer to which business information that is information related to the business of a predetermined business operator is input, an output layer, and any layer from the input layer to the output layer and other than the output layer Including a first element belonging to the layer and a second element whose value is calculated based on the weight of the first element and the first element, and for business information input to the input layer, other than the output layer Using each element belonging to each layer as the first element, the computer is caused to output the score value used for demand prediction from the output layer by performing an operation based on the first element and the weight of the first element. It is a model for.

Here, it is assumed that the model M1 or the like is realized by a regression model represented by “y = a ₁ * x ₁ + a ₂ * x ₂ +... + A _i * x _i ”. In this case, for example, the first element included in the model M1 corresponds to input data (xi) such as x1 or x2. The weight of the first element corresponds to the coefficient ai corresponding to xi. Here, the regression model can be regarded as a simple perceptron having an input layer and an output layer. When each model is regarded as a simple perceptron, the first element can correspond to any node of the input layer, and the second element can be regarded as a node of the output layer.

  Further, it is assumed that the model M1 or the like is realized by a neural network having one or more intermediate layers such as DNN. In this case, for example, the first element included in the model M1 corresponds to any node of the input layer or the intermediate layer. The second element corresponds to the next node, which is a node to which a value is transmitted from the node corresponding to the first element. The weight of the first element corresponds to a connection coefficient that is a weight considered for a value transmitted from a node corresponding to the first element to a node corresponding to the second element.

  The model information storage unit 122 is not limited to the above, and may store various model information according to the purpose.

(Demand information storage unit 123)
The demand information storage unit 123 according to the embodiment stores various types of information related to demand. FIG. 7 shows an example of the demand information storage unit 123 according to the embodiment. The demand information storage unit 123 illustrated in FIG. 7 includes items such as “date and time”, “target”, “area”, “score”, and “demand level”.

  “Date and time” indicates the time (date and time) for which the demand is predicted. “Target” indicates an object whose demand is predicted. “Area” indicates an area to be predicted. “Score” indicates a score that is an evaluation value of demand related to a target in a corresponding area. For example, “demand level” indicates a demand level related to an object in a corresponding area.

  For example, in the example illustrated in FIG. 7, the target whose demand is predicted is the bakery MA, and the predicted date is the date DA21. The score indicating the demand for the bakery MA at the date and time DA21 indicates “0.75”. In addition, the demand level of the bakery MA at the date and time DA21 indicates “high”.

  In addition, the demand information storage part 123 may memorize | store various information according to the objective not only above.

(Control unit 130)
Returning to the description of FIG. 4, the control unit 130 is a controller and is stored in a storage device inside the prediction device 100 by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Various programs (corresponding to an example of a prediction program) are implemented by using the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 130 includes an input layer, an output layer, and an input layer to which business information that is information related to the business of a predetermined operator is input by information processing according to the model M1 and the like stored in the model information storage unit 122 A first element belonging to any layer from the layer to the output layer other than the output layer, and a second element whose value is calculated based on the weight of the first element and the first element , By using each element belonging to each layer other than the output layer as the first element for the business information input to the input layer, the calculation based on the first element and the weight of the first element is used for forecasting demand. The score value to be output from the output layer.

  As illustrated in FIG. 4, the control unit 130 includes an acquisition unit 131, a generation unit 132, a prediction unit 133, and a provision unit 134, and realizes or executes information processing functions and operations described below. . Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 4, and may be another configuration as long as information processing described later is performed. The control unit 130, by information processing according to the model M1 (model data MDT1) stored in the storage unit 120, a second element whose value is calculated based on the first element and the weight of the first element, For the business information input to the input layer, the element that belongs to each layer other than the output layer is set as the first element, and the calculation based on the first element and the weight of the first element is performed, thereby forecasting demand The score value used for is output from the output layer.

(Acquisition part 131)
The acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires various types of information from the learning data storage unit 121, the model information storage unit 122, the demand information storage unit 123, and the like. The acquisition unit 131 may acquire various types of information from an external information processing apparatus. Moreover, the acquisition part 131 may acquire various information from the provider apparatus 10 grade | etc.,.

  The acquisition unit 131 acquires learning data as shown in the learning data storage unit 121. For example, the acquisition unit 131 acquires learning data including the data DT1-1 to DT1 to 4 from the learning data storage unit 121.

  For example, the acquisition unit 131 acquires business information that is information related to a business of a predetermined operator. For example, the acquisition unit 131 acquires business information including information related to the business of a predetermined business operator. For example, the acquisition unit 131 acquires business information including business results of a predetermined business operator. For example, the acquisition unit 131 acquires business information including information related to a business of a business other than a predetermined business. For example, the acquisition unit 131 acquires business information including business results of other business operators. For example, the acquisition unit 131 acquires business information including information regarding the business of another business operator located in a predetermined area where the predetermined business operator is located. For example, the acquisition unit 131 acquires business information including information regarding the business of another business operator located outside a predetermined area where the predetermined business operator is located. For example, the acquisition unit 131 acquires business information including information related to a business of another business that performs a business similar to a business of a predetermined business. For example, the acquisition unit 131 acquires business information including information related to a business of another business that performs a business similar to a business of a predetermined business.

  For example, the acquisition unit 131 acquires business information including information on a predetermined business owned by another business other than the predetermined business. The acquisition unit 131 acquires business information including information held by other businesses other than the predetermined business and sales information of the predetermined business. For example, the acquisition unit 131 acquires business information including area related information regarding an area where a predetermined business operator is located. For example, the acquisition unit 131 acquires business information including reservation information regarding an area where a predetermined business operator is located. For example, the acquisition unit 131 acquires business information including search information related to a business of a predetermined business.

  In the example of FIG. 1, the acquisition unit 131 acquires company information from a company. For example, the acquisition unit 131 acquires company information from the company device 10 used by the company. For example, the acquisition unit 131 acquires the business operator information INF11 including the business content, the location where the business is located, the past sales record, and the like. For example, the acquisition unit 131 acquires business information indicating that the business content of the bakery MA is a bakery and is located in the A area. For example, the acquisition unit 131 acquires company information including sales information indicating sales on the dates DA11 to DA14.

  In the example of FIG. 1, the acquisition unit 131 acquires analysis information from the information providing apparatus 50. For example, the acquisition unit 131 acquires information for analysis including reservation information, search information, and the like from the information providing apparatus 50 that provides a reservation service or a search service. For example, the acquisition unit 131 acquires the analysis information INF21 corresponding to the business content and position of the bakery MA. For example, the acquisition unit 131 acquires the analysis information INF21 including reservation information, search information, and the like for the dates DA10 to DA13.

  In the example of FIG. 2, the acquisition unit 131 acquires analysis information from the information providing apparatus 50. For example, the acquisition unit 131 acquires information for analysis including reservation information, search information, and the like from the information providing apparatus 50 that provides a reservation service or a search service. For example, the acquisition unit 131 acquires the analysis information INF22 corresponding to the business content and position of the bakery MA. For example, the acquisition unit 131 acquires the analysis information INF22 including reservation information, search information, and the like at the date and time DA20.

  In the example of FIG. 2, the acquisition unit 131 acquires a demand prediction request at the date and time DA21 from the bakery MA. For example, the acquisition unit 131 acquires information including business information INF12 of the bakery MA as a demand prediction request at the date DA21. For example, the acquisition unit 131 acquires the company information INF12 from the company apparatus 10 used by the manager SA1 of the bakery MA. For example, the acquisition unit 131 acquires business information INF12 including business content information EDT1 indicating the business content of the bakery MA and location information ADT1 indicating that the bakery MA is located in the A area.

(Generator 132)
The generation unit 132 generates various types of information. For example, the generation unit 132 generates a model as shown in the model information storage unit 122 using the learning data stored in the learning data storage unit 121. For example, the generation unit 132 generates a model used for prediction of demand related to a business object of a predetermined operator based on various information acquired by the acquisition unit 131. For example, the generation unit 132 generates a model used for prediction of demand related to a business object of a predetermined operator based on the learning data acquired by the acquisition unit 131. For example, the generation unit 132 generates demand information related to the business object of the business based on learning data including predetermined business information and correct information indicating demand related to the business object of the business corresponding to the predetermined business information. Generate a model to be used for prediction.

  For example, the generation unit 132 generates a model M1 and the like, and stores the generated model M1 and the like in the model information storage unit 122. Note that the generation unit 132 may generate the model M1 or the like using any learning algorithm. For example, the generation unit 132 generates a model M1 and the like using a learning algorithm such as a neural network, a support vector machine (SVM), clustering, and reinforcement learning. As an example, when the generation unit 132 generates a model M1 or the like using a neural network, the model M1 or the like includes an input layer including one or more neurons, an intermediate layer including one or more neurons, and one or more neurons. Including an output layer.

  The generation unit 132 generates a model and stores the generated model in the model information storage unit 122. Specifically, the generation unit 132 is an input layer to which business information that is information related to the business of a predetermined operator is input, an output layer, and any layer from the input layer to the output layer. Including a first element belonging to a layer other than the first element and a second element whose value is calculated based on the first element and the weight of the first element, and for business information input to the input layer, other than the output layer By using each element belonging to each layer as a first element and performing a calculation based on the first element and the weight of the first element, a model is generated that outputs a score value used for demand prediction from the output layer.

  In the example of FIG. 1, the generation unit 132 generates analysis operator information INF11-1 by analyzing the operator information INF11. For example, the generation unit 132 generates analysis provider information INF11-1 from the provider information INF11. For example, based on the sales corresponding to each date and time DA11 to DA14, the generation unit 132 generates analysis business operator information INF11-1 including information indicating the level of demand for each date and time DA11 to DA14. For example, based on the difference between the sales corresponding to each date and time DA11 to DA14 and the overall average sales, the generation unit 132 generates the analysis company information INF11-1 including information indicating the level of demand for each date and time DA11 to DA14. Generate.

  In the example of FIG. 1, the generation unit 132 generates a model based on the learning data. For example, the generation unit 132 generates a model based on the learning data. For example, the generation unit 132 generates a model by performing learning using the data DT1-1 to DT1-4 in the learning data storage unit 121 as learning data (teacher data).

  In the example of FIG. 1, the generation unit 132 adds data obtained by combining the analysis operator information INF11-1 and the analysis information INF21 as learning data. For example, the production | generation part 132 produces | generates the learning data which makes the demand information of each date DA11-DA14 etc. contained in the analysis provider information INF11-1 correct information. For example, the generation unit 132 adds learning data to the learning data storage unit 121.

  For example, as the correct answer information RDT1-1, the generating unit 132 correct information RDT1- indicating that the demand for the bakery MA in the area A at the date and time DA11 is “high” as indicated by the analysis company information INF11-1. Data DT1-1 including 1 is generated as learning data. For example, the generation unit 132 generates, as learning data, data DT1-1 including, as input information, business content information EDT1 indicating the business content of the bakery MA and position information ADT1 indicating that the bakery MA is located in the A area. . For example, the generation unit 132 generates, as learning data, data DT1-1 including, as input information, reservation information BDT10 and search information SDT10 at a date and time DA10 that is earlier than the date and time DA11 corresponding to the correct answer information RDT1-1.

  In the example of FIG. 1, the generation unit 132 generates a model M1 using learning data as shown in the learning data storage unit 121. For example, when the generation unit 132 is “1” indicating that demand is high as in the correct answer information RDT1-1, when the business information included in the data DT1-1 is input to the model M1, the model M1 The learning process is performed so that the score output by “1” approaches “1”. For example, when the business content information EDT1, the position information ADT1, the reservation information BDT10, and the search information SDT10 of the data DT1-1 are input to the model M1, the generation unit 132 approaches the score output by the model M1 to “1”. Thus, the learning process is performed.

  Further, for example, when the generation unit 132 is “0.5” indicating that the demand is medium (average) like the correct answer information RDT1-2, the business included in the data DT1-2 in the model M1 When the information is input, when the business information included in the data DT1-2 is input to the model M1, the learning process is performed so that the score output by the model M1 approaches “0.5”. For example, when the business content information EDT1, the position information ADT1, the reservation information BDT12, and the search information SDT12 of the data DT1-2 are input to the model M1, the generation unit 132 outputs a score “0.5”. The learning process is performed so that

  For example, when the generation unit 132 is “0” indicating that demand is low as in the correct answer information RDT1-4, the model M1 is input when the business information included in the data DT1-4 is input to the model M1. When the business information included in the data DT1-4 is input to the DT1, the learning process is performed so that the score output by the model M1 approaches “0”. For example, when the business content information EDT1, the position information ADT1, the reservation information BDT14, and the search information SDT14 of the data DT1-4 are input to the model M1, the generation unit 132 outputs a score “0.5”. The learning process is performed so that

  In the example of FIG. 2, the generation unit 132 generates input data that combines the provider information INF12 and the analysis information INF22. For example, as illustrated in the input data list IPD21, the generation unit 132 generates, as input data, data DT21 including business content information EDT1 and position information ADT1 included in the provider information INF11. For example, as shown in the input data list IPD21, the generation unit 132 generates, as input data, data DT21 including reservation information BDT20 and search information SDT20 at a date and time DA20 that is earlier than the date and time DA21 that is the target of demand prediction.

(Prediction unit 133)
The prediction unit 133 predicts various types of information. The prediction unit 133 predicts various information using information stored in the learning data storage unit 121, the model information storage unit 122, the demand information storage unit 123, and the like. For example, the prediction unit 133 predicts various types of information based on various types of information acquired by the acquisition unit 131.

  For example, the prediction unit 133 predicts a demand related to a business target of a predetermined operator based on the business information acquired by the acquisition unit 131. For example, the prediction unit 133 is a model used for prediction of a demand related to a business target of a predetermined business, and based on an output of the model to which business information is input, a demand related to the business target of the predetermined business Predict. The prediction unit 133 predicts demand related to a predetermined business based on information held by another business and sales information of the predetermined business.

  In the example of FIG. 2, the prediction unit 133 predicts the demand based on a score indicating the demand related to the target “bread” of the business of the bakery MA at the date and time DA21. For example, when the score output by the model M1 to which the business information is input is less than 0.3, the prediction unit 133 predicts that the target demand for the business corresponding to the business information is “low”. For example, when the score output by the model M1 to which business information is input is 0.3 or more and less than 0.7, the prediction unit 133 predicts that the target demand of the business corresponding to the business information is “medium”. . For example, when the score output by the model M1 to which the business information is input is 0.7 or more, the prediction unit 133 predicts that the target demand of the business corresponding to the business information is “high”.

  For example, the prediction unit 133 predicts whether the target demand of the business corresponding to the business information at the requested date / time is “low”, “medium”, or “high”. For example, as shown in the demand information storage unit 123 in FIG. 2, the prediction unit 133 predicts the demand for bread of the bakery MA located in the A area at the date and time DA21 as “high”.

  In the above example, the model M1 is a value obtained by quantifying the prediction of demand related to the business object of the predetermined business operator in the predetermined area when the business information that is information related to the business of the predetermined business operator is input. The example which is the model which outputs is shown. However, the model (model X) according to the embodiment may be a model generated based on a result obtained by repeatedly inputting / outputting data to / from the model M1. For example, the model X may be a model (model Y) that is learned so that business information that is information related to a business of a predetermined business operator is input and the score output by the model M1 is output. Alternatively, the model M1 may be a model that is learned so that business information that is information related to a business of a predetermined business operator is input and the output value of the model Y is output. When the prediction unit 133 performs a prediction process using GAN (Generative Adversarial Networks), the model M1 may be a model that constitutes a part of the GAN.

(Providing unit 134)
The providing unit 134 provides various information. For example, the providing unit 134 provides various information to the business entity apparatus 10. The providing unit 134 provides a service based on the demand related to the business object of the predetermined business operator predicted by the prediction unit 133. For example, the providing unit 134 provides information related to the planned sales quantity of the target to be predicted based on the demand related to the business target of the predetermined business operator predicted by the prediction unit 133. Further, for example, the providing unit 134 may provide information regarding the model generated by the generating unit 132 to an external information processing apparatus. For example, the providing unit 134 may provide information output from the model to an external information processing apparatus.

  For example, the providing unit 134 provides information based on the predicted demand. In the example of FIG. 2, the providing unit 134 provides information indicating that the demand for bread of the bakery MA located in the A area at the date and time DA21 is high to the business entity apparatus 10.

For example, the providing unit 134 may provide various services in addition to providing information based on the predicted demand. For example, the providing unit 134 may use information on the predicted demand in various services such as an electronic commerce service and a content distribution service. For example, the providing unit 134 may use a predicted demand for an advertisement distribution service such as advertisement placement.
For example, the providing unit 134 may use the predicted demand for online advertisement placement.

  Further, for example, the providing unit 134 may use information related to the predicted demand in granting an incentive to the user in the electronic commerce service. For example, the providing unit 134 may use information on the predicted demand in coupon distribution or the like. For example, the providing unit 134 may provide an automatic order service such as a product based on the predicted demand. For example, the providing unit 134 may provide an automatic purchase ordering service based on the predicted demand. For example, the providing unit 134 may use a demand predicted for providing information about a price for a product or the like. For example, the providing unit 134 may provide a dynamic pricing service or the like based on the predicted demand. For example, the providing unit 134 may provide a pricing service or the like that automatically determines the price of a product or the like based on the predicted demand. Note that the above is an example, and the providing unit 134 may provide various services based on the predicted demand.

[4. Generation process flow)
Next, the procedure of the generation process by the prediction system 1 according to the embodiment will be described using FIG. FIG. 8 is a flowchart illustrating an example of the generation process according to the embodiment.

  As shown in FIG. 8, the prediction device 100 acquires learning data (step S101). For example, the prediction device 100 acquires learning data from the learning data storage unit 121.

  Thereafter, the prediction device 100 generates a model based on the learning data (step S102). In the example of FIG. 1, the prediction device 100 generates a model M1 using learning data from the learning data storage unit 121.

[5. (Prediction process flow)
Next, the procedure of the prediction process by the prediction system 1 according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart illustrating an example of the prediction process according to the embodiment.

  As illustrated in FIG. 9, the prediction device 100 acquires a demand prediction request (step S201). In the example of FIG. 2, the prediction device 100 acquires the analysis information INF22 for the date and time DA20 from the information providing device 50.

  Further, the prediction device 100 acquires a demand prediction request (step S202). In the example of FIG. 2, the prediction device 100 acquires the business operator information INF12 from the business operator device 10 used by the manager SA1 of the bakery MA as a demand prediction request at the date and time DA21.

  Moreover, the prediction apparatus 100 predicts the demand regarding the object of the business of the predetermined | prescribed provider in an area using a model (step S203). In the example of FIG. 2, the prediction apparatus 100 predicts the demand for bread provided by the bakery MA at the date and time DA21 using the model M1.

  Further, the prediction device 100 provides a service based on the predicted information (Step S204). In the example of FIG. 2, the prediction device 100 provides demand information DML21 at the predicted date and time DA21 to the business entity device 10.

[6. effect〕
As described above, the prediction device 100 according to the embodiment includes the acquisition unit 131 and the prediction unit 133. The acquisition unit 131 acquires business information that is information related to a business of a predetermined business operator. In addition, the prediction unit 133 predicts a demand related to a business target of a predetermined operator based on the business information acquired by the acquisition unit 131.

  As described above, the prediction device 100 according to the embodiment predicts the demand related to the business target of the predetermined business based on the business information that is information related to the business of the predetermined business. Demand for business targets can be predicted appropriately.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information related to a business of a predetermined business operator.

  As described above, the prediction apparatus 100 according to the embodiment can appropriately predict the demand for the business target of the predetermined operator by acquiring the business information including information related to the business of the predetermined operator. .

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including business results of a predetermined business operator.

  As described above, the prediction device 100 according to the embodiment can appropriately predict the demand for the business target of the predetermined business operator by acquiring the business information including the business performance of the predetermined business operator.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information related to a predetermined business owned by another business other than the predetermined business.

  As described above, the prediction apparatus 100 according to the embodiment acquires business information including information related to a predetermined business owned by another business other than the predetermined business, so that the business of the predetermined business can be obtained. Demand can be predicted appropriately.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information held by other businesses other than the predetermined business and sales information of the predetermined business. The prediction unit 133 predicts demand related to a predetermined business based on information held by another business and sales information of the predetermined business.

  As described above, the prediction device 100 according to the embodiment predicts a demand related to a predetermined operator based on information held by another business operator and sales information of the predetermined business operator. Demand for business targets can be predicted appropriately.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including area-related information regarding an area where a predetermined business operator is located.

  As described above, the prediction device 100 according to the embodiment appropriately predicts the demand for the target of the business of the predetermined operator by acquiring the business information including the area related information regarding the area where the predetermined business is located. be able to.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including reservation information regarding an area where a predetermined business operator is located.

  As described above, the prediction device 100 according to the embodiment appropriately predicts the demand for the business target of the predetermined operator by acquiring the business information including the reservation information related to the area where the predetermined operator is located. Can do.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including search information related to a business of a predetermined business operator.

  As described above, the prediction apparatus 100 according to the embodiment can appropriately predict the demand for the target of the business of the predetermined operator by acquiring the business information including the search information related to the business of the predetermined business. .

  Moreover, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information related to businesses of other businesses other than the predetermined business.

  As described above, the prediction device 100 according to the embodiment appropriately obtains the demand for the target of the predetermined business by acquiring the business information including information on the business of the other business other than the predetermined business. Can be predicted.

  In the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including business results of other business operators.

  As described above, the prediction apparatus 100 according to the embodiment can appropriately predict the demand for the business target of a predetermined business operator by acquiring business information including business results of other business operators.

  Moreover, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information on the business of another business operator located in a predetermined area where the predetermined business operator is located.

  As described above, the prediction device 100 according to the embodiment acquires business information including information on the business of another business operator located in a predetermined area where the predetermined business operator is located. Demand for business targets can be predicted appropriately.

  Further, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information on the business of another business operator located outside a predetermined area where the predetermined business operator is located.

  As described above, the prediction apparatus 100 according to the embodiment acquires business information including information related to the business of another business located outside the predetermined area where the predetermined business is located, thereby obtaining the business of the predetermined business. Demand for business targets can be predicted appropriately.

  Further, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information related to a business of another business that performs a business similar to the business of a predetermined business.

  As described above, the prediction device 100 according to the embodiment acquires business information including information related to the business of another business that conducts a business similar to the business of the predetermined business, so that the business of the predetermined business can be obtained. The demand for the target can be appropriately predicted.

  Further, in the prediction device 100 according to the embodiment, the acquisition unit 131 acquires business information including information related to a business of another business that performs a business similar to the business of a predetermined business.

  As described above, the prediction device 100 according to the embodiment acquires business information including information related to the business of another business that conducts a business similar to the business of the predetermined business, thereby obtaining the business of the predetermined business. The demand for the target can be appropriately predicted.

  Further, in the prediction device 100 according to the embodiment, the prediction unit 133 generates a model generated based on the predetermined business information and correct information indicating demand related to the business target of the business operator corresponding to the predetermined business information. Used to predict the demand for the business object of a given operator.

  As described above, the prediction apparatus 100 according to the embodiment uses a model generated based on predetermined business information and correct answer information indicating demand related to a business target of an operator corresponding to the predetermined business information, By predicting the demand related to the business target of the predetermined business operator, the demand for the business target of the predetermined business business can be appropriately predicted.

  In addition, the prediction apparatus 100 according to the embodiment includes a providing unit 134. The providing unit 134 provides a service using information related to the demand related to the business target of the predetermined business operator predicted by the prediction unit 133.

  As described above, the prediction apparatus 100 according to the embodiment provides a service using information related to the demand related to the business target of the predetermined business operator predicted by the prediction unit 133, so that the business operator as the information providing destination can provide the service. A service based on demand in a predetermined area can be provided.

[7. Hardware configuration)
The prediction apparatus 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in FIG. 10, for example. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the prediction device. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, an HDD (Hard Disk Drive) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F). ) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from other devices via the network N and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the network N.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. In addition, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the prediction apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 performs control by executing a program or data (for example, model M1 (model data MDT1)) loaded on the RAM 1200. The function of the unit 130 is realized. The CPU 1100 of the computer 1000 reads and executes these programs or data (for example, model M1 (model data MDT1)) from the recording medium 1800. As another example, these programs or data are transmitted from other devices via the network N. May be obtained.

  As described above, some of the embodiments and modifications of the present application have been described in detail with reference to the drawings. It is possible to carry out the present invention in other forms that have been modified and improved.

[8. Others]
In addition, among the processes described in the above-described embodiments and modifications, all or a part of the processes described as being automatically performed can be manually performed, or are described as being performed manually. All or part of the processing can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  In addition, the above-described embodiments and modifications can be combined as appropriate within a range that does not contradict processing contents.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 Prediction system 100 Prediction apparatus 121 Learning data storage part 122 Model information storage part 123 Demand information storage part 130 Control part 131 Acquisition part 132 Generation part 133 Prediction part 134 Provision part 10 Provider apparatus 50 Information provision apparatus N network

Claims (16)

An acquisition unit that acquires business information including business information that is information related to a business of a predetermined business operator and includes reservation information including an attribute of a user who made a reservation related to an area where the predetermined business operator is located;
The score output by the model by inputting data based on the business information acquired by the acquisition unit into the model is based on which range of values corresponds to each level of demand. A forecasting unit for forecasting demand related to the target of the predetermined business and corresponding to the attribute of the user;
A prediction apparatus comprising: The acquisition unit
The prediction apparatus according to claim 1, wherein the business information including information related to the business of the predetermined business operator is acquired. The acquisition unit
The prediction apparatus according to claim 2, wherein the business information including business results of the predetermined business operator is acquired. The acquisition unit
The prediction apparatus according to claim 1, wherein the business information including information related to the predetermined business owned by another business other than the predetermined business is acquired. The acquisition unit
Obtaining the business information including information held by other businesses other than the predetermined business and sales information of the predetermined business,
The prediction unit
5. The demand for the predetermined business is predicted based on information held by the other business and sales information of the predetermined business. 5. Prediction device. The acquisition unit
The prediction apparatus according to claim 1, wherein the business information including area related information related to an area where the predetermined business operator is located is acquired. The acquisition unit
The prediction apparatus according to claim 1, wherein the business information including search information regarding the business of the predetermined business operator is acquired. The acquisition unit
The prediction apparatus according to claim 1, wherein the business information including information related to a business of another business other than the predetermined business is acquired. The acquisition unit
The prediction apparatus according to claim 8, wherein the business information including business results of the other business operators is acquired. The acquisition unit
The prediction apparatus according to claim 8 or 9, wherein the business information including information related to a business of the other business operator located in a predetermined area where the predetermined business operator is located is acquired. The acquisition unit
The prediction apparatus according to claim 8 or 9, wherein the business information including information related to a business of the other business operator located outside a predetermined area where the predetermined business operator is located is acquired. The acquisition unit
The prediction apparatus according to claim 8, wherein the business information including information related to a business of the other business that performs a business similar to the business of the predetermined business is acquired. The prediction unit
By inputting data based on the business information into the model generated based on predetermined business information and correct information indicating demand regarding the business target of the business operator corresponding to the predetermined business information, The prediction apparatus of any one of Claims 1-12 which predicts the demand regarding the object of the business of the said operator. A providing unit that provides a service by transmitting information related to a demand related to a target of the business of the predetermined operator predicted by the prediction unit to a terminal device used by the predetermined operator;
The prediction apparatus according to claim 1, further comprising: A prediction method performed by a computer,
An acquisition step of acquiring business information including business information which is information related to a business of a predetermined business operator and includes reservation information including an attribute of a user who made a reservation related to an area where the predetermined business operator is located;
The score output by the model by inputting data based on the business information acquired in the acquisition step into the model is based on which range of values corresponds to each level of demand. A forecasting step of forecasting demand related to the target of the predetermined business and corresponding to the attribute of the user;
The prediction method characterized by including. An acquisition procedure for acquiring business information including business information that is information related to a business of a predetermined business and includes reservation information including an attribute of a user who made a reservation related to an area in which the predetermined business is located;
The score output by the model by inputting data based on the business information acquired by the acquisition procedure into the model is based on which range of values corresponds to each level of demand. A prediction procedure for predicting demand related to an object corresponding to the attribute of the user that is an object of the business of the predetermined operator;
A prediction program that causes a computer to execute.

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