System

Abstract

An object of a system according to an embodiment is to accurately grasp the demand of consumers and optimize a store opening plan of a company.SOLUTION: A system includes a demand registration part, a store opening plan support part, and a matching part. The demand registration unit registers a demand of a consumer. The store opening plan support unit supports a store opening plan of the business operator on the basis of the demand registered by the demand registration unit. The matching part matches the store opening plan supported by the store opening plan support part with the demand registered by the demand registration part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult for businesses to accurately grasp consumer demand when opening a new store, making it difficult to predict profits after the store opened.

[0005] The system according to the embodiment aims to accurately grasp consumer demand and optimize store opening plans of businesses. [Means for solving the problem]

[0006] The system according to the embodiment includes a demand registration unit, a store opening plan support unit, and a matching unit. The demand registration unit registers consumer demand. The store opening plan support unit supports a business operator's store opening plan based on the demand registered by the demand registration unit. The matching unit matches the store opening plan supported by the store opening plan support unit with the demand registered by the demand registration unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp consumer demand and optimize store opening plans of businesses. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The matching platform according to an embodiment of the present invention is a system that matches the supply and demand of businesses wishing to open stores in vacant spaces with consumers who want specific services or stores in the vicinity. This allows the matching platform to optimize the investments of businesses and improve the quality of life of consumers.

[0029] A matching platform according to an embodiment includes a demand registration unit, a store opening plan support unit, and a matching unit. The demand registration unit registers consumer demand. For example, a consumer inputs a specific request such as "I want a cafe" or "I want a fitness gym." This information is analyzed by a generation AI and stored as consumer demand data. The store opening plan support unit supports a business operator's store opening plan based on the demand registered by the demand registration unit. For example, the generation AI analyzes consumer demand data and provides the business operator with a profit forecast after opening a store. For example, it makes a specific prediction such as "There is high demand for cafes in this area, so opening a store is expected to result in high profits." The matching unit matches the store opening plan supported by the store opening plan support unit with the demand registered by the demand registration unit. For example, the matching unit matches businesses looking to open a cafe in an area where a consumer registered "I want a cafe." This allows for efficient matching of consumer demand with businesses' store opening plans.

[0030] The demand registration unit allows the generation AI to refer to past consumer behavior data and predict potential demand in response to requests entered by consumers. For example, if a consumer enters "I want a cafe," the demand registration unit analyzes past consumer behavior data and predicts potential demand based on the frequency of cafe use and popularity in the area. For example, in areas where there have been many similar requests in the past, it is predicted that there will be high demand for cafes. The generation AI analyzes consumer behavior data based on, for example, the algorithm used and the type of learning data, and predicts potential demand. This allows for more accurate matching by predicting potential consumer demand.

[0031] When a consumer's request is input into the demand registration unit, the generation AI can suggest related services and stores in real time, expanding the options available. For example, if a consumer inputs "I want a cafe," the demand registration unit will suggest in real time, "In addition to a cafe, how about a bakery or delicatessen?" This allows the consumer to consider other services related to their request. The generation AI will suggest related services and stores based on, for example, the proposal algorithm and the timing of the proposal. This will expand the consumer's options, making it possible to meet a wider variety of needs.

[0032] The demand registration unit can also accept consumer requests via voice or image input, and the generation AI can analyze them to generate demand data. For example, when a consumer says "I want a cafe" via voice input, the generation AI analyzes the voice and converts it into text data to generate demand data. This makes it easy to register requests via voice input. Also, when a consumer uploads a "photo of the cafe" via image input, the generation AI analyzes the image and generates related demand data. For example, image recognition technology can be used to extract the cafe's features and save them as demand data. This improves convenience by allowing consumer requests to be accepted via voice or image input.

[0033] The demand registration unit can add a function that, when a consumer inputs a request, compares it with the requests of other consumers to find common needs. For example, when a consumer inputs "I want a cafe," the demand registration unit allows the generation AI to analyze the request data of other consumers and display the number of other consumers in the same area who have input "I want a cafe." This allows common needs to be confirmed. For example, the generation AI compares the request with other consumers' requests based on needs classification criteria and comparison algorithms to find common needs. This makes it possible to meet the requests of more consumers by finding common needs.

[0034] In the store opening plan support section, the generation AI analyzes the business operator's past store opening data, learning successful and unsuccessful patterns to optimize the store opening plan. In the store opening plan support section, for example, the generation AI analyzes the business operator's past store opening data and extracts commonalities between successful store openings. For example, it learns successful patterns based on location conditions and target customer demographics. The generation AI also analyzes data on unsuccessful store openings and learns failure patterns. For example, it identifies failure patterns based on the presence of competing stores and low demand. This makes it possible to increase the success rate by optimizing store opening plans based on past data.

[0035] The store opening planning support unit can analyze the competitive situation in real time in areas where a business is considering opening a store and evaluate its competitive advantage. For example, the generation AI in the store opening planning support unit collects data on competing stores in real time in areas where a business is considering opening a store and analyzes the competitive situation. For example, it evaluates competitive advantage based on the number of stores in the same industry and sales data. The generation AI uses an algorithm that evaluates competitive advantage based on, for example, the number of competitors and the strength of the competition. This makes it easier to evaluate competitive advantage by analyzing the competitive situation in real time.

[0036] In the store opening plan support section, the generation AI simulates different scenarios for a business operator's store opening plan and can propose an optimal store opening strategy. In the store opening plan support section, for example, the generation AI simulates different location conditions for a business operator's store opening plan and proposes an optimal store opening strategy. For example, it compares multiple location conditions, such as near major transportation hubs or next to commercial facilities. The generation AI simulates different scenarios based on, for example, the scenario setting method and the accuracy of the simulation. In this way, by simulating different scenarios, it is possible to propose an optimal store opening strategy.

[0037] The store opening planning support unit can integrate socioeconomic data for areas where businesses are considering opening stores, and comprehensively evaluate the risks and opportunities for store opening. In the store opening planning support unit, for example, the generation AI analyzes demographic data for areas where businesses are considering opening stores, and evaluates the risks and opportunities for store opening. For example, the evaluation is based on population growth rates and age distribution. The generation AI integrates data based on socioeconomic data collection and analysis methods, and comprehensively evaluates risks and opportunities. In this way, by integrating socioeconomic data, it is possible to comprehensively evaluate the risks and opportunities for store opening.

[0038] The matching unit allows the generation AI to analyze consumer demand data and business store opening plans, and improve the algorithm for optimal matching. For example, the matching unit allows the generation AI to analyze consumer demand data and business store opening plans, and improve the algorithm for optimal matching. For example, it develops a matching algorithm that takes into account the balance between supply and demand. The generation AI improves the algorithm based on, for example, the type of algorithm used and the improvement method. By improving the algorithm, more accurate matching becomes possible.

[0039] The matching unit visualizes the matching results and can provide an interface that can be intuitively understood by businesses and consumers. For example, the generation AI visualizes the matching results and provides an interface that can be intuitively understood by businesses and consumers. For example, the generation AI displays the matching results on a map and visually shows potential store locations. For example, the generation AI visualizes the matching results based on visualization tools and visualization content. In this way, by visualizing the matching results, businesses and consumers can intuitively understand them.

[0040] In the matching unit, the generation AI simultaneously matches different industries and services, making it possible to meet the needs of multiple businesses and consumers at once. In the matching unit, for example, the generation AI simultaneously matches different industries and services, making it possible to meet the needs of multiple businesses and consumers at once. For example, it performs matching to simultaneously meet the demand for cafes and fitness gyms. For example, the generation AI matches different industries and services based on simultaneous processing methods and simultaneous processing targets. This makes it possible to simultaneously match different industries and services, making it possible to meet multiple needs at once.

[0041] The matching unit can predict the economic effects on the entire region based on the matching results and make proposals for regional revitalization. For example, the generation AI in the matching unit predicts the economic effects on the entire region based on the matching results and makes proposals for regional revitalization. For example, it predicts the job creation effect of opening a new store. For example, the generation AI predicts the economic effects based on the prediction model used and the accuracy of the prediction. This makes it possible to predict the economic effects on the entire region and make specific proposals for regional revitalization.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When a consumer inputs a request, the demand registration unit can make personalized suggestions based on past consumer behavior data. For example, if a consumer inputs "I want a cafe," the generation AI analyzes the consumer's past behavioral data and makes specific suggestions such as, "It might be good if a cafe like the one you previously visited opened in this area." This enables suggestions tailored to the consumer's individual needs, resulting in more satisfying service. Also, if a consumer inputs "I want a fitness gym," the generation AI can suggest, "It might be good if a facility like the gym you previously visited opened in this area," based on the consumer's past fitness-related behavioral data. Furthermore, if a consumer inputs "I want a restaurant," the generation AI can suggest, "It might be good if a restaurant like the one you previously visited opened in this area," based on the consumer's past dining history.

[0044] When a consumer inputs a request, the demand registration unit can make suggestions based on the local season and events. For example, if a consumer inputs "I want a cafe," the generation AI analyzes the local season and event information and suggests, "Since many cherry blossom viewing events are held in the spring in this area, it might be a good idea to open a cafe to coincide with the cherry blossom viewing season." This enables suggestions based on the characteristics of the area, supporting more effective store opening plans. Similarly, if a consumer inputs "I want a fitness gym," the generation AI can use information about local sports events to suggest, "Since marathons are held in the summer in this area, it might be a good idea to open a fitness gym to coincide with the marathon season." Furthermore, if a consumer inputs "I want a restaurant," the generation AI can use information about local food culture events to suggest, "Since harvest festivals are held in the fall in this area, it might be a good idea to open a restaurant to coincide with the harvest festival season."

[0045] When a consumer inputs a request, the demand registration unit can make suggestions based on other consumers' reviews and ratings. For example, if a consumer inputs "I want a cafe," the generation AI analyzes other consumers' reviews and ratings and suggests, "Many consumers in this area have given cafes high ratings, so opening a cafe might be a good idea." This makes it possible to take other consumers' opinions into consideration when making suggestions, providing more reliable information. Also, if a consumer inputs "I want a fitness gym," the generation AI can suggest, based on other consumers' reviews and ratings of fitness gyms, "Many consumers in this area have given fitness gyms high ratings, so opening a fitness gym might be a good idea." Furthermore, if a consumer inputs "I want a restaurant," the generation AI can suggest, based on other consumers' reviews and ratings of restaurants, "Many consumers in this area have given restaurants high ratings, so opening a restaurant might be a good idea."

[0046] The demand registration unit can also accept consumer requests via voice or image input, and the generation AI can analyze them to generate demand data. For example, if a consumer says "I want a cafe" via voice input, the generation AI analyzes the voice and converts it into text data to generate demand data. This makes it easy to register requests via voice input. Also, if a consumer uploads a photo of a cafe via image input, the generation AI analyzes the image and generates related demand data. For example, image recognition technology can be used to extract the cafe's features and save them as demand data. This improves convenience by allowing consumer requests to be accepted via voice or image input. Furthermore, if a consumer says "I want a fitness gym" via voice input, the generation AI can analyze the voice and convert it into text data to generate demand data. Similarly, if a consumer says "I want a restaurant" and uploads a photo of the restaurant via image input, the generation AI can analyze the image and generate related demand data.

[0047] The demand registration unit can add a function that, when a consumer inputs a request, compares it with the requests of other consumers to find common needs. For example, if a consumer inputs "I want a cafe," the generation AI analyzes the request data of other consumers and displays the number of other consumers in the same area who have input "I want a cafe." This allows common needs to be confirmed. The generation AI compares the request with other consumers' requests based on, for example, need classification criteria or comparison algorithms to find common needs. This allows the demands of more consumers to be met by finding common needs. Furthermore, if a consumer inputs "I want a fitness gym," the generation AI can analyze the request data of other consumers and display the number of other consumers in the same area who have input "I want a fitness gym." Furthermore, if a consumer inputs "I want a restaurant," the generation AI can analyze the request data of other consumers and display the number of other consumers in the same area who have input "I want a restaurant."

[0048] The store opening plan support unit uses the generation AI to analyze a business's past store opening data, learning patterns of success and failure to optimize the store opening plan. For example, the generation AI analyzes a business's past store opening data and extracts commonalities among successful store openings. For example, it learns patterns of success based on factors such as location and target customer demographics. The generation AI also analyzes data from unsuccessful store openings to learn patterns of failure. For example, it identifies patterns of failure based on factors such as the presence of competing stores and low demand. This increases the success rate by optimizing store opening plans based on past data. Furthermore, the generation AI can propose specific store opening strategies to businesses based on patterns of success and failure. For example, it can make specific predictions such as, "There is high demand for cafes in this area, so opening a store will likely result in high profits." It can also provide specific advice such as, "There are many competing stores in this area, so it would be better to avoid opening a store here."

[0049] The store opening planning support department can analyze the competitive situation in real time in areas where businesses are considering opening stores and evaluate their competitive advantage. For example, the generation AI collects data on competing stores in real time in areas where businesses are considering opening stores and analyzes the competitive situation. For example, it evaluates competitive advantage based on the number of stores in the same industry and sales data. The generation AI uses an algorithm to evaluate competitive advantage based on, for example, the number of competitors and the strength of competition. This makes it easier to evaluate competitive advantage by analyzing the competitive situation in real time. The generation AI can also propose specific store opening strategies to businesses based on data on competing stores. For example, it can provide specific advice such as, "There are few competing stores in this area, so there is a high chance of opening a store." It can also provide specific advice such as, "There are many competing stores in this area, so it would be better to avoid opening a store."

[0050] In the store opening plan support department, the generation AI can simulate different scenarios for a business operator's store opening plan and propose an optimal store opening strategy. For example, the generation AI can simulate different location conditions for a business operator's store opening plan and propose an optimal store opening strategy. For example, it compares multiple location conditions, such as near major transportation hubs or next to commercial facilities. The generation AI simulates different scenarios based on, for example, the scenario setting method and the accuracy of the simulation. By simulating different scenarios, it can propose an optimal store opening strategy. The generation AI can also propose specific store opening strategies to businesses based on the simulation results. For example, it can provide specific advice such as, "In this area, opening a store near major transportation hubs will increase customer attraction." It can also provide specific advice such as, "In this area, opening a store next to a commercial facility will increase competitive advantage."

[0051] The store opening planning support department can integrate socioeconomic data for areas where businesses are considering opening a store and comprehensively evaluate the risks and opportunities for store opening. For example, the generation AI analyzes demographic data for areas where businesses are considering opening a store and evaluates the risks and opportunities for store opening. For example, it evaluates based on population growth rates and age distribution. The generation AI integrates data based on socioeconomic data collection and analysis methods, and comprehensively evaluates risks and opportunities. By integrating socioeconomic data, it can comprehensively evaluate the risks and opportunities for store opening. The generation AI can also propose specific store opening strategies to businesses based on socioeconomic data. For example, it can provide specific advice such as, "Since the youth population in this area is increasing, it may be a good idea to open a store targeting young people." It can also provide specific advice such as, "Since the elderly population in this area is large, it may be a good idea to open a store that provides services for seniors."

[0052] In the matching department, the generation AI analyzes consumer demand data and business store opening plans and can improve the algorithm for optimal matching. For example, the generation AI analyzes consumer demand data and business store opening plans and improves the algorithm for optimal matching. For example, it develops a matching algorithm that takes into account the balance between supply and demand. The generation AI improves the algorithm based on, for example, the type of algorithm used and the improvement method. By improving the algorithm, more accurate matching becomes possible. The generation AI can also propose specific matching strategies to businesses based on the improved algorithm. For example, it can provide specific advice such as, "Since there is a high demand for cafes in this area, it might be a good idea to open a cafe." It can also provide specific advice such as, "Since there is a high demand for fitness gyms in this area, it might be a good idea to open a fitness gym."

[0053] The matching unit can visualize the matching results and provide an interface that businesses and consumers can intuitively understand. For example, the generation AI can visualize the matching results and provide an interface that businesses and consumers can intuitively understand. For example, the generation AI can display the matching results on a map and visually show potential store locations. The generation AI can visualize the matching results based on visualization tools and visualization content, for example. This allows businesses and consumers to intuitively understand the matching results. The generation AI can also propose specific store opening strategies to businesses based on the visualized matching results. For example, it can provide specific advice such as, "There is a high demand for cafes in this area, so it might be a good idea to open a cafe." It can also provide specific advice such as, "There is a high demand for fitness gyms in this area, so it might be a good idea to open a fitness gym."

[0054] In the matching section, the generation AI simultaneously matches different industries and services, enabling it to meet the needs of multiple businesses and consumers at once. For example, the generation AI simultaneously matches different industries and services, satisfying the needs of multiple businesses and consumers at once. For example, it matches cafes and fitness gyms to simultaneously meet the demand. The generation AI, for example, matches different industries and services based on simultaneous processing methods and simultaneous processing targets. This allows it to simultaneously match different industries and services, thereby meeting multiple needs at once. The generation AI can also propose specific store opening strategies to businesses based on the results of simultaneous matching. For example, it can provide specific advice such as, "Since there is high demand for cafes and fitness gyms in this area, it may be a good idea to open both." It can also provide specific advice such as, "Since there is high demand for restaurants and fitness gyms in this area, it may be a good idea to open both."

[0055] The matching unit can predict the economic impact on the entire region based on the matching results and make proposals for regional revitalization. For example, the generation AI predicts the economic impact on the entire region based on the matching results and makes proposals for regional revitalization. For example, it predicts the job creation effect of opening a new store. The generation AI predicts the economic impact based on, for example, the prediction model used and the accuracy of the prediction. This allows for the prediction of the economic impact on the entire region, making it possible to make specific proposals for regional revitalization. The generation AI can also propose specific store opening strategies to businesses based on the predicted economic impact. For example, it can provide specific advice such as, "Opening a cafe in this region has a high job creation effect, so it might be a good idea to open a cafe." It can also provide specific advice such as, "Opening a fitness gym in this region has a high job creation effect, so it might be a good idea to open a fitness gym."

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The demand registration unit registers the consumer's demand. For example, a consumer might enter a specific request such as "I want a cafe" or "I want a fitness gym." This information is analyzed by the generation AI and stored as consumer demand data. Step 2: The store opening planning support unit supports the business operator's store opening plans based on the demand registered by the demand registration unit. For example, the generation AI analyzes consumer demand data and provides the business operator with a profit forecast after opening a store. For example, it makes a specific prediction such as, "There is high demand for cafes in this area, so opening a store is expected to result in high profits." Step 3: The matching unit matches the store opening plans supported by the store opening plan support unit with the demand registered by the demand registration unit. For example, the matching unit matches businesses that want to open cafes with areas where consumers have registered that they "want a cafe." This allows for efficient matching of consumer demand with businesses' store opening plans.

[0058] (Example 2) The matching platform according to an embodiment of the present invention is a system that matches the supply and demand of businesses wishing to open stores in vacant spaces with consumers who want specific services or stores in the vicinity. This allows the matching platform to optimize the investments of businesses and improve the quality of life of consumers.

[0059] A matching platform according to an embodiment includes a demand registration unit, a store opening plan support unit, and a matching unit. The demand registration unit registers consumer demand. For example, a consumer inputs a specific request such as "I want a cafe" or "I want a fitness gym." This information is analyzed by a generation AI and stored as consumer demand data. The store opening plan support unit supports a business operator's store opening plan based on the demand registered by the demand registration unit. For example, the generation AI analyzes consumer demand data and provides the business operator with a profit forecast after opening a store. For example, it makes a specific prediction such as "There is high demand for cafes in this area, so opening a store is expected to result in high profits." The matching unit matches the store opening plan supported by the store opening plan support unit with the demand registered by the demand registration unit. For example, the matching unit matches businesses looking to open a cafe in an area where a consumer registered "I want a cafe." This allows for efficient matching of consumer demand with businesses' store opening plans.

[0060] The demand registration unit allows the generation AI to refer to past consumer behavior data and predict potential demand in response to requests entered by consumers. For example, if a consumer enters "I want a cafe," the demand registration unit analyzes past consumer behavior data and predicts potential demand based on the frequency of cafe use and popularity in the area. For example, in areas where there have been many similar requests in the past, it is predicted that there will be high demand for cafes. The generation AI analyzes consumer behavior data based on, for example, the algorithm used and the type of learning data, and predicts potential demand. This allows for more accurate matching by predicting potential consumer demand.

[0061] When a consumer's request is input into the demand registration unit, the generation AI can suggest related services and stores in real time, expanding the options available. For example, if a consumer inputs "I want a cafe," the demand registration unit will suggest in real time, "In addition to a cafe, how about a bakery or delicatessen?" This allows the consumer to consider other services related to their request. The generation AI will suggest related services and stores based on, for example, the proposal algorithm and the timing of the proposal. This will expand the consumer's options, making it possible to meet a wider variety of needs.

[0062] The demand registration unit can use the emotion estimation function to analyze the emotions felt by consumers when they input their requests, and provide an interface for eliciting positive emotions. For example, when a consumer inputs a request, the demand registration unit uses the emotion estimation function to analyze facial expressions and voice tone, and provides an interface for eliciting positive emotions. For example, if a smile is detected, the function may display "That's a great idea!". The emotion estimation function analyzes emotions based on, for example, the algorithm and emotion classification criteria used. This can elicit positive emotions from consumers, providing a better user experience.

[0063] The demand registration unit can also accept consumer requests via voice or image input, and the generation AI can analyze them to generate demand data. For example, when a consumer says "I want a cafe" via voice input, the generation AI analyzes the voice and converts it into text data to generate demand data. This makes it easy to register requests via voice input. Also, when a consumer uploads a "photo of the cafe" via image input, the generation AI analyzes the image and generates related demand data. For example, image recognition technology can be used to extract the cafe's features and save them as demand data. This improves convenience by allowing consumer requests to be accepted via voice or image input.

[0064] The demand registration unit can add a function that, when a consumer inputs a request, compares it with the requests of other consumers to find common needs. For example, when a consumer inputs "I want a cafe," the demand registration unit allows the generation AI to analyze the request data of other consumers and display the number of other consumers in the same area who have input "I want a cafe." This allows common needs to be confirmed. For example, the generation AI compares the request with other consumers' requests based on needs classification criteria and comparison algorithms to find common needs. This makes it possible to meet the requests of more consumers by finding common needs.

[0065] The demand registration unit uses the emotion estimation function to provide real-time feedback on the emotions felt by consumers when they input their requests, and can make suggestions to improve the input content. For example, when a consumer inputs a request, the demand registration unit uses the emotion estimation function to analyze emotions in real time and make suggestions to elicit positive emotions. For example, if the input content is negative, it may display "Please be more specific!" The emotion estimation function, for example, analyzes emotions based on the algorithm and emotion evaluation criteria used and provides feedback. This allows for real-time feedback of consumer emotions, enabling better request input.

[0066] In the store opening plan support section, the generation AI analyzes the business operator's past store opening data, learning successful and unsuccessful patterns to optimize the store opening plan. In the store opening plan support section, for example, the generation AI analyzes the business operator's past store opening data and extracts commonalities between successful store openings. For example, it learns successful patterns based on location conditions and target customer demographics. The generation AI also analyzes data on unsuccessful store openings and learns failure patterns. For example, it identifies failure patterns based on the presence of competing stores and low demand. This makes it possible to increase the success rate by optimizing store opening plans based on past data.

[0067] The store opening planning support unit can analyze the competitive situation in real time in areas where a business is considering opening a store and evaluate its competitive advantage. For example, the generation AI in the store opening planning support unit collects data on competing stores in real time in areas where a business is considering opening a store and analyzes the competitive situation. For example, it evaluates competitive advantage based on the number of stores in the same industry and sales data. The generation AI uses an algorithm that evaluates competitive advantage based on, for example, the number of competitors and the strength of the competition. This makes it easier to evaluate competitive advantage by analyzing the competitive situation in real time.

[0068] The store opening plan support unit can use the emotion estimation function to predict consumers' emotional reactions to a business operator's store opening plan and propose strategies to elicit positive reactions. For example, the store opening plan support unit uses generative AI to predict consumers' emotional reactions to a business operator's store opening plan and propose strategies to elicit positive reactions. For example, a consumer survey is conducted before the store opening to collect emotional data. The emotion estimation function analyzes consumer emotions using, for example, facial expression recognition or voice analysis and proposes strategies to elicit positive reactions. This makes it possible to propose strategies to elicit positive reactions by predicting consumers' emotional reactions.

[0069] In the store opening plan support section, the generation AI simulates different scenarios for a business operator's store opening plan and can propose an optimal store opening strategy. In the store opening plan support section, for example, the generation AI simulates different location conditions for a business operator's store opening plan and proposes an optimal store opening strategy. For example, it compares multiple location conditions, such as near major transportation hubs or next to commercial facilities. The generation AI simulates different scenarios based on, for example, the scenario setting method and the accuracy of the simulation. In this way, by simulating different scenarios, it is possible to propose an optimal store opening strategy.

[0070] The store opening planning support unit can integrate socioeconomic data for areas where businesses are considering opening stores, and comprehensively evaluate the risks and opportunities for store opening. In the store opening planning support unit, for example, the generation AI analyzes demographic data for areas where businesses are considering opening stores, and evaluates the risks and opportunities for store opening. For example, the evaluation is based on population growth rates and age distribution. The generation AI integrates data based on socioeconomic data collection and analysis methods, and comprehensively evaluates risks and opportunities. In this way, by integrating socioeconomic data, it is possible to comprehensively evaluate the risks and opportunities for store opening.

[0071] The store opening plan support unit can use the emotion estimation function to survey local residents' emotions regarding a business operator's store opening plan in advance and adjust the store opening plan. For example, the store opening plan support unit uses a generative AI to survey local residents' emotions regarding a business operator's store opening plan in advance and propose a strategy to elicit a positive response. For example, it collects emotional data from local residents and adjusts the store opening plan. The emotion estimation function, for example, uses facial expression recognition and voice analysis to analyze the emotions of local residents and adjust the store opening plan. This makes it possible to optimize the store opening plan by surveying the emotions of local residents in advance.

[0072] The matching unit allows the generation AI to analyze consumer demand data and business store opening plans, and improve the algorithm for optimal matching. For example, the matching unit allows the generation AI to analyze consumer demand data and business store opening plans, and improve the algorithm for optimal matching. For example, it develops a matching algorithm that takes into account the balance between supply and demand. The generation AI improves the algorithm based on, for example, the type of algorithm used and the improvement method. By improving the algorithm, more accurate matching becomes possible.

[0073] The matching unit visualizes the matching results and can provide an interface that can be intuitively understood by businesses and consumers. For example, the generation AI visualizes the matching results and provides an interface that can be intuitively understood by businesses and consumers. For example, the generation AI displays the matching results on a map and visually shows potential store locations. For example, the generation AI visualizes the matching results based on visualization tools and visualization content. In this way, by visualizing the matching results, businesses and consumers can intuitively understand them.

[0074] The matching unit can use the emotion estimation function to analyze the emotional reactions of consumers and businesses to the matching results, thereby improving matching accuracy. For example, the generation AI in the matching unit uses the emotion estimation function to analyze the emotional reactions of consumers and businesses to the matching results, thereby improving matching accuracy. For example, it prioritizes the adoption of matching results with a high number of positive emotional reactions. The emotion estimation function, for example, analyzes emotional reactions based on the analysis tools and methods used. In this way, matching accuracy can be improved by analyzing emotional reactions.

[0075] In the matching unit, the generation AI simultaneously matches different industries and services, making it possible to meet the needs of multiple businesses and consumers at once. In the matching unit, for example, the generation AI simultaneously matches different industries and services, making it possible to meet the needs of multiple businesses and consumers at once. For example, it performs matching to simultaneously meet the demand for cafes and fitness gyms. For example, the generation AI matches different industries and services based on simultaneous processing methods and simultaneous processing targets. This makes it possible to simultaneously match different industries and services, making it possible to meet multiple needs at once.

[0076] The matching unit can predict the economic effects on the entire region based on the matching results and make proposals for regional revitalization. For example, the generation AI in the matching unit predicts the economic effects on the entire region based on the matching results and makes proposals for regional revitalization. For example, it predicts the job creation effect of opening a new store. For example, the generation AI predicts the economic effects based on the prediction model used and the accuracy of the prediction. This makes it possible to predict the economic effects on the entire region and make specific proposals for regional revitalization.

[0077] The matching unit uses an emotion estimation function to monitor the emotions of consumers and businesses regarding the matching results in real time, and is able to continuously perform optimal matching. For example, the generation AI uses the emotion estimation function to monitor the emotions of consumers and businesses regarding the matching results in real time, and is able to continuously perform optimal matching. For example, it prioritizes the adoption of matching results with a high number of positive emotional responses. The emotion estimation function, for example, monitors emotions in real time based on monitoring tools and monitoring methods. This allows for continuous optimal matching by monitoring emotions in real time.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] When a consumer inputs a request, the demand registration unit can make personalized suggestions based on past consumer behavior data. For example, if a consumer inputs "I want a cafe," the generation AI analyzes the consumer's past behavioral data and makes specific suggestions such as, "It might be good if a cafe like the one you previously visited opened in this area." This enables suggestions tailored to the consumer's individual needs, resulting in more satisfying service. Also, if a consumer inputs "I want a fitness gym," the generation AI can suggest, "It might be good if a facility like the gym you previously visited opened in this area," based on the consumer's past fitness-related behavioral data. Furthermore, if a consumer inputs "I want a restaurant," the generation AI can suggest, "It might be good if a restaurant like the one you previously visited opened in this area," based on the consumer's past dining history.

[0080] When a consumer inputs a request, the demand registration unit can make suggestions based on the local season and events. For example, if a consumer inputs "I want a cafe," the generation AI analyzes the local season and event information and suggests, "Since many cherry blossom viewing events are held in the spring in this area, it might be a good idea to open a cafe to coincide with the cherry blossom viewing season." This enables suggestions based on the characteristics of the area, supporting more effective store opening plans. Similarly, if a consumer inputs "I want a fitness gym," the generation AI can use information about local sports events to suggest, "Since marathons are held in the summer in this area, it might be a good idea to open a fitness gym to coincide with the marathon season." Furthermore, if a consumer inputs "I want a restaurant," the generation AI can use information about local food culture events to suggest, "Since harvest festivals are held in the fall in this area, it might be a good idea to open a restaurant to coincide with the harvest festival season."

[0081] When a consumer inputs a request, the demand registration unit can make suggestions based on other consumers' reviews and ratings. For example, if a consumer inputs "I want a cafe," the generation AI analyzes other consumers' reviews and ratings and suggests, "Many consumers in this area have given cafes high ratings, so opening a cafe might be a good idea." This makes it possible to take other consumers' opinions into consideration when making suggestions, providing more reliable information. Also, if a consumer inputs "I want a fitness gym," the generation AI can suggest, based on other consumers' reviews and ratings of fitness gyms, "Many consumers in this area have given fitness gyms high ratings, so opening a fitness gym might be a good idea." Furthermore, if a consumer inputs "I want a restaurant," the generation AI can suggest, based on other consumers' reviews and ratings of restaurants, "Many consumers in this area have given restaurants high ratings, so opening a restaurant might be a good idea."

[0082] The demand registration unit can use the emotion estimation function to analyze the emotions felt by consumers when they input their requests and provide an interface for eliciting positive emotions. For example, when a consumer inputs a request, the emotion estimation function can analyze their facial expressions and voice tone to provide an interface for eliciting positive emotions. For example, if a smile is detected, the function can display "What a great idea!". The emotion estimation function analyzes emotions based on the algorithm and emotion classification criteria used, for example. This can elicit positive emotions from consumers and provide a better user experience. Furthermore, if a consumer inputs "I want a cafe," and the emotion estimation function detects a positive emotion, the function can display "Everyone will be happy if a cafe opens in this area!". Furthermore, if a consumer inputs "I want a fitness gym," and the emotion estimation function detects a positive emotion, the function can display "What a great idea to support a healthy lifestyle!".

[0083] The demand registration unit can also accept consumer requests via voice or image input, and the generation AI can analyze them to generate demand data. For example, if a consumer says "I want a cafe" via voice input, the generation AI analyzes the voice and converts it into text data to generate demand data. This makes it easy to register requests via voice input. Also, if a consumer uploads a photo of a cafe via image input, the generation AI analyzes the image and generates related demand data. For example, image recognition technology can be used to extract the cafe's features and save them as demand data. This improves convenience by allowing consumer requests to be accepted via voice or image input. Furthermore, if a consumer says "I want a fitness gym" via voice input, the generation AI can analyze the voice and convert it into text data to generate demand data. Similarly, if a consumer says "I want a restaurant" and uploads a photo of the restaurant via image input, the generation AI can analyze the image and generate related demand data.

[0084] The demand registration unit can add a function that, when a consumer inputs a request, compares it with the requests of other consumers to find common needs. For example, if a consumer inputs "I want a cafe," the generation AI analyzes the request data of other consumers and displays the number of other consumers in the same area who have input "I want a cafe." This allows common needs to be confirmed. The generation AI compares the request with other consumers' requests based on, for example, need classification criteria or comparison algorithms to find common needs. This allows the demands of more consumers to be met by finding common needs. Furthermore, if a consumer inputs "I want a fitness gym," the generation AI can analyze the request data of other consumers and display the number of other consumers in the same area who have input "I want a fitness gym." Furthermore, if a consumer inputs "I want a restaurant," the generation AI can analyze the request data of other consumers and display the number of other consumers in the same area who have input "I want a restaurant."

[0085] The demand registration unit can use the emotion estimation function to provide real-time feedback on the emotions felt by consumers when they enter their requests and make suggestions to improve their input. For example, when a consumer enters a request, the emotion estimation function can analyze their emotions in real time and make suggestions to elicit positive emotions. For example, if the input is negative, the function can display a message saying, "Please be more specific!" The emotion estimation function, for example, analyzes emotions based on the algorithm and emotion evaluation criteria used and provides feedback. This allows for better request input by providing real-time feedback on the consumer's emotions. Furthermore, if a consumer enters "I want a cafe," and the emotion estimation function detects a negative emotion, the function can display a message saying, "Please be more specific about the location and features!" Furthermore, if a consumer enters "I want a fitness gym," and the emotion estimation function detects a negative emotion, the function can display a message saying, "Please be more specific about the facilities you want!"

[0086] The store opening plan support unit uses the generation AI to analyze a business's past store opening data, learning patterns of success and failure to optimize the store opening plan. For example, the generation AI analyzes a business's past store opening data and extracts commonalities among successful store openings. For example, it learns patterns of success based on factors such as location and target customer demographics. The generation AI also analyzes data from unsuccessful store openings to learn patterns of failure. For example, it identifies patterns of failure based on factors such as the presence of competing stores and low demand. This increases the success rate by optimizing store opening plans based on past data. Furthermore, the generation AI can propose specific store opening strategies to businesses based on patterns of success and failure. For example, it can make specific predictions such as, "There is high demand for cafes in this area, so opening a store will likely result in high profits." It can also provide specific advice such as, "There are many competing stores in this area, so it would be better to avoid opening a store here."

[0087] The store opening planning support department can analyze the competitive situation in real time in areas where businesses are considering opening stores and evaluate their competitive advantage. For example, the generation AI collects data on competing stores in real time in areas where businesses are considering opening stores and analyzes the competitive situation. For example, it evaluates competitive advantage based on the number of stores in the same industry and sales data. The generation AI uses an algorithm to evaluate competitive advantage based on, for example, the number of competitors and the strength of competition. This makes it easier to evaluate competitive advantage by analyzing the competitive situation in real time. The generation AI can also propose specific store opening strategies to businesses based on data on competing stores. For example, it can provide specific advice such as, "There are few competing stores in this area, so there is a high chance of opening a store." It can also provide specific advice such as, "There are many competing stores in this area, so it would be better to avoid opening a store."

[0088] The store opening plan support department can use the emotion estimation function to predict consumer emotional reactions to a business operator's store opening plan and propose strategies to elicit positive reactions. For example, the generative AI can predict consumer emotional reactions to a business operator's store opening plan and propose strategies to elicit positive reactions. For example, a consumer survey is conducted before the store opening to collect emotional data. The emotion estimation function analyzes consumer emotions using, for example, facial recognition or voice analysis, and proposes strategies to elicit positive reactions. This makes it possible to propose strategies to elicit positive reactions by predicting consumer emotional reactions. It can also propose specific store opening strategies to businesses based on the results of the consumer survey. For example, it can provide specific advice such as, "There are many positive reactions to opening a cafe in this area, so there is a high chance of opening a store." It can also provide specific advice such as, "There are many positive reactions to opening a fitness gym in this area, so there is a high chance of opening a store."

[0089] The store opening plan support department can use the emotion estimation function to predict consumer emotional reactions to a business operator's store opening plan and propose strategies to elicit positive reactions. For example, the generative AI can predict consumer emotional reactions to a business operator's store opening plan and propose strategies to elicit positive reactions. For example, a consumer survey is conducted before the store opening to collect emotional data. The emotion estimation function analyzes consumer emotions using, for example, facial recognition or voice analysis, and proposes strategies to elicit positive reactions. This makes it possible to propose strategies to elicit positive reactions by predicting consumer emotional reactions. It can also propose specific store opening strategies to businesses based on the results of the consumer survey. For example, it can provide specific advice such as, "There are many positive reactions to opening a cafe in this area, so there is a high chance of opening a store." It can also provide specific advice such as, "There are many positive reactions to opening a fitness gym in this area, so there is a high chance of opening a store."

[0090] In the store opening plan support department, the generation AI can simulate different scenarios for a business operator's store opening plan and propose an optimal store opening strategy. For example, the generation AI can simulate different location conditions for a business operator's store opening plan and propose an optimal store opening strategy. For example, it compares multiple location conditions, such as near major transportation hubs or next to commercial facilities. The generation AI simulates different scenarios based on, for example, the scenario setting method and the accuracy of the simulation. By simulating different scenarios, it can propose an optimal store opening strategy. The generation AI can also propose specific store opening strategies to businesses based on the simulation results. For example, it can provide specific advice such as, "In this area, opening a store near major transportation hubs will increase customer attraction." It can also provide specific advice such as, "In this area, opening a store next to a commercial facility will increase competitive advantage."

[0091] The store opening planning support department can integrate socioeconomic data for areas where businesses are considering opening a store and comprehensively evaluate the risks and opportunities for store opening. For example, the generation AI analyzes demographic data for areas where businesses are considering opening a store and evaluates the risks and opportunities for store opening. For example, it evaluates based on population growth rates and age distribution. The generation AI integrates data based on socioeconomic data collection and analysis methods, and comprehensively evaluates risks and opportunities. By integrating socioeconomic data, it can comprehensively evaluate the risks and opportunities for store opening. The generation AI can also propose specific store opening strategies to businesses based on socioeconomic data. For example, it can provide specific advice such as, "Since the youth population in this area is increasing, it may be a good idea to open a store targeting young people." It can also provide specific advice such as, "Since the elderly population in this area is large, it may be a good idea to open a store that provides services for seniors."

[0092] The store opening plan support unit can use the emotion estimation function to survey local residents' emotions regarding a business operator's store opening plan in advance and adjust the store opening plan. For example, the generation AI can survey local residents' emotions regarding the business operator's store opening plan in advance and propose a strategy to elicit a positive response. For example, it can collect emotional data from local residents and adjust the store opening plan. The emotion estimation function can analyze local residents' emotions using facial recognition and voice analysis, for example, and adjust the store opening plan. This allows the store opening plan to be optimized by surveying local residents' emotions in advance. It can also propose specific store opening strategies to businesses based on the emotional data of local residents. For example, it can provide specific advice such as, "There is a high chance of opening a store in this area because there is a lot of positive response to opening a cafe in this area." It can also provide specific advice such as, "There is a high chance of opening a store in this area because there is a lot of positive response to opening a fitness gym in this area."

[0093] In the matching department, the generation AI analyzes consumer demand data and business store opening plans and can improve the algorithm for optimal matching. For example, the generation AI analyzes consumer demand data and business store opening plans and improves the algorithm for optimal matching. For example, it develops a matching algorithm that takes into account the balance between supply and demand. The generation AI improves the algorithm based on, for example, the type of algorithm used and the improvement method. By improving the algorithm, more accurate matching becomes possible. The generation AI can also propose specific matching strategies to businesses based on the improved algorithm. For example, it can provide specific advice such as, "Since there is a high demand for cafes in this area, it might be a good idea to open a cafe." It can also provide specific advice such as, "Since there is a high demand for fitness gyms in this area, it might be a good idea to open a fitness gym."

[0094] The matching unit can visualize the matching results and provide an interface that businesses and consumers can intuitively understand. For example, the generation AI can visualize the matching results and provide an interface that businesses and consumers can intuitively understand. For example, the generation AI can display the matching results on a map and visually show potential store locations. The generation AI can visualize the matching results based on visualization tools and visualization content, for example. This allows businesses and consumers to intuitively understand the matching results. The generation AI can also propose specific store opening strategies to businesses based on the visualized matching results. For example, it can provide specific advice such as, "There is a high demand for cafes in this area, so it might be a good idea to open a cafe." It can also provide specific advice such as, "There is a high demand for fitness gyms in this area, so it might be a good idea to open a fitness gym."

[0095] The matching unit can use the emotion estimation function to analyze the emotional responses of consumers and businesses to the matching results, thereby improving matching accuracy. For example, the generation AI can use the emotion estimation function to analyze the emotional responses of consumers and businesses to the matching results, thereby improving matching accuracy. For example, it can prioritize matching results with a high number of positive emotional responses. The emotion estimation function can analyze emotional responses based on, for example, the analysis tools and methods used. This analysis of emotional responses can improve matching accuracy. In addition, based on the emotion estimation function, it can also propose specific matching strategies to businesses. For example, it can provide specific advice such as, "There are many positive responses to opening a cafe in this area, so it might be a good idea to open a cafe." It can also provide specific advice such as, "There are many positive responses to opening a fitness gym in this area, so it might be a good idea to open a fitness gym."

[0096] In the matching section, the generation AI simultaneously matches different industries and services, enabling it to meet the needs of multiple businesses and consumers at once. For example, the generation AI simultaneously matches different industries and services, satisfying the needs of multiple businesses and consumers at once. For example, it matches cafes and fitness gyms to simultaneously meet the demand. The generation AI, for example, matches different industries and services based on simultaneous processing methods and simultaneous processing targets. This allows it to simultaneously match different industries and services, thereby meeting multiple needs at once. The generation AI can also propose specific store opening strategies to businesses based on the results of simultaneous matching. For example, it can provide specific advice such as, "Since there is high demand for cafes and fitness gyms in this area, it may be a good idea to open both." It can also provide specific advice such as, "Since there is high demand for restaurants and fitness gyms in this area, it may be a good idea to open both."

[0097] The matching unit can predict the economic impact on the entire region based on the matching results and make proposals for regional revitalization. For example, the generation AI predicts the economic impact on the entire region based on the matching results and makes proposals for regional revitalization. For example, it predicts the job creation effect of opening a new store. The generation AI predicts the economic impact based on, for example, the prediction model used and the accuracy of the prediction. This allows for the prediction of the economic impact on the entire region, making it possible to make specific proposals for regional revitalization. The generation AI can also propose specific store opening strategies to businesses based on the predicted economic impact. For example, it can provide specific advice such as, "Opening a cafe in this region has a high job creation effect, so it might be a good idea to open a cafe." It can also provide specific advice such as, "Opening a fitness gym in this region has a high job creation effect, so it might be a good idea to open a fitness gym."

[0098] The matching unit uses the emotion estimation function to monitor the emotions of consumers and businesses regarding the matching results in real time, allowing for continuous optimal matching. For example, the generation AI uses the emotion estimation function to monitor the emotions of consumers and businesses regarding the matching results in real time, allowing for continuous optimal matching. For example, it prioritizes matching results with a high number of positive emotional responses. The emotion estimation function monitors emotions in real time based on monitoring tools and methods, for example. This allows for continuous optimal matching by monitoring emotions in real time. In addition, it can propose specific matching strategies to businesses based on the emotion estimation function. For example, it can provide specific advice such as, "There are many positive responses to opening a cafe in this area, so it might be a good idea to open a cafe." It can also provide specific advice such as, "There are many positive responses to opening a fitness gym in this area, so it might be a good idea to open a fitness gym."

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The demand registration unit registers the consumer's demand. For example, a consumer might enter a specific request such as "I want a cafe" or "I want a fitness gym." This information is analyzed by the generation AI and stored as consumer demand data. Step 2: The store opening planning support unit supports the business operator's store opening plans based on the demand registered by the demand registration unit. For example, the generation AI analyzes consumer demand data and provides the business operator with a profit forecast after opening a store. For example, it makes a specific prediction such as, "There is high demand for cafes in this area, so opening a store is expected to result in high profits." Step 3: The matching unit matches the store opening plans supported by the store opening plan support unit with the demand registered by the demand registration unit. For example, the matching unit matches businesses that want to open cafes with areas where consumers have registered that they "want a cafe." This allows for efficient matching of consumer demand with businesses' store opening plans.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a demand registration unit for registering consumer demands; a store opening plan support unit that supports a store opening plan of a business operator based on the demand registered by the demand registration unit; a matching unit that matches the store opening plan supported by the store opening plan support unit with the demand registered by the demand registration unit. A system characterized by:

2. The demand registration unit In response to the request entered by the consumer, the generation AI refers to past consumer behavior data and predicts potential demand.

2. The system of claim 1.

3. The demand registration unit The consumer's requests can be input through voice or image input, and the generation AI analyzes them to generate demand data.

2. The system of claim 1.

4. The store opening plan support department The generative AI analyzes the business's past store opening data, learns patterns of success and failure, and optimizes store opening plans.

2. The system of claim 1.

5. The store opening plan support department The generation AI simulates different scenarios for the store opening plan of the business operator and proposes the optimal store opening strategy.

2. The system of claim 1.

6. The matching unit The generative AI analyzes the consumer demand data and the store opening plans of the business operator, and improves the algorithm to achieve optimal matching.

2. The system of claim 1.

7. The matching unit Generative AI simultaneously matches different industries and services, satisfying the needs of multiple businesses and consumers at once.

2. The system of claim 1.

8. The demand registration unit Analyze the emotions felt when the consumer inputs their request, and provide an interface to elicit positive emotions.

2. The system of claim 1.

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