System

Abstract

An object of the system according to the embodiment is to simulate the success of a new business or a new service in the market in advance.SOLUTION: A system includes a reception unit, a simulation unit, and an evaluation unit. The reception unit inputs information on a new business or a new service. The simulation unit starts simulation in the virtual world on the basis of the information input by the reception unit. The evaluation unit evaluates a result simulated by the simulation unit.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 to simulate the market success of new businesses or services in advance, and there was a problem of high risk.

[0005] The system according to the embodiment aims to simulate in advance the success of a new business or new service in the market. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a simulation unit, and an evaluation unit. The reception unit inputs information about a new business or new service. The simulation unit starts a simulation in a virtual world based on the information input by the reception unit. The evaluation unit evaluates the results of the simulation by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can simulate in advance the success of a new business or new service in the market. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A simulation system according to an embodiment of the present invention simulates whether a corporation can actually generate sales and profits when developing a new business or service aimed at general consumers. This simulation system creates a virtual living space by having a generation AI, which has been input with the concept of "purchase," live in a virtual world. It is possible to simulate the sales potential of a new service when it is launched in this virtual world before it is actually released to the market. The generation AI learns big data, such as purchase data from electronic payment systems and search history from search engines, to reproduce the lives of real people as closely as possible. For example, a corporation inputs information about a new business or service, such as information about a new beverage or application. This information is then input into the generation AI. The generation AI then begins a simulation in the virtual world based on the input information. The generation AI acts as a virtual consumer living in the virtual world and tries out new services. For example, it purchases and drinks a new beverage. The generation AI then reproduces the behavior of real consumers based on big data, such as purchase data from electronic payment systems and search history from search engines. This makes consumer behavior in the virtual world more realistic. Based on the results of the simulation, the generative AI calculates the extent to which a new service will be used and the expected sales. For example, it simulates how many virtual consumers will purchase a new beverage and how much profit will be generated. This allows a company to simulate the sales and profits of a new business or service before actually launching it on the market. This reduces risk and enables the development of effective marketing strategies. For example, a new application can be simulated in the virtual world to determine in advance how many downloads it can expect and how much revenue it can generate. A new beverage can also be simulated in the virtual world to determine in advance how much sales it can expect and how much profit it can generate. This allows a company to increase the chances of success for new businesses and services.

[0029] A simulation system according to an embodiment includes a reception unit, a simulation unit, and an evaluation unit. The reception unit inputs information about a new business or service. Examples of new businesses or services include, but are not limited to, technical services, food and beverage services, and entertainment services. For example, a corporation can input information about a new beverage or application to the reception unit. The simulation unit uses a generation AI to start a simulation in a virtual world based on the information input by the reception unit. The generation AI acts as a virtual consumer living in the virtual world using technologies such as natural language generation, image generation, and behavioral simulation. The simulation unit acts as a virtual consumer living in the virtual world using the generation AI and reproduces the behavior of real consumers based on big data such as purchase data from an electronic payment system and search history from a search engine. For example, the generation AI learns big data such as purchase data from an electronic payment system and search history from a search engine, and simulates consumer behavior in the virtual world. The evaluation unit evaluates the results of the simulation by the simulation unit. For example, the evaluation unit calculates sales and profits of the new service based on the simulation results. The evaluation unit can also provide information for developing a marketing strategy based on the simulation results. For example, the evaluation unit may predict sales and profits based on the simulation results and formulate a marketing strategy. This allows the simulation system according to the embodiment to simulate sales and profits of new businesses and new services.

[0030] The simulation unit can act as a virtual consumer living in a virtual world using the generation AI. The generation AI can, for example, simulate conversations as a virtual consumer using natural language generation technology. The generation AI can also simulate the visual behavior of a virtual consumer using image generation technology. Furthermore, the generation AI can simulate the purchasing behavior of a virtual consumer using behavior simulation technology. For example, the generation AI can simulate actions such as purchasing and trying a new beverage in the virtual world. This allows for a simulation that is close to reality by reproducing the behavior of a virtual consumer. Some or all of the above-mentioned processing in the simulation unit is performed using the generation AI. For example, the simulation unit inputs the behavior of a virtual consumer into the generation AI and simulates that behavior.

[0031] The simulation unit can reproduce the behavior of real consumers based on big data such as purchase data from an electronic payment system or search history from a search engine. Examples of big data include, but are not limited to, electronic payment data, search history data, and social media data. For example, the simulation unit simulates the purchasing behavior of a virtual consumer based on the purchase data from an electronic payment system. The simulation unit can also simulate the interests of the virtual consumer based on search history from a search engine. Furthermore, the simulation unit can simulate the social network of the virtual consumer based on social media data. For example, the simulation unit simulates what products the virtual consumer will purchase based on the electronic payment data. This reproduces real consumer behavior, improving the accuracy of the simulation. Some or all of the above-described processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs big data into the generation AI and simulates consumer behavior based on that data.

[0032] The evaluation unit can calculate the sales and profits of the new service based on the simulation results. Sales and profits include, but are not limited to, revenue models, cost calculations, and profit margins, for example. The evaluation unit, for example, forecasts sales based on the simulation results. The evaluation unit can also forecast profits based on the simulation results. Furthermore, the evaluation unit can evaluate customer satisfaction based on the simulation results. For example, the evaluation unit forecasts sales of the new service based on the simulation results and calculates how much profit will be obtained. In this way, by calculating sales and profits based on the simulation results, it is possible to predict market reaction in advance. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the simulation results into AI and predicts sales and profits.

[0033] The simulation unit can act as a virtual consumer based on information about a new beverage or application. Examples of new beverages or applications include, but are not limited to, energy drinks, health drinks, mobile apps, and web apps. The simulation unit can act as a virtual consumer based on information about a new beverage. The simulation unit can also act as a virtual consumer based on information about a new application. The simulation unit can also act as a virtual consumer based on information about a new service. For example, the simulation unit simulates behavior such as purchasing and drinking a new beverage. This enables specific simulations by acting as a virtual consumer based on information about the new beverage or application. Some or all of the above-described processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs information about the new beverage or application into the generation AI and simulates behavior as a virtual consumer based on that information.

[0034] The evaluation unit can provide information for formulating a marketing strategy based on the simulation results. Marketing strategies include, but are not limited to, selection of a target market, promotion methods, and pricing. For example, the evaluation unit selects a target market based on the simulation results. The evaluation unit can also plan a promotion method based on the simulation results. Furthermore, the evaluation unit can set prices based on the simulation results. For example, the evaluation unit selects a target market based on the simulation results and proposes an optimal promotion method. This allows for the formulation of an effective marketing strategy by providing information for formulating a marketing strategy based on the simulation results. Some or all of the above-described processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the simulation results into AI and formulates a marketing strategy.

[0035] The reception unit can analyze past input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of information that the user has input in the past and suggest the optimal input method. Furthermore, the reception unit can predict and suggest the input method to be used in a specific time period based on the user's past input history. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs past input history data into AI and selects the optimal input method.

[0036] When inputting information about a new business or new service, the reception unit can filter the information based on the user's current business situation or areas of interest. For example, the reception unit can prioritize and display highly relevant information based on the user's current business situation. The reception unit can also filter and display related information based on the user's areas of interest. Furthermore, the reception unit can combine the user's business situation and areas of interest to provide optimal information. For example, if the user is in the food and beverage industry, the reception unit can prioritize and display information about new businesses and new services related to the food and beverage industry. This allows highly relevant information to be provided by filtering information based on the user's business situation and areas of interest. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs data about the user's business situation and areas of interest into AI and performs filtering.

[0037] When inputting information for a new business or new service, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface optimized for text input. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. For example, if the user selects voice input, the reception unit inputs information using voice recognition software. This enables efficient information input by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input method data into AI and selects the optimal input means.

[0038] When inputting information about new businesses or new services, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. The reception unit, for example, prioritizes displaying relevant information based on the user's current location. The reception unit can also provide region-specific information based on the user's geographical location information. Furthermore, the reception unit can filter optimal information by taking into account the user's location information. For example, if the user is in a specific region, the reception unit prioritizes displaying information about new businesses or new services related to that region. This makes it possible to provide region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's geographical location information data into AI and selects highly relevant information.

[0039] When inputting information about a new business or new service, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit analyzes the user's social media posts and inputs related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. Furthermore, the reception unit can input related information based on the user's social media check-in information. For example, the reception unit analyzes the user's social media posts and inputs information about related new businesses or new services. This makes it possible to provide highly relevant information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's social media data into AI and selects related information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting information for a new business or new service. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. Furthermore, the reception unit can optimize the input procedure based on the user's feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's past feedback data into AI to customize the input method.

[0041] During the simulation, the simulation unit can adjust the level of detail of the simulation based on the importance of the virtual consumer. For example, the simulation unit performs a detailed simulation for an important virtual consumer. The simulation unit can also perform a simplified simulation for a less important virtual consumer. Furthermore, the simulation unit can gradually adjust the level of detail of the simulation according to the importance of the virtual consumer. For example, the simulation unit adjusts the level of detail of the simulation based on the purchasing power or influence of the virtual consumer. This allows for an efficient simulation by adjusting the level of detail of the simulation according to the importance of the virtual consumer. Some or all of the above-described processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs importance data of the virtual consumer into the generation AI and adjusts the level of detail of the simulation.

[0042] During the simulation, the simulation unit can apply different simulation algorithms depending on the category of the virtual consumer. For example, the simulation unit can apply a simulation algorithm for young people to a young virtual consumer. The simulation unit can also apply a simulation algorithm for elderly people to an elderly virtual consumer. Furthermore, the simulation unit can apply a related simulation algorithm to a virtual consumer with specific interests. For example, the simulation unit applies a simulation algorithm based on the age and gender of the virtual consumer. In this way, applying a simulation algorithm depending on the category of the virtual consumer enables a simulation that is closer to reality. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs category data of the virtual consumer into the generation AI and applies the simulation algorithm.

[0043] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the simulation unit analyzes the user's past simulation results to improve accuracy. The simulation unit can also optimize the simulation algorithm based on data obtained from the past simulation results. Furthermore, the simulation unit can adjust simulation parameters by referring to the user's past simulation results. For example, the simulation unit builds a feedback loop to improve the accuracy of the simulation based on the past simulation results. This improves the accuracy of the simulation by referring to the user's past simulation results. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs past simulation result data into the generation AI to improve the accuracy of the simulation.

[0044] During the simulation, the simulation unit can determine the priority of the simulation based on the behavioral history of the virtual consumer. For example, the simulation unit determines the priority of the simulation based on the virtual consumer's past purchase history. The simulation unit can also determine the priority of the simulation based on the virtual consumer's past search history. Furthermore, the simulation unit can analyze the virtual consumer's past behavioral patterns and determine the priority of the simulation. For example, the simulation unit determines the priority of the simulation based on the virtual consumer's purchase history. This enables efficient simulation by determining the priority of the simulation based on the virtual consumer's behavioral history. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs the virtual consumer's behavioral history data into the generation AI and determines the priority of the simulation.

[0045] The simulation unit can adjust the order of simulations based on the relevance of the virtual consumer during the simulation. For example, if the relevance of the virtual consumer is high, the simulation unit adjusts the order of simulations as a priority. Also, if the relevance of the virtual consumer is low, the simulation unit can postpone the order of simulations. Furthermore, the simulation unit can gradually adjust the order of simulations according to the relevance of the virtual consumer. For example, the simulation unit adjusts the order of simulations based on the interests and purchasing history of the virtual consumer. This enables efficient simulations by adjusting the order of simulations based on the relevance of the virtual consumer. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs relevance data of the virtual consumer into the generation AI and adjusts the order of simulations.

[0046] During the simulation, the simulation unit can adjust the use of technical terminology in the simulation according to the user's level of expertise. For example, if the user has technical expertise, the simulation unit can have the generation AI perform a simulation that makes heavy use of technical terminology. Furthermore, if the user does not have technical expertise, the simulation unit can have the generation AI perform a simulation that avoids technical terminology. Furthermore, the simulation unit can gradually adjust the use of technical terminology in the simulation according to the user's level of expertise. For example, the simulation unit adjusts the use of technical terminology in the simulation based on the user's level of expertise. This allows for an easy-to-understand simulation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the simulation unit is performed using the generation AI. For example, the simulation unit inputs the user's level of expertise data into the generation AI and adjusts the use of technical terminology in the simulation.

[0047] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the simulation result during evaluation. For example, the evaluation unit performs a detailed evaluation for important simulation results. The evaluation unit can also perform a simplified evaluation for less important simulation results. Furthermore, the evaluation unit can gradually adjust the level of detail of the evaluation based on the importance of the simulation result. For example, the evaluation unit adjusts the level of detail of the evaluation based on the importance of the simulation result. This enables efficient evaluation by adjusting the level of detail of the evaluation based on the importance of the simulation result. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs importance data of the simulation result to AI and adjusts the level of detail of the evaluation.

[0048] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the simulation results. For example, the evaluation unit can apply a beverage-specific evaluation algorithm to the simulation results of a new beverage. The evaluation unit can also apply an application-specific evaluation algorithm to the simulation results of a new application. Furthermore, the evaluation unit can select and apply an optimal evaluation algorithm depending on the category of the simulation results. For example, the evaluation unit applies an evaluation algorithm based on the category of the simulation results. This enables more appropriate evaluation by applying an evaluation algorithm depending on the category of the simulation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs category data of the simulation results into AI and applies the evaluation algorithm.

[0049] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when performing the evaluation. For example, the evaluation unit analyzes the user's past evaluation results to improve the accuracy of the evaluation. The evaluation unit can also optimize the evaluation algorithm based on data obtained from the past evaluation results. Furthermore, the evaluation unit can adjust the evaluation parameters by referring to the user's past evaluation results. For example, the evaluation unit builds a feedback loop to improve the accuracy of the evaluation based on the past evaluation results. This improves the accuracy of the evaluation by referring to the user's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs past evaluation result data into AI to improve the accuracy of the evaluation.

[0050] During evaluation, the evaluation unit can determine the priority of evaluation based on the submission time of the simulation results. For example, if the simulation results are submitted early, the evaluation unit can prioritize the evaluation. Also, if the simulation results are submitted late, the evaluation unit can postpone the evaluation. Furthermore, the evaluation unit can gradually adjust the priority of evaluation based on the submission time. For example, the evaluation unit determines the priority of evaluation based on the submission time of the simulation results. This enables efficient evaluation by determining the priority of evaluation based on the submission time of the simulation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs data on the submission time of the simulation results into AI and determines the priority of evaluation.

[0051] The evaluation unit can adjust the order of evaluation based on the relevance of the simulation results during evaluation. For example, if the relevance of the simulation results is high, the evaluation unit prioritizes evaluation. Furthermore, if the relevance of the simulation results is low, the evaluation unit can postpone evaluation. Furthermore, the evaluation unit can gradually adjust the order of evaluation according to the relevance of the simulation results. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the simulation results. This enables efficient evaluation by adjusting the order of evaluation based on the relevance of the simulation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs relevance data of the simulation results into AI and adjusts the order of evaluation.

[0052] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit can make an evaluation that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the evaluation unit can make an evaluation that avoids technical terminology. Furthermore, the evaluation unit can gradually adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, the evaluation unit adjusts the use of technical terminology in the evaluation based on the user's level of expertise. This allows for an evaluation that is easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the user's level of expertise data into the AI ​​and adjusts the use of technical terminology in the evaluation.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The simulation unit can take into account the virtual consumer's health condition when simulating the virtual consumer's behavior. For example, if the virtual consumer is health-conscious, it can simulate the behavior of preferentially purchasing products that are considered to be good for health. Also, if the virtual consumer has a specific allergy, it can simulate the behavior of avoiding products that cater to that allergy. Furthermore, if the virtual consumer is on a diet, it can simulate the behavior of selecting low-calorie products. In this way, by performing a simulation based on the virtual consumer's health condition, it is possible to reproduce consumer behavior that is closer to reality.

[0055] The simulation unit can take into account the social influence of the virtual consumer when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer is an influencer, the simulation unit can simulate the impact of that purchasing behavior on other virtual consumers. Also, if the virtual consumer has influence over family and friends, the simulation unit can also simulate the purchasing behavior taking that influence into account. Furthermore, if the virtual consumer belongs to a specific community, the simulation unit can also simulate the purchasing behavior taking into account the influence within that community. In this way, by taking into account the social influence of the virtual consumer, it is possible to reproduce consumer behavior that is closer to reality.

[0056] The simulation unit can take into account the virtual consumer's past purchasing history when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer has frequently purchased products of a particular brand in the past, the simulation unit can simulate the behavior of preferentially selecting products of that brand. Also, if the virtual consumer has purchased products of a particular category in the past, the simulation unit can simulate the behavior of selecting products of that category. Furthermore, if the virtual consumer has purchased products in a particular price range in the past, the simulation unit can simulate the behavior of selecting products in that price range. In this way, by taking into account the virtual consumer's past purchasing history, it is possible to reproduce consumer behavior that is closer to reality.

[0057] The simulation unit can take into account the lifestyle of the virtual consumer when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer likes outdoor activities, the simulation unit can simulate behavior in which the virtual consumer prioritizes purchasing outdoor goods. If the virtual consumer likes urban life, the simulation unit can also simulate behavior in which the virtual consumer selects products related to urban life. Furthermore, if the virtual consumer is ecologically conscious, the simulation unit can also simulate behavior in which the virtual consumer selects environmentally friendly products. In this way, by taking the lifestyle of the virtual consumer into consideration, it is possible to reproduce consumer behavior that is closer to reality.

[0058] The simulation unit can take into account the economic situation of the virtual consumer when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer is a high-income earner, the simulation unit can simulate behavior in which the virtual consumer preferentially purchases high-priced items. If the virtual consumer is a low-income earner, the simulation unit can also simulate behavior in which the virtual consumer selects low-priced items. Furthermore, if the virtual consumer is a middle-income earner, the simulation unit can also simulate behavior in which the virtual consumer selects middle-priced items. In this way, by taking into account the economic situation of the virtual consumer, it is possible to reproduce consumer behavior that is closer to reality.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit inputs information about a new business or service. Examples of new businesses or services include, but are not limited to, technology services, food and beverage services, and entertainment. For example, a corporation can input information about a new beverage or application into the reception unit. Step 2: The simulation unit uses the generation AI to start a simulation in the virtual world based on the information entered by the reception unit. The generation AI acts as a virtual consumer living in the virtual world using technologies such as natural language generation, image generation, and behavioral simulation. The simulation unit uses the generation AI to act as a virtual consumer living in the virtual world and reproduces the behavior of real consumers based on big data such as purchase data from electronic payment systems and search history from search engines. For example, the generation AI learns big data such as purchase data from electronic payment systems and search history from search engines, and simulates consumer behavior in the virtual world. Step 3: The evaluation unit evaluates the results of the simulation performed by the simulation unit. For example, the evaluation unit calculates the sales and profits of the new service based on the simulation results. The evaluation unit can also provide information for formulating a marketing strategy based on the simulation results. For example, the evaluation unit predicts sales and profits based on the simulation results and formulates a marketing strategy.

[0061] (Example 2) A simulation system according to an embodiment of the present invention simulates whether a corporation can actually generate sales and profits when developing a new business or service aimed at general consumers. This simulation system creates a virtual living space by having a generation AI, which has been input with the concept of "purchase," live in a virtual world. It is possible to simulate the sales potential of a new service when it is launched in this virtual world before it is actually released to the market. The generation AI learns big data, such as purchase data from electronic payment systems and search history from search engines, to reproduce the lives of real people as closely as possible. For example, a corporation inputs information about a new business or service, such as information about a new beverage or application. This information is then input into the generation AI. The generation AI then begins a simulation in the virtual world based on the input information. The generation AI acts as a virtual consumer living in the virtual world and tries out new services. For example, it purchases and drinks a new beverage. The generation AI then reproduces the behavior of real consumers based on big data, such as purchase data from electronic payment systems and search history from search engines. This makes consumer behavior in the virtual world more realistic. Based on the results of the simulation, the generative AI calculates the extent to which a new service will be used and the expected sales. For example, it simulates how many virtual consumers will purchase a new beverage and how much profit will be generated. This allows a company to simulate the sales and profits of a new business or service before actually launching it on the market. This reduces risk and enables the development of effective marketing strategies. For example, a new application can be simulated in the virtual world to determine in advance how many downloads it can expect and how much revenue it can generate. A new beverage can also be simulated in the virtual world to determine in advance how much sales it can expect and how much profit it can generate. This allows a company to increase the chances of success for new businesses and services.

[0062] A simulation system according to an embodiment includes a reception unit, a simulation unit, and an evaluation unit. The reception unit inputs information about a new business or service. Examples of new businesses or services include, but are not limited to, technical services, food and beverage services, and entertainment services. For example, a corporation can input information about a new beverage or application to the reception unit. The simulation unit uses a generation AI to start a simulation in a virtual world based on the information input by the reception unit. The generation AI acts as a virtual consumer living in the virtual world using technologies such as natural language generation, image generation, and behavioral simulation. The simulation unit acts as a virtual consumer living in the virtual world using the generation AI and reproduces the behavior of real consumers based on big data such as purchase data from an electronic payment system and search history from a search engine. For example, the generation AI learns big data such as purchase data from an electronic payment system and search history from a search engine, and simulates consumer behavior in the virtual world. The evaluation unit evaluates the results of the simulation by the simulation unit. For example, the evaluation unit calculates sales and profits of the new service based on the simulation results. The evaluation unit can also provide information for developing a marketing strategy based on the simulation results. For example, the evaluation unit may predict sales and profits based on the simulation results and formulate a marketing strategy. This allows the simulation system according to the embodiment to simulate sales and profits of new businesses and new services.

[0063] The simulation unit can act as a virtual consumer living in a virtual world using the generation AI. The generation AI can, for example, simulate conversations as a virtual consumer using natural language generation technology. The generation AI can also simulate the visual behavior of a virtual consumer using image generation technology. Furthermore, the generation AI can simulate the purchasing behavior of a virtual consumer using behavior simulation technology. For example, the generation AI can simulate actions such as purchasing and trying a new beverage in the virtual world. This allows for a simulation that is close to reality by reproducing the behavior of a virtual consumer. Some or all of the above-mentioned processing in the simulation unit is performed using the generation AI. For example, the simulation unit inputs the behavior of a virtual consumer into the generation AI and simulates that behavior.

[0064] The simulation unit can reproduce the behavior of real consumers based on big data such as purchase data from an electronic payment system or search history from a search engine. Examples of big data include, but are not limited to, electronic payment data, search history data, and social media data. For example, the simulation unit simulates the purchasing behavior of a virtual consumer based on the purchase data from an electronic payment system. The simulation unit can also simulate the interests of the virtual consumer based on search history from a search engine. Furthermore, the simulation unit can simulate the social network of the virtual consumer based on social media data. For example, the simulation unit simulates what products the virtual consumer will purchase based on the electronic payment data. This reproduces real consumer behavior, improving the accuracy of the simulation. Some or all of the above-described processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs big data into the generation AI and simulates consumer behavior based on that data.

[0065] The evaluation unit can calculate the sales and profits of the new service based on the simulation results. Sales and profits include, but are not limited to, revenue models, cost calculations, and profit margins, for example. The evaluation unit, for example, forecasts sales based on the simulation results. The evaluation unit can also forecast profits based on the simulation results. Furthermore, the evaluation unit can evaluate customer satisfaction based on the simulation results. For example, the evaluation unit forecasts sales of the new service based on the simulation results and calculates how much profit will be obtained. In this way, by calculating sales and profits based on the simulation results, it is possible to predict market reaction in advance. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the simulation results into AI and predicts sales and profits.

[0066] The simulation unit can act as a virtual consumer based on information about a new beverage or application. Examples of new beverages or applications include, but are not limited to, energy drinks, health drinks, mobile apps, and web apps. The simulation unit can act as a virtual consumer based on information about a new beverage. The simulation unit can also act as a virtual consumer based on information about a new application. The simulation unit can also act as a virtual consumer based on information about a new service. For example, the simulation unit simulates behavior such as purchasing and drinking a new beverage. This enables specific simulations by acting as a virtual consumer based on information about the new beverage or application. Some or all of the above-described processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs information about the new beverage or application into the generation AI and simulates behavior as a virtual consumer based on that information.

[0067] The evaluation unit can provide information for formulating a marketing strategy based on the simulation results. Marketing strategies include, but are not limited to, selection of a target market, promotion methods, and pricing. For example, the evaluation unit selects a target market based on the simulation results. The evaluation unit can also plan a promotion method based on the simulation results. Furthermore, the evaluation unit can set prices based on the simulation results. For example, the evaluation unit selects a target market based on the simulation results and proposes an optimal promotion method. This allows for the formulation of an effective marketing strategy by providing information for formulating a marketing strategy based on the simulation results. Some or all of the above-described processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the simulation results into AI and formulates a marketing strategy.

[0068] The reception unit can estimate the user's emotions and adjust the timing of information input for a new business or new service based on the estimated user emotions. For example, if a user is feeling stressed, the reception unit can prompt the user to input information at a time when the user can relax. Furthermore, if the user is concentrating, the reception unit can also prompt the user to input information at that time. Furthermore, if the user is tired, the reception unit can prompt the user to input information after a break. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This enables efficient information input by adjusting the timing of information input according to the user's emotions. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's emotion data into AI and adjusts the timing of information input.

[0069] The reception unit can analyze past input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of information that the user has input in the past and suggest the optimal input method. Furthermore, the reception unit can predict and suggest the input method to be used in a specific time period based on the user's past input history. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs past input history data into AI and selects the optimal input method.

[0070] When inputting information about a new business or new service, the reception unit can filter the information based on the user's current business situation or areas of interest. For example, the reception unit can prioritize and display highly relevant information based on the user's current business situation. The reception unit can also filter and display related information based on the user's areas of interest. Furthermore, the reception unit can combine the user's business situation and areas of interest to provide optimal information. For example, if the user is in the food and beverage industry, the reception unit can prioritize and display information about new businesses and new services related to the food and beverage industry. This allows highly relevant information to be provided by filtering information based on the user's business situation and areas of interest. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs data about the user's business situation and areas of interest into AI and performs filtering.

[0071] When inputting information for a new business or new service, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface optimized for text input. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. For example, if the user selects voice input, the reception unit inputs information using voice recognition software. This enables efficient information input by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input method data into AI and selects the optimal input means.

[0072] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, when the user is nervous, the reception unit prioritizes input of important information. Furthermore, when the user is relaxed, the reception unit can also input detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the minimum necessary information. For example, the reception unit records the user's voice and estimates the user's emotions using voice analysis technology. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows important information to be input preferentially by determining the priority of information according to the user's emotions. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's emotion data into AI and determines the priority of information.

[0073] When inputting information about new businesses or new services, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. The reception unit, for example, prioritizes displaying relevant information based on the user's current location. The reception unit can also provide region-specific information based on the user's geographical location information. Furthermore, the reception unit can filter optimal information by taking into account the user's location information. For example, if the user is in a specific region, the reception unit prioritizes displaying information about new businesses or new services related to that region. This makes it possible to provide region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's geographical location information data into AI and selects highly relevant information.

[0074] When inputting information about a new business or new service, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit analyzes the user's social media posts and inputs related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. Furthermore, the reception unit can input related information based on the user's social media check-in information. For example, the reception unit analyzes the user's social media posts and inputs information about related new businesses or new services. This makes it possible to provide highly relevant information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's social media data into AI and selects related information.

[0075] The reception unit can customize the input method by reflecting the user's past feedback when inputting information for a new business or new service. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. Furthermore, the reception unit can optimize the input procedure based on the user's feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's past feedback data into AI to customize the input method.

[0076] The simulation unit can estimate the user's emotions and adjust the way the simulation is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can perform a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generation AI can also perform a simulation that emphasizes the shortest route. If the user is excited, the generation AI can also perform a simulation that adds visually stimulating effects. For example, the simulation unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for a more appropriate simulation by adjusting the way the simulation is presented based on the user's emotions. Some or all of the above-described processing in the simulation unit is performed using the generation AI. For example, the simulation unit inputs the user's emotion data into the generation AI and adjusts the way the simulation is presented.

[0077] During the simulation, the simulation unit can adjust the level of detail of the simulation based on the importance of the virtual consumer. For example, the simulation unit performs a detailed simulation for an important virtual consumer. The simulation unit can also perform a simplified simulation for a less important virtual consumer. Furthermore, the simulation unit can gradually adjust the level of detail of the simulation according to the importance of the virtual consumer. For example, the simulation unit adjusts the level of detail of the simulation based on the purchasing power or influence of the virtual consumer. This allows for an efficient simulation by adjusting the level of detail of the simulation according to the importance of the virtual consumer. Some or all of the above-described processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs importance data of the virtual consumer into the generation AI and adjusts the level of detail of the simulation.

[0078] During the simulation, the simulation unit can apply different simulation algorithms depending on the category of the virtual consumer. For example, the simulation unit can apply a simulation algorithm for young people to a young virtual consumer. The simulation unit can also apply a simulation algorithm for elderly people to an elderly virtual consumer. Furthermore, the simulation unit can apply a related simulation algorithm to a virtual consumer with specific interests. For example, the simulation unit applies a simulation algorithm based on the age and gender of the virtual consumer. In this way, applying a simulation algorithm depending on the category of the virtual consumer enables a simulation that is closer to reality. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs category data of the virtual consumer into the generation AI and applies the simulation algorithm.

[0079] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the simulation unit analyzes the user's past simulation results to improve accuracy. The simulation unit can also optimize the simulation algorithm based on data obtained from the past simulation results. Furthermore, the simulation unit can adjust simulation parameters by referring to the user's past simulation results. For example, the simulation unit builds a feedback loop to improve the accuracy of the simulation based on the past simulation results. This improves the accuracy of the simulation by referring to the user's past simulation results. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs past simulation result data into the generation AI to improve the accuracy of the simulation.

[0080] The simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated user emotions. For example, if the user is in a hurry, the generation AI can perform a short, to-the-point simulation. Alternatively, if the user is relaxed, the generation AI can perform a longer simulation with detailed explanations. Furthermore, if the user is excited, the generation AI can perform a simulation with visually stimulating effects. For example, the simulation unit records the user's voice and estimates the user's emotions using voice analysis technology. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for a more appropriate simulation by adjusting the length of the simulation according to the user's emotions. Some or all of the above-described processing in the simulation unit is performed using the generation AI. For example, the simulation unit inputs the user's emotion data into the generation AI and adjusts the length of the simulation.

[0081] During the simulation, the simulation unit can determine the priority of the simulation based on the behavioral history of the virtual consumer. For example, the simulation unit determines the priority of the simulation based on the virtual consumer's past purchase history. The simulation unit can also determine the priority of the simulation based on the virtual consumer's past search history. Furthermore, the simulation unit can analyze the virtual consumer's past behavioral patterns and determine the priority of the simulation. For example, the simulation unit determines the priority of the simulation based on the virtual consumer's purchase history. This enables efficient simulation by determining the priority of the simulation based on the virtual consumer's behavioral history. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs the virtual consumer's behavioral history data into the generation AI and determines the priority of the simulation.

[0082] The simulation unit can adjust the order of simulations based on the relevance of the virtual consumer during the simulation. For example, if the relevance of the virtual consumer is high, the simulation unit adjusts the order of simulations as a priority. Also, if the relevance of the virtual consumer is low, the simulation unit can postpone the order of simulations. Furthermore, the simulation unit can gradually adjust the order of simulations according to the relevance of the virtual consumer. For example, the simulation unit adjusts the order of simulations based on the interests and purchasing history of the virtual consumer. This enables efficient simulations by adjusting the order of simulations based on the relevance of the virtual consumer. Some or all of the above-mentioned processing in the simulation unit is performed using a generation AI. For example, the simulation unit inputs relevance data of the virtual consumer into the generation AI and adjusts the order of simulations.

[0083] During the simulation, the simulation unit can adjust the use of technical terminology in the simulation according to the user's level of expertise. For example, if the user has technical expertise, the simulation unit can have the generation AI perform a simulation that makes heavy use of technical terminology. Furthermore, if the user does not have technical expertise, the simulation unit can have the generation AI perform a simulation that avoids technical terminology. Furthermore, the simulation unit can gradually adjust the use of technical terminology in the simulation according to the user's level of expertise. For example, the simulation unit adjusts the use of technical terminology in the simulation based on the user's level of expertise. This allows for an easy-to-understand simulation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the simulation unit is performed using the generation AI. For example, the simulation unit inputs the user's level of expertise data into the generation AI and adjusts the use of technical terminology in the simulation.

[0084] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user emotions. For example, if the user is nervous, the evaluation unit provides a simple, highly visible evaluation method. Furthermore, if the user is relaxed, the evaluation unit can provide an evaluation method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide an evaluation method that focuses on the key points. For example, the evaluation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for more appropriate evaluation by adjusting the evaluation method according to the user's emotions. Some or all of the above-described processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the user's emotion data into AI and adjusts the evaluation method.

[0085] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the simulation result during evaluation. For example, the evaluation unit performs a detailed evaluation for important simulation results. The evaluation unit can also perform a simplified evaluation for less important simulation results. Furthermore, the evaluation unit can gradually adjust the level of detail of the evaluation based on the importance of the simulation result. For example, the evaluation unit adjusts the level of detail of the evaluation based on the importance of the simulation result. This enables efficient evaluation by adjusting the level of detail of the evaluation based on the importance of the simulation result. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs importance data of the simulation result to AI and adjusts the level of detail of the evaluation.

[0086] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the simulation results. For example, the evaluation unit can apply a beverage-specific evaluation algorithm to the simulation results of a new beverage. The evaluation unit can also apply an application-specific evaluation algorithm to the simulation results of a new application. Furthermore, the evaluation unit can select and apply an optimal evaluation algorithm depending on the category of the simulation results. For example, the evaluation unit applies an evaluation algorithm based on the category of the simulation results. This enables more appropriate evaluation by applying an evaluation algorithm depending on the category of the simulation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs category data of the simulation results into AI and applies the evaluation algorithm.

[0087] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when performing the evaluation. For example, the evaluation unit analyzes the user's past evaluation results to improve the accuracy of the evaluation. The evaluation unit can also optimize the evaluation algorithm based on data obtained from the past evaluation results. Furthermore, the evaluation unit can adjust the evaluation parameters by referring to the user's past evaluation results. For example, the evaluation unit builds a feedback loop to improve the accuracy of the evaluation based on the past evaluation results. This improves the accuracy of the evaluation by referring to the user's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs past evaluation result data into AI to improve the accuracy of the evaluation.

[0088] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can prioritize important evaluations. Furthermore, if the user is relaxed, the evaluation unit can also perform detailed evaluations. Furthermore, if the user is in a hurry, the evaluation unit can prioritize the minimum necessary evaluations. For example, the evaluation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the priority of evaluations to be determined according to the user's emotions, thereby prioritizing important evaluations. Some or all of the above-described processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the user's emotion data into AI and determines the priority of evaluations.

[0089] During evaluation, the evaluation unit can determine the priority of evaluation based on the submission time of the simulation results. For example, if the simulation results are submitted early, the evaluation unit can prioritize the evaluation. Also, if the simulation results are submitted late, the evaluation unit can postpone the evaluation. Furthermore, the evaluation unit can gradually adjust the priority of evaluation based on the submission time. For example, the evaluation unit determines the priority of evaluation based on the submission time of the simulation results. This enables efficient evaluation by determining the priority of evaluation based on the submission time of the simulation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs data on the submission time of the simulation results into AI and determines the priority of evaluation.

[0090] The evaluation unit can adjust the order of evaluation based on the relevance of the simulation results during evaluation. For example, if the relevance of the simulation results is high, the evaluation unit prioritizes evaluation. Furthermore, if the relevance of the simulation results is low, the evaluation unit can postpone evaluation. Furthermore, the evaluation unit can gradually adjust the order of evaluation according to the relevance of the simulation results. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the simulation results. This enables efficient evaluation by adjusting the order of evaluation based on the relevance of the simulation results. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs relevance data of the simulation results into AI and adjusts the order of evaluation.

[0091] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit can make an evaluation that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the evaluation unit can make an evaluation that avoids technical terminology. Furthermore, the evaluation unit can gradually adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, the evaluation unit adjusts the use of technical terminology in the evaluation based on the user's level of expertise. This allows for an evaluation that is easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the evaluation unit is performed using AI. For example, the evaluation unit inputs the user's level of expertise data into the AI ​​and adjusts the use of technical terminology in the evaluation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, simulation unit, evaluation unit, and generation AI is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, simulation unit, evaluation unit, and generation AI is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, simulation unit, evaluation unit, and generation AI is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, simulation unit, evaluation unit, and generation AI is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation AI is realized by the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The simulation unit can take into account the virtual consumer's health condition when simulating the virtual consumer's behavior. For example, if the virtual consumer is health-conscious, it can simulate the behavior of preferentially purchasing products that are considered to be good for health. Also, if the virtual consumer has a specific allergy, it can simulate the behavior of avoiding products that cater to that allergy. Furthermore, if the virtual consumer is on a diet, it can simulate the behavior of selecting low-calorie products. In this way, by performing a simulation based on the virtual consumer's health condition, it is possible to reproduce consumer behavior that is closer to reality.

[0094] The simulation unit can take into account the social influence of the virtual consumer when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer is an influencer, the simulation unit can simulate the impact of that purchasing behavior on other virtual consumers. Also, if the virtual consumer has influence over family and friends, the simulation unit can also simulate the purchasing behavior taking that influence into account. Furthermore, if the virtual consumer belongs to a specific community, the simulation unit can also simulate the purchasing behavior taking into account the influence within that community. In this way, by taking into account the social influence of the virtual consumer, it is possible to reproduce consumer behavior that is closer to reality.

[0095] The simulation unit can take into account the virtual consumer's past purchasing history when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer has frequently purchased products of a particular brand in the past, the simulation unit can simulate the behavior of preferentially selecting products of that brand. Also, if the virtual consumer has purchased products of a particular category in the past, the simulation unit can simulate the behavior of selecting products of that category. Furthermore, if the virtual consumer has purchased products in a particular price range in the past, the simulation unit can simulate the behavior of selecting products in that price range. In this way, by taking into account the virtual consumer's past purchasing history, it is possible to reproduce consumer behavior that is closer to reality.

[0096] The simulation unit can take into account the lifestyle of the virtual consumer when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer likes outdoor activities, the simulation unit can simulate behavior in which the virtual consumer prioritizes purchasing outdoor goods. If the virtual consumer likes urban life, the simulation unit can also simulate behavior in which the virtual consumer selects products related to urban life. Furthermore, if the virtual consumer is ecologically conscious, the simulation unit can also simulate behavior in which the virtual consumer selects environmentally friendly products. In this way, by taking the lifestyle of the virtual consumer into consideration, it is possible to reproduce consumer behavior that is closer to reality.

[0097] The simulation unit can take into account the economic situation of the virtual consumer when simulating the purchasing behavior of the virtual consumer. For example, if the virtual consumer is a high-income earner, the simulation unit can simulate behavior in which the virtual consumer preferentially purchases high-priced items. If the virtual consumer is a low-income earner, the simulation unit can also simulate behavior in which the virtual consumer selects low-priced items. Furthermore, if the virtual consumer is a middle-income earner, the simulation unit can also simulate behavior in which the virtual consumer selects middle-priced items. In this way, by taking into account the economic situation of the virtual consumer, it is possible to reproduce consumer behavior that is closer to reality.

[0098] The simulation unit can estimate the user's emotions and adjust the difficulty of the simulation based on the estimated user's emotions. For example, if the user is feeling stressed, the simulation difficulty can be lowered to allow the user to relax. Also, if the user is concentrating, the simulation difficulty can be increased to provide the user with a challenge. Furthermore, if the user is tired, the simulation difficulty can be adjusted to allow the user to proceed without straining themselves. In this way, adjusting the simulation difficulty according to the user's emotions enables a more appropriate simulation.

[0099] The simulation unit can estimate the user's emotions and adjust the feedback method of the simulation based on the estimated user's emotions. For example, if the user is nervous, positive feedback can be given preferentially. If the user is relaxed, detailed feedback can be given. Furthermore, if the user is in a hurry, brief feedback can be given. This allows for more effective feedback by adjusting the feedback method according to the user's emotions.

[0100] The simulation unit can estimate the user's emotions and adjust the speed of the simulation based on the estimated user's emotions. For example, if the user is relaxed, the simulation can proceed at a leisurely pace. If the user is in a hurry, the simulation can proceed quickly. Furthermore, if the user is excited, the simulation can be provided with visually stimulating effects. This allows for a more appropriate simulation by adjusting the speed of the simulation according to the user's emotions.

[0101] The simulation unit can estimate the user's emotions and customize the content of the simulation based on the estimated user's emotions. For example, if the user is relaxed, it can provide a simulation with content that helps the user to relax. If the user is concentrating, it can provide a simulation with content that requires concentration. Furthermore, if the user is tired, it can provide a simulation with content that helps the user to relax. This allows for more appropriate simulations by customizing the content of the simulation according to the user's emotions.

[0102] The simulation unit can estimate the user's emotions and adjust the simulation interface based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible interface can be provided. If the user is relaxed, an interface containing detailed information can be provided. Furthermore, if the user is in a hurry, an interface that focuses on the main points can be provided. This allows for a more appropriate simulation by adjusting the interface according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit inputs information about a new business or service. Examples of new businesses or services include, but are not limited to, technology services, food and beverage services, and entertainment. For example, a corporation can input information about a new beverage or application into the reception unit. Step 2: The simulation unit uses the generation AI to start a simulation in the virtual world based on the information entered by the reception unit. The generation AI acts as a virtual consumer living in the virtual world using technologies such as natural language generation, image generation, and behavioral simulation. The simulation unit uses the generation AI to act as a virtual consumer living in the virtual world and reproduces the behavior of real consumers based on big data such as purchase data from electronic payment systems and search history from search engines. For example, the generation AI learns big data such as purchase data from electronic payment systems and search history from search engines, and simulates consumer behavior in the virtual world. Step 3: The evaluation unit evaluates the results of the simulation performed by the simulation unit. For example, the evaluation unit calculates the sales and profits of the new service based on the simulation results. The evaluation unit can also provide information for formulating a marketing strategy based on the simulation results. For example, the evaluation unit predicts sales and profits based on the simulation results and formulates a marketing strategy.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reception desk where information on new businesses or new services is entered; a simulation unit that starts a simulation in a virtual world based on the information input by the reception unit; an evaluation unit that evaluates the results of the simulation by the simulation unit; Equipped with A system characterized by:

2. The simulation unit Act as a virtual consumer living in a virtual world with generative AI The system of claim 1 .

3. The simulation unit Reproducing real-world consumer behavior based on big data such as purchase data from electronic payment systems or search history from search engines The system of claim 1 .

4. The evaluation unit Calculate sales and profits for new services based on simulation results The system of claim 1 .

5. The simulation unit Act as a virtual consumer based on information about a new beverage or app The system of claim 1 .

6. The evaluation unit Providing information for formulating marketing strategies based on simulation results The system of claim 1 .

7. The reception unit To estimate a user's emotions and adjust the timing of inputting information for a new business or a new service based on the estimated user's emotions. The system of claim 1 .

8. The reception unit Analyze past input history and select the optimal input method The system of claim 1 .

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