System

Abstract

An object of a system according to an embodiment is to fairly and equitably calculate and present values of a product, a service, point information, and the like.SOLUTION: A system includes a data collection part, a value calculation part, and a value presentation part. The data collection unit collects target data. The value calculation unit calculates a value based on the data collected by the data collection unit. The value presentation unit presents the value calculated by the value calculation 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] Conventional technology has had the problem of making it difficult to fairly and equitably calculate the value of products, services, location information, etc.

[0005] The system according to the embodiment aims to calculate and present the value of products, services, location information, etc. in a fair and impartial manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a value calculation unit, and a value presentation unit. The data collection unit collects target data. The value calculation unit calculates a value based on the data collected by the data collection unit. The value presentation unit presents the value calculated by the value calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can calculate and present the value of products, services, location information, and the like in a fair and impartial manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The value calculation system according to an embodiment of the present invention is a system that calculates the value of any product, service, or location information using statistical methods and presents the value in a fair and just manner. As a result, the value calculation system can calculate and present the value of any product, service, or location information in a fair and just manner.

[0029] A value calculation system according to an embodiment includes a data collection unit, a value calculation unit, and a value presentation unit. The data collection unit collects target data. For example, the data collection unit collects product market prices, user reviews, product performance data, and the like. The data collection unit can also collect the number of service users, user satisfaction levels, and service coverage areas. The data collection unit can also collect geographical conditions of locations, transportation access, and the status of surrounding facilities. For example, the data collection unit collects product market prices from online shopping sites and user reviews from social media. The number of service users is obtained from a service provider database, and user satisfaction levels are collected from a questionnaire survey. The geographical conditions of locations are obtained from a map database, and transportation access is collected from a transportation facility database. The value calculation unit calculates value based on the collected data. For example, the value calculation unit calculates the value of a product using regression analysis. The value calculation unit can also calculate the value of a service using clustering. The value calculation unit can also calculate the value of location information using statistical methods. For example, the value calculation unit uses regression analysis to analyze the relationship between the market price of a product and user reviews to calculate the value of the product. Clustering is used to analyze the relationship between the number of users of a service and satisfaction levels to calculate the value of the service. Statistical methods are used to analyze the relationship between the geographical conditions of a location and transportation access to calculate the value of the location information. The value presentation unit presents the calculated value. For example, the value presentation unit presents the value of the product as a numerical value. The value presentation unit can also present the value of the service as a graph. The value presentation unit can also display the value of the location information on a map. For example, the value presentation unit displays the value of the product as a score from 0 to 100 and the value of the service as a bar graph. The value of the location information is displayed in different colors on the map. This allows the value calculation system according to the embodiment to calculate and present the value of any product, service, or location information in a fair and impartial manner. For example, consumers can compare product values ​​to select the optimal product. Service users can compare service values ​​to select the optimal service.Real estate buyers can compare the value of location information and choose the most suitable property.

[0030] The data collection unit can evaluate the reliability of collected data in real time and automatically exclude low-reliability data. For example, the data collection unit applies a reliability evaluation algorithm to data collected by the generation AI and calculates a reliability score. For example, it assigns a score based on the reliability of the data source and the consistency of the data, and excludes low-reliability data. In addition, in order to evaluate reliability in real time, the data collection unit uses technology that shortens the time from data collection to evaluation. For example, the data collection unit evaluates reliability simultaneously with data collection and immediately excludes low-reliability data. This eliminates low-reliability data, improving the accuracy of value calculation.

[0031] The data collection unit can analyze correlations between different data sources to ensure data consistency. For example, the data collection unit analyzes correlations between data collected by the generation AI from different data sources to extract consistent data. For example, it compares ratings for the same product on different review sites to extract consistent ratings. The data collection unit also performs data consistency checks to ensure data consistency. For example, the data collection unit checks the consistency of the collected data and corrects or excludes inconsistent data. This ensures data consistency, thereby improving the reliability of value calculations.

[0032] The data collection unit can collect and analyze unstructured data from social media or blogs. For example, the data collection unit uses a generative AI to collect social media posts and blog articles and analyze the unstructured data. For example, the data collection unit collects tweets and blog reviews about products and performs text mining. The data collection unit also uses text mining technology to analyze the unstructured data. For example, the data collection unit analyzes the collected unstructured data with a text mining algorithm and uses it to calculate value. In this way, by utilizing unstructured data, value can be calculated from a wider variety of information sources.

[0033] The data collection unit collects data from different regions and cultural spheres, and can calculate value that reflects regional values ​​and trends. In the data collection unit, for example, the generation AI collects data from different regions and cultural spheres and analyzes regional values ​​and trends. For example, it collects consumer behavior and market needs for each region. The data collection unit also integrates data for each region to reflect regional values ​​and trends. For example, the data collection unit integrates consumer behavior data for each region to reflect regional values. This enables more accurate value calculation by reflecting regional values ​​and trends.

[0034] The value calculation unit can improve the accuracy of value calculation by combining different statistical methods. For example, the generative AI in the value calculation unit combines Bayesian estimation and deep learning to improve the accuracy of value calculation. For example, it sets a prior probability using Bayesian estimation and extracts data features using deep learning. The value calculation unit also calculates value by combining regression analysis and clustering. For example, the value calculation unit analyzes data relationships using regression analysis and classifies data using clustering. In this way, the accuracy of value calculation is improved by combining different statistical methods.

[0035] The value calculation unit can perform simulations on the calculated value and predict value fluctuations under different scenarios. For example, the value calculation unit simulates value fluctuations under different scenarios for the value calculated by the generation AI. For example, it performs a simulation that takes into account changes in economic conditions and fluctuations in market trends. The value calculation unit also predicts value fluctuations using Monte Carlo simulation. For example, the value calculation unit sets multiple scenarios and simulates value fluctuations under each scenario. This makes it possible to more accurately estimate future value by predicting value fluctuations under different scenarios.

[0036] The value calculation unit can incorporate environmental data and climate data to calculate value taking environmental factors into account. For example, the value calculation unit uses a generation AI to collect environmental data and climate data and incorporate it into value calculation. For example, the value calculation unit evaluates value based on data such as temperature and precipitation. The value calculation unit also integrates environmental data to take environmental factors into account. For example, the value calculation unit calculates value taking into account the impact of climate change. This allows for more realistic value calculation by taking environmental factors into account.

[0037] The value calculation unit can integrate data from different industries and fields and calculate value from a cross-industry perspective. For example, the value calculation unit calculates value by having the generative AI collect and integrate data from different industries and fields. For example, it evaluates value by integrating data from the technology field and the consumer market. The value calculation unit also analyzes data from different industries to reflect a cross-industry perspective. For example, the value calculation unit integrates data from the medical and entertainment fields and calculates value. This makes it possible to calculate value more comprehensively by integrating data from different industries and fields.

[0038] The value presentation unit can provide users with details of the calculation process to ensure transparency when presenting value. For example, the value presentation unit generates a report including an explanation of each step so that the generation AI can provide users with details of the value calculation process. For example, the report may include details of the data sources and statistical methods used. The value presentation unit also provides an interactive dashboard to ensure transparency. For example, the value presentation unit provides a dashboard that allows users to check the calculation process in real time. This ensures transparency in the calculation process, thereby gaining users' trust.

[0039] The value presentation unit can incorporate evaluations from third-party organizations into the presented value to ensure fairness. For example, the value presentation unit builds a system that incorporates evaluations from third-party organizations into the value calculated by the generation AI. For example, it reflects the opinions of experts and data from independent evaluation organizations. In addition, the value presentation unit ensures transparency of the evaluation process to ensure fairness. For example, the value presentation unit provides users with details of the evaluation process and makes public the weighting criteria. In this way, incorporating evaluations from third-party organizations improves the fairness of value presentation.

[0040] The value presentation unit can present personalized value that reflects the user's individual needs and preferences. For example, the value presentation unit uses a generation AI to analyze the user's individual needs and preferences and present personalized value based on the results. For example, the value presentation unit customizes the value based on the user's past behavioral data and search history. The value presentation unit also optimizes the value presentation based on user feedback. For example, the value presentation unit collects user feedback and improves the value presentation method based on that feedback. This makes it possible to present more appropriate value by reflecting the user's individual needs and preferences.

[0041] The value presentation unit enables value presentation on different devices and platforms, allowing users to check value information anywhere. For example, the value presentation unit develops a multi-platform compatible system to enable the generation AI to present value on different devices and platforms. For example, it optimizes display on smartphones, tablets, and PCs. The value presentation unit also uses a cloud-based system to enable users to check value information anywhere. For example, the value presentation unit stores data on the cloud and allows users to access it via the Internet. This enables value presentation on different devices and platforms, improving user convenience.

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

[0043] The value calculation system may further include a prediction unit that analyzes a user's purchasing history and predicts future purchasing behavior based on past purchasing patterns. For example, the prediction unit may analyze the types and price ranges of products the user has purchased in the past and predict which products the user is likely to purchase next. The prediction unit may also analyze the user's purchasing frequency and seasonal purchasing trends and predict which products the user is likely to purchase at a particular time. Furthermore, the prediction unit may combine the user's purchasing history with market trends to more accurately predict future purchasing behavior. This allows users to predict their own purchasing behavior and make planned purchases.

[0044] The value calculation system may further include a health assessment unit that collects the user's health data and calculates the value of products and services based on the user's health status. For example, the health assessment unit may analyze data collected from the user's fitness tracker or smartwatch to suggest products and services suited to the user's health status. The health assessment unit may also analyze the user's food records and exercise history to calculate the value of products and services useful for maintaining health. Furthermore, the health assessment unit may combine the user's health data with market health trends to more accurately calculate the value of health-related products and services. This allows the user to select the most suitable products and services based on their health status.

[0045] The value calculation system can further include an influence evaluation unit that evaluates a user's social influence and calculates value based on that influence. For example, the influence evaluation unit analyzes the number of followers and engagement rate of the user on social media to evaluate social influence. The influence evaluation unit can also analyze the content of users' posts and the number of shares, and place emphasis on the evaluation of highly influential users. Furthermore, the influence evaluation unit can combine the user's social influence with market trends to improve the accuracy of value calculation. This makes it possible to calculate value taking social influence into account.

[0046] The value calculation system may further include a prediction unit that analyzes a user's purchasing history and predicts future purchasing behavior based on past purchasing patterns. For example, the prediction unit may analyze the types and price ranges of products the user has purchased in the past and predict which products the user is likely to purchase next. The prediction unit may also analyze the user's purchasing frequency and seasonal purchasing trends and predict which products the user is likely to purchase at a particular time. Furthermore, the prediction unit may combine the user's purchasing history with market trends to more accurately predict future purchasing behavior. This allows users to predict their own purchasing behavior and make planned purchases.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The data collection unit collects target data. For example, the data collection unit collects the market price of the product, user reviews, product performance data, the number of service users, user satisfaction, service coverage area, geographical conditions of the location, transportation access, and the status of surrounding facilities. Specifically, the market price of the product is collected from online shopping sites, and user reviews are collected from social media. The number of service users is obtained from the service provider's database, and user satisfaction is collected from a questionnaire survey. The geographical conditions of the location are obtained from a map database, and transportation access is collected from a transportation database. Step 2: The value calculation unit calculates value based on the collected data. For example, the value calculation unit calculates the value of a product using regression analysis, the value of a service using clustering, and the value of point information using statistical methods. Specifically, the value of the product is calculated by analyzing the relationship between the market price of the product and user reviews using regression analysis. The value of the service is calculated by analyzing the relationship between the number of users of the service and satisfaction using clustering. The value of the point information is calculated by analyzing the relationship between the geographical conditions of the point and transportation access using statistical methods. Step 3: The value presentation unit presents the calculated value. For example, the value presentation unit presents the value of the product as a numerical value, the value of the service as a graph, and the value of the location information on a map. Specifically, the value of the product is displayed as a score from 0 to 100, the value of the service is displayed as a bar graph, and the value of the location information is displayed in different colors on the map.

[0049] (Example 2) The value calculation system according to an embodiment of the present invention is a system that calculates the value of any product, service, or location information using statistical methods and presents the value in a fair and just manner. As a result, the value calculation system can calculate and present the value of any product, service, or location information in a fair and just manner.

[0050] A value calculation system according to an embodiment includes a data collection unit, a value calculation unit, and a value presentation unit. The data collection unit collects target data. For example, the data collection unit collects product market prices, user reviews, product performance data, and the like. The data collection unit can also collect the number of service users, user satisfaction levels, and service coverage areas. The data collection unit can also collect geographical conditions of locations, transportation access, and the status of surrounding facilities. For example, the data collection unit collects product market prices from online shopping sites and user reviews from social media. The number of service users is obtained from a service provider database, and user satisfaction levels are collected from a questionnaire survey. The geographical conditions of locations are obtained from a map database, and transportation access is collected from a transportation facility database. The value calculation unit calculates value based on the collected data. For example, the value calculation unit calculates the value of a product using regression analysis. The value calculation unit can also calculate the value of a service using clustering. The value calculation unit can also calculate the value of location information using statistical methods. For example, the value calculation unit uses regression analysis to analyze the relationship between the market price of a product and user reviews to calculate the value of the product. Clustering is used to analyze the relationship between the number of users of a service and satisfaction levels to calculate the value of the service. Statistical methods are used to analyze the relationship between the geographical conditions of a location and transportation access to calculate the value of the location information. The value presentation unit presents the calculated value. For example, the value presentation unit presents the value of the product as a numerical value. The value presentation unit can also present the value of the service as a graph. The value presentation unit can also display the value of the location information on a map. For example, the value presentation unit displays the value of the product as a score from 0 to 100 and the value of the service as a bar graph. The value of the location information is displayed in different colors on the map. This allows the value calculation system according to the embodiment to calculate and present the value of any product, service, or location information in a fair and impartial manner. For example, consumers can compare product values ​​to select the optimal product. Service users can compare service values ​​to select the optimal service.Real estate buyers can compare the value of location information and choose the most suitable property.

[0051] The data collection unit can evaluate the reliability of collected data in real time and automatically exclude low-reliability data. For example, the data collection unit applies a reliability evaluation algorithm to data collected by the generation AI and calculates a reliability score. For example, it assigns a score based on the reliability of the data source and the consistency of the data, and excludes low-reliability data. In addition, in order to evaluate reliability in real time, the data collection unit uses technology that shortens the time from data collection to evaluation. For example, the data collection unit evaluates reliability simultaneously with data collection and immediately excludes low-reliability data. This eliminates low-reliability data, improving the accuracy of value calculation.

[0052] The data collection unit can analyze correlations between different data sources to ensure data consistency. For example, the data collection unit analyzes correlations between data collected by the generation AI from different data sources to extract consistent data. For example, it compares ratings for the same product on different review sites to extract consistent ratings. The data collection unit also performs data consistency checks to ensure data consistency. For example, the data collection unit checks the consistency of the collected data and corrects or excludes inconsistent data. This ensures data consistency, thereby improving the reliability of value calculations.

[0053] The data collection unit can extract emotions from user reviews or ratings and use the emotion data as part of the value calculation. For example, the data collection unit uses a generation AI to perform emotion analysis on user reviews and ratings to extract positive, negative, or neutral emotions. For example, the data collection unit analyzes the review text and calculates an emotion score. The data collection unit also uses an emotion analysis algorithm to use the emotion data in the value calculation. For example, the data collection unit weights the value calculation based on the emotion score to reflect the emotion data. In this way, using the emotion data enables more accurate value calculation.

[0054] The data collection unit can collect and analyze unstructured data from social media or blogs. For example, the data collection unit uses a generative AI to collect social media posts and blog articles and analyze the unstructured data. For example, the data collection unit collects tweets and blog reviews about products and performs text mining. The data collection unit also uses text mining technology to analyze the unstructured data. For example, the data collection unit analyzes the collected unstructured data with a text mining algorithm and uses it to calculate value. In this way, by utilizing unstructured data, value can be calculated from a wider variety of information sources.

[0055] The data collection unit collects data from different regions and cultural spheres, and can calculate value that reflects regional values ​​and trends. In the data collection unit, for example, the generation AI collects data from different regions and cultural spheres and analyzes regional values ​​and trends. For example, it collects consumer behavior and market needs for each region. The data collection unit also integrates data for each region to reflect regional values ​​and trends. For example, the data collection unit integrates consumer behavior data for each region to reflect regional values. This enables more accurate value calculation by reflecting regional values ​​and trends.

[0056] The data collection unit can analyze the emotions of users when they enter data in real time and suggest data collection methods that elicit positive emotions. For example, the data collection unit uses a generative AI to analyze the facial expressions and voice of users when they enter data and estimate emotions in real time. For example, the data collection unit analyzes the user's emotions using a camera or microphone and makes suggestions that elicit positive emotions. The data collection unit also uses an emotion estimation algorithm to elicit positive emotions. For example, the data collection unit provides positive feedback to the user based on the emotion score. This elicits positive emotions and improves the quality of the user's data entry.

[0057] The value calculation unit can improve the accuracy of value calculation by combining different statistical methods. For example, the generative AI in the value calculation unit combines Bayesian estimation and deep learning to improve the accuracy of value calculation. For example, it sets a prior probability using Bayesian estimation and extracts data features using deep learning. The value calculation unit also calculates value by combining regression analysis and clustering. For example, the value calculation unit analyzes data relationships using regression analysis and classifies data using clustering. In this way, the accuracy of value calculation is improved by combining different statistical methods.

[0058] The value calculation unit can perform simulations on the calculated value and predict value fluctuations under different scenarios. For example, the value calculation unit simulates value fluctuations under different scenarios for the value calculated by the generation AI. For example, it performs a simulation that takes into account changes in economic conditions and fluctuations in market trends. The value calculation unit also predicts value fluctuations using Monte Carlo simulation. For example, the value calculation unit sets multiple scenarios and simulates value fluctuations under each scenario. This makes it possible to more accurately estimate future value by predicting value fluctuations under different scenarios.

[0059] The value calculation unit can incorporate the user's emotional data into a statistical model and provide a calculation result that reflects the emotional value. For example, the value calculation unit uses a generation AI to incorporate the user's emotional data into a statistical model and provide a calculation result that reflects the emotional value. For example, the value calculation unit calculates the value by placing emphasis on data with strong positive emotions. The value calculation unit also analyzes the emotional data using an emotion estimation function and incorporates it into the statistical model. For example, the value calculation unit weights the value calculation based on the emotional score to reflect the emotional value. In this way, by incorporating the emotional data into the statistical model, it becomes possible to calculate a value that reflects the user's emotions.

[0060] The value calculation unit can incorporate environmental data and climate data to calculate value taking environmental factors into account. For example, the value calculation unit uses a generation AI to collect environmental data and climate data and incorporate it into value calculation. For example, the value calculation unit evaluates value based on data such as temperature and precipitation. The value calculation unit also integrates environmental data to take environmental factors into account. For example, the value calculation unit calculates value taking into account the impact of climate change. This allows for more realistic value calculation by taking environmental factors into account.

[0061] The value calculation unit can integrate data from different industries and fields and calculate value from a cross-industry perspective. For example, the value calculation unit calculates value by having the generative AI collect and integrate data from different industries and fields. For example, it evaluates value by integrating data from the technology field and the consumer market. The value calculation unit also analyzes data from different industries to reflect a cross-industry perspective. For example, the value calculation unit integrates data from the medical and entertainment fields and calculates value. This makes it possible to calculate value more comprehensively by integrating data from different industries and fields.

[0062] The value calculation unit can analyze the emotional response of the user when receiving the value calculation result and propose the optimal presentation method. For example, the value calculation unit uses a generation AI to analyze the user's emotional response and propose the optimal presentation method of the value calculation result. For example, it proposes a presentation format that elicits positive emotions. The value calculation unit also analyzes the emotional response using an emotion estimation function and optimizes the presentation method. For example, the value calculation unit provides the user with the optimal presentation method based on the emotion score. In this way, the optimal value presentation method can be provided by analyzing the user's emotional response.

[0063] The value presentation unit can provide users with details of the calculation process to ensure transparency when presenting value. For example, the value presentation unit generates a report including an explanation of each step so that the generation AI can provide users with details of the value calculation process. For example, the report may include details of the data sources and statistical methods used. The value presentation unit also provides an interactive dashboard to ensure transparency. For example, the value presentation unit provides a dashboard that allows users to check the calculation process in real time. This ensures transparency in the calculation process, thereby gaining users' trust.

[0064] The value presentation unit can incorporate evaluations from third-party organizations into the presented value to ensure fairness. For example, the value presentation unit builds a system that incorporates evaluations from third-party organizations into the value calculated by the generation AI. For example, it reflects the opinions of experts and data from independent evaluation organizations. In addition, the value presentation unit ensures transparency of the evaluation process to ensure fairness. For example, the value presentation unit provides users with details of the evaluation process and makes public the weighting criteria. In this way, incorporating evaluations from third-party organizations improves the fairness of value presentation.

[0065] The value presentation unit can analyze the user's emotional response and develop a value presentation method that elicits positive emotions. For example, the value presentation unit uses a generation AI to analyze the user's emotional response and develop a value presentation method that elicits positive emotions. For example, if the emotional score is high, detailed information is provided, and if the emotional score is low, brief information is provided. The value presentation unit also analyzes the emotional response using an emotion estimation function and optimizes the value presentation method. For example, the value presentation unit provides positive feedback to the user based on the emotional score. This elicits positive emotions, thereby improving user satisfaction.

[0066] The value presentation unit can present personalized value that reflects the user's individual needs and preferences. For example, the value presentation unit uses a generation AI to analyze the user's individual needs and preferences and present personalized value based on the results. For example, the value presentation unit customizes the value based on the user's past behavioral data and search history. The value presentation unit also optimizes the value presentation based on user feedback. For example, the value presentation unit collects user feedback and improves the value presentation method based on that feedback. This makes it possible to present more appropriate value by reflecting the user's individual needs and preferences.

[0067] The value presentation unit enables value presentation on different devices and platforms, allowing users to check value information anywhere. For example, the value presentation unit develops a multi-platform compatible system to enable the generation AI to present value on different devices and platforms. For example, it optimizes display on smartphones, tablets, and PCs. The value presentation unit also uses a cloud-based system to enable users to check value information anywhere. For example, the value presentation unit stores data on the cloud and allows users to access it via the Internet. This enables value presentation on different devices and platforms, improving user convenience.

[0068] The value presentation unit can analyze the emotions of the user when receiving the value presentation in real time and provide the value information at the optimal timing. For example, the value presentation unit uses a generation AI to analyze the user's emotions in real time and provide the value information at the optimal timing. For example, the value presentation unit presents the value information when the user is relaxed. The value presentation unit also analyzes emotions using an emotion estimation function and optimizes the timing of providing the value information. For example, the value presentation unit provides the value information to the user at the optimal timing based on the emotion score. This improves user acceptance by providing the value information at the optimal timing.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The value calculation system may further include a prediction unit that analyzes a user's purchasing history and predicts future purchasing behavior based on past purchasing patterns. For example, the prediction unit may analyze the types and price ranges of products the user has purchased in the past and predict which products the user is likely to purchase next. The prediction unit may also analyze the user's purchasing frequency and seasonal purchasing trends and predict which products the user is likely to purchase at a particular time. Furthermore, the prediction unit may combine the user's purchasing history with market trends to more accurately predict future purchasing behavior. This allows users to predict their own purchasing behavior and make planned purchases.

[0071] The value calculation system may further include a health assessment unit that collects the user's health data and calculates the value of products and services based on the user's health status. For example, the health assessment unit may analyze data collected from the user's fitness tracker or smartwatch to suggest products and services suited to the user's health status. The health assessment unit may also analyze the user's food records and exercise history to calculate the value of products and services useful for maintaining health. Furthermore, the health assessment unit may combine the user's health data with market health trends to more accurately calculate the value of health-related products and services. This allows the user to select the most suitable products and services based on their health status.

[0072] The value calculation system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the weighting of the value calculation based on the estimated emotions. For example, the emotion adjustment unit performs emotion analysis on the user's reviews and ratings, and places emphasis on ratings where positive emotions are strong. The emotion adjustment unit can also adjust the value calculation algorithm based on the user's emotion data to reflect emotional value. Furthermore, the emotion adjustment unit can analyze fluctuations in the user's emotions in real time and reflect them in the value calculation results. This enables more accurate value calculation that takes the user's emotions into account.

[0073] The value calculation system can further include an influence evaluation unit that evaluates a user's social influence and calculates value based on that influence. For example, the influence evaluation unit analyzes the number of followers and engagement rate of the user on social media to evaluate social influence. The influence evaluation unit can also analyze the content of users' posts and the number of shares, and place emphasis on the evaluation of highly influential users. Furthermore, the influence evaluation unit can combine the user's social influence with market trends to improve the accuracy of value calculation. This makes it possible to calculate value taking social influence into account.

[0074] The value calculation system can further include an emotion optimization unit that estimates the user's emotions and optimizes the method of presenting value based on the estimated emotions. For example, the emotion optimization unit analyzes the user's emotions in real time and proposes a presentation format that elicits positive emotions. The emotion optimization unit can also adjust the content of the value presentation based on the user's emotion score to increase emotional satisfaction. Furthermore, the emotion optimization unit can monitor fluctuations in the user's emotions and provide value information at the optimal timing. This makes it possible to present optimal value that takes the user's emotions into consideration.

[0075] The value calculation system can further include a purchasing willingness adjustment unit that estimates the user's purchasing willingness and adjusts the content of the value offer based on the estimated purchasing willingness. For example, the purchasing willingness adjustment unit analyzes the user's past purchasing history and search history, and provides detailed information when the user's purchasing willingness is high. The purchasing willingness adjustment unit can also analyze the user's purchasing willingness in real time, and provide concise information when the user's purchasing willingness is low. Furthermore, the purchasing willingness adjustment unit can monitor fluctuations in the user's purchasing willingness and provide value information at the optimal timing. This makes it possible to offer optimal value that takes the user's purchasing willingness into consideration.

[0076] The value calculation system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the value calculation algorithm based on the estimated emotions. For example, the emotion adjustment unit performs emotion analysis on the user's reviews and ratings, and places emphasis on ratings when positive emotions are strong. The emotion adjustment unit can also adjust the value calculation algorithm based on the user's emotion data to reflect emotional value. Furthermore, the emotion adjustment unit can analyze fluctuations in the user's emotions in real time and reflect them in the value calculation results. This enables more accurate value calculation that takes the user's emotions into account.

[0077] The value calculation system can further include an emotion optimization unit that estimates the user's emotions and optimizes the method of presenting value based on the estimated emotions. For example, the emotion optimization unit analyzes the user's emotions in real time and proposes a presentation format that elicits positive emotions. The emotion optimization unit can also adjust the content of the value presentation based on the user's emotion score to increase emotional satisfaction. Furthermore, the emotion optimization unit can monitor fluctuations in the user's emotions and provide value information at the optimal timing. This makes it possible to present optimal value that takes the user's emotions into consideration.

[0078] The value calculation system can further include a purchasing willingness adjustment unit that estimates the user's purchasing willingness and adjusts the content of the value offer based on the estimated purchasing willingness. For example, the purchasing willingness adjustment unit analyzes the user's past purchasing history and search history, and provides detailed information when the user's purchasing willingness is high. The purchasing willingness adjustment unit can also analyze the user's purchasing willingness in real time, and provide concise information when the user's purchasing willingness is low. Furthermore, the purchasing willingness adjustment unit can monitor fluctuations in the user's purchasing willingness and provide value information at the optimal timing. This makes it possible to offer optimal value that takes the user's purchasing willingness into consideration.

[0079] The value calculation system may further include a prediction unit that analyzes a user's purchasing history and predicts future purchasing behavior based on past purchasing patterns. For example, the prediction unit may analyze the types and price ranges of products the user has purchased in the past and predict which products the user is likely to purchase next. The prediction unit may also analyze the user's purchasing frequency and seasonal purchasing trends and predict which products the user is likely to purchase at a particular time. Furthermore, the prediction unit may combine the user's purchasing history with market trends to more accurately predict future purchasing behavior. This allows users to predict their own purchasing behavior and make planned purchases.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The data collection unit collects target data. For example, the data collection unit collects the market price of the product, user reviews, product performance data, the number of service users, user satisfaction, service coverage area, geographical conditions of the location, transportation access, and the status of surrounding facilities. Specifically, the market price of the product is collected from online shopping sites, and user reviews are collected from social media. The number of service users is obtained from the service provider's database, and user satisfaction is collected from a questionnaire survey. The geographical conditions of the location are obtained from a map database, and transportation access is collected from a transportation database. Step 2: The value calculation unit calculates value based on the collected data. For example, the value calculation unit calculates the value of a product using regression analysis, the value of a service using clustering, and the value of point information using statistical methods. Specifically, the value of the product is calculated by analyzing the relationship between the market price of the product and user reviews using regression analysis. The value of the service is calculated by analyzing the relationship between the number of users of the service and satisfaction using clustering. The value of the point information is calculated by analyzing the relationship between the geographical conditions of the point and transportation access using statistical methods. Step 3: The value presentation unit presents the calculated value. For example, the value presentation unit presents the value of the product as a numerical value, the value of the service as a graph, and the value of the location information on a map. Specifically, the value of the product is displayed as a score from 0 to 100, the value of the service is displayed as a bar graph, and the value of the location information is displayed in different colors on the map.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 data collection unit that collects target data; a value calculation unit that calculates a value based on the data collected by the data collection unit; a value presentation unit that presents the value calculated by the value calculation unit. A system characterized by:

2. The data collection unit Evaluate the reliability of collected data in real time and automatically filter out unreliable data 2. The system of claim 1.

3. The data collection unit Collect and analyze unstructured social media or blog data 2. The system of claim 1.

4. The value calculation unit Combining different statistical methods to improve the accuracy of value calculations 2. The system of claim 1.

5. The value presentation unit To ensure transparency in presenting value, details of the calculation process are provided to users.

2. The system of claim 1.

6. The data collection unit Extracting sentiment from user reviews or ratings and using that sentiment data as part of value calculations 2. The system of claim 1.

7. The value calculation unit Incorporating user emotional data into statistical models to provide calculation results that reflect emotional value 2. The system of claim 1.

8. The value presentation unit Analyze users' emotional reactions and develop value presentation methods that elicit positive emotions 2. The system of claim 1.

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