System

Abstract

An object of a system according to an embodiment is to facilitate legal interpretation and data linkage and to utilize data in real time.SOLUTION: A system according to an embodiment includes a legal interpretation part, a data cooperation part, and a real time data utilization part. The legal interpreter gets a comprehensive agreement on the privacy policy. The data collaboration unit integrates the data into a common data infrastructure based on the consent obtained by the legal interpretation unit. The real-time data utilization unit utilizes the data integrated by the data linkage unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to legally interpret data and share data even between companies in the same group, making it difficult to utilize data in real time.

[0005] The system according to the embodiment aims to facilitate legal interpretation and data linkage, and to utilize data in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a legal interpretation unit, a data linking unit, and a real-time data utilization unit. The legal interpretation unit obtains comprehensive consent in a privacy policy. The data linking unit integrates data into a common data platform based on the consent obtained by the legal interpretation unit. The real-time data utilization unit utilizes the data integrated by the data linking unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment facilitates legal interpretation and data linkage, and enables data to be utilized in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The data utilization promotion system according to an embodiment of the present invention is a system that enables data that is the basis for AI development to be easily shared between companies within the same group. As a result, the data utilization promotion system solves problems of legal interpretation and data linkage, and by utilizing data in real time, it can promote the use of data that is the basis for AI development.

[0029] A data utilization promotion system according to an embodiment includes a legal interpretation unit, a data linking unit, and a real-time data utilization unit. The legal interpretation unit obtains comprehensive consent in a privacy policy. For example, when a user registers for a new service, the user agrees to the privacy policy, allowing the service to share the user's data with other related services. The data linking unit integrates data into a common data platform based on the consent obtained by the legal interpretation unit. For example, a user's purchase history and behavioral data are updated in real time, and targeted advertising and recommendation systems operate based on the updated data. The real-time data utilization unit utilizes the data integrated by the data linking unit in real time. For example, the moment a user visits a website, the behavioral data is analyzed in real time, and optimal advertisements and content are displayed. This allows the data utilization promotion system according to an embodiment to promote the utilization of data, which is the basis for AI development, and facilitate data utilization between companies within the same group.

[0030] The legal interpretation unit can use a generation AI to automatically analyze the content of the user's consent and generate an optimal consent statement that meets legal requirements. For example, the legal interpretation unit uses a generation AI to automatically analyze the wording used when the user consents and generate an optimal consent statement that meets legal requirements. For example, it analyzes the text entered by the user when consenting and automatically adds wording that meets the necessary legal requirements. The legal interpretation unit also uses a generation AI to analyze the wording used when the user consents and automatically generate a template that meets legal requirements. For example, it creates a template that complements legally required information based on the content entered by the user. The legal interpretation unit also uses a generation AI to analyze the wording used when the user consents in real time and proposes optimal wording that meets legal requirements. For example, it analyzes the content entered by the user and presents legally required information in real time. This automatically analyzes the content of the user's consent and generates a consent statement that meets legal requirements, thereby resolving the problem of legal interpretation.

[0031] The legal interpretation unit analyzes the user's past consent history and understands consent trends, thereby enabling more efficient consent acquisition in the future. For example, the legal interpretation unit analyzes the user's past consent history using generation AI to understand consent trends. For example, it analyzes the content and frequency of past consent and uses this as a reference when obtaining consent in the future. The legal interpretation unit also uses generation AI to build a system that analyzes the user's past consent history and understands consent trends. For example, it proposes an efficient method for obtaining consent in the future based on the content of past consent. The legal interpretation unit also analyzes the user's past consent history and understands consent trends, thereby enabling more efficient consent acquisition in the future. For example, it automatically presents information necessary for consent acquisition based on the past consent history. In this way, by analyzing the user's past consent history and understanding consent trends, it is possible to improve the efficiency of consent acquisition in the future.

[0032] The data integration unit uses generation AI to automatically convert differences in data formats that occur during data integration, thereby achieving seamless data integration. The data integration unit, for example, uses generation AI to build a system that automatically converts differences in data formats that occur during data integration. For example, it converts different data formats into a unified format. The data integration unit also uses generation AI to automatically convert differences in data formats that occur during data integration, thereby achieving seamless data integration. For example, it converts data between different databases into a unified format. The data integration unit also uses generation AI to develop a system that automatically converts differences in data formats that occur during data integration. For example, it converts different data formats in real time, thereby achieving seamless data integration. This makes it possible to automatically convert differences in data formats that occur during data integration, thereby achieving seamless data integration.

[0033] The data integration unit can analyze the data integration history and propose the optimal method for improving the success rate and speed of integration. For example, the data integration unit builds a system that analyzes the data integration history using generation AI and proposes the optimal method for improving the success rate and speed of integration. For example, it proposes the optimal method based on past data integration history. The data integration unit also uses generation AI to analyze the data integration history and proposes the optimal method for improving the success rate and speed of integration. For example, it proposes the optimal method based on past success cases. The data integration unit also develops a system that analyzes the data integration history and proposes the optimal method for improving the success rate and speed of integration. For example, it proposes the optimal method in real time based on past data integration history. This makes it possible to analyze the data integration history and propose the optimal method for improving the success rate and speed of integration.

[0034] The real-time data utilization unit can use the generative AI to automatically update the machine learning model based on real-time data, thereby improving accuracy. The real-time data utilization unit, for example, uses the generative AI to build a system that automatically updates the machine learning model based on real-time data. For example, the machine learning model is automatically retrained based on data collected in real time. The real-time data utilization unit also analyzes the real-time data with the generative AI and automatically updates the machine learning model. For example, the model parameters are adjusted based on the data collected in real time. The real-time data utilization unit also uses the generative AI to develop a system that automatically updates the machine learning model based on real-time data. For example, the accuracy of the model is improved based on the data collected in real time. This makes it possible to automatically update the machine learning model based on real-time data, thereby improving accuracy.

[0035] The real-time data utilization unit can develop an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data. The real-time data utilization unit, for example, develops an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data. For example, an advertising campaign is adjusted based on data collected in real time. The real-time data utilization unit also uses generative AI to build a system that instantly adjusts a marketing strategy based on the analysis results of real-time data. For example, targeted advertising is optimized based on data collected in real time. The real-time data utilization unit also develops an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data. For example, a promotion strategy is adjusted based on data collected in real time. This makes it possible to develop an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data.

[0036] The Real-Time Data Utilization Department can explore applicability in different industries and applications based on real-time data. For example, the Real-Time Data Utilization Department analyzes real-time data using generative AI and builds a system that explores applicability in different industries and applications. For example, it discovers new business opportunities based on data collected in real time. The Real-Time Data Utilization Department also uses generative AI to explore applicability in different industries and applications based on real-time data. For example, it identifies new market needs based on data collected in real time. The Real-Time Data Utilization Department also develops a system that explores applicability in different industries and applications based on real-time data. For example, it proposes new products and services based on data collected in real time. This makes it possible to explore applicability in different industries and applications based on real-time data.

[0037] The real-time data utilization unit performs predictive analysis by utilizing real-time data, and is able to predict future trends. The real-time data utilization unit, for example, uses a generation AI to perform predictive analysis by utilizing real-time data, and builds a system that predicts future trends. For example, future market trends are predicted based on data collected in real time. The real-time data utilization unit also analyzes the real-time data with a generation AI, performs predictive analysis, and predicts future trends. For example, future consumer behavior is predicted based on data collected in real time. The real-time data utilization unit also uses a generation AI to perform predictive analysis by utilizing real-time data, and develops a system that predicts future trends. For example, future technology trends are predicted based on data collected in real time. This makes it possible to perform predictive analysis by utilizing real-time data, and predict future trends.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The data utilization promotion system may further include a data anonymization unit. The data anonymization unit performs a process of anonymizing data to protect the user's privacy. For example, the data is converted so that the user's personal information cannot be identified. The data anonymization unit also works with the data linkage unit to integrate the anonymized data into a common data platform. This makes it possible to promote data utilization while protecting the user's privacy. Furthermore, the data anonymization unit works with the real-time data utilization unit to analyze the anonymized data in real time and provide optimal advertisements and content. This makes it possible to maximize data utilization while protecting the user's privacy.

[0040] The data utilization promotion system can further include a data quality evaluation unit. The data quality evaluation unit evaluates the quality of the data and performs processing to utilize only highly reliable data. For example, it detects and corrects missing data and outliers. The data quality evaluation unit also works with the data integration unit to integrate the quality-evaluated data into a common data platform. This allows for the utilization of highly reliable data. Furthermore, the data quality evaluation unit works with the real-time data utilization unit to analyze the quality-evaluated data in real time and provide optimal advertisements and content. This allows for the maximization of data utilization while ensuring data quality.

[0041] The data utilization promotion system can further include a data visualization unit. The data visualization unit performs processing to visually display data. For example, it displays data trends and patterns in graphs and charts. The data visualization unit also works with the data integration unit to visually display integrated data. This makes it easier to understand the data and supports decision-making. Furthermore, the data visualization unit works with the real-time data utilization unit to visually display data collected in real time, allowing optimal advertisements and content to be provided. This makes it possible to maximize data utilization through data visualization.

[0042] The data utilization promotion system can further include a data security unit. The data security unit performs processing to ensure data security. For example, it performs data encryption and access control. The data security unit also works with the data integration unit to integrate secure data into a common data platform. This makes it possible to promote data utilization while ensuring data security. Furthermore, the data security unit works with the real-time data utilization unit to analyze secure data in real time and provide optimal advertisements and content. This makes it possible to maximize data utilization while ensuring data security.

[0043] The data utilization promotion system can further include a data backup unit. The data backup unit performs a process of backing up data. For example, it periodically creates backups of data to prevent data loss. The data backup unit also works with the data integration unit to integrate the backed up data into a common data platform. This makes it possible to promote data utilization while preventing data loss. Furthermore, the data backup unit works with the real-time data utilization unit to analyze the backed up data in real time and provide optimal advertisements and content. This makes it possible to maximize data utilization through data backup.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The legal interpretation department obtains blanket consent in a privacy policy. For example, when a user signs up for a new service, they consent to the privacy policy, allowing that service to share their data with other related services. Step 2: The data integration unit integrates the data into a common data platform based on the consent obtained by the legal interpretation unit. For example, user purchase history and behavioral data are updated in real time, and targeted advertising and recommendation systems operate based on this. Step 3: The real-time data utilization unit utilizes the data integrated by the data integration unit in real time. For example, the moment a user visits a website, their behavioral data is analyzed in real time and the most appropriate advertisements and content are displayed.

[0046] (Example 2) The data utilization promotion system according to an embodiment of the present invention is a system that enables data that is the basis for AI development to be easily shared between companies within the same group. As a result, the data utilization promotion system solves problems of legal interpretation and data linkage, and by utilizing data in real time, it can promote the use of data that is the basis for AI development.

[0047] A data utilization promotion system according to an embodiment includes a legal interpretation unit, a data linking unit, and a real-time data utilization unit. The legal interpretation unit obtains comprehensive consent in a privacy policy. For example, when a user registers for a new service, the user agrees to the privacy policy, allowing the service to share the user's data with other related services. The data linking unit integrates data into a common data platform based on the consent obtained by the legal interpretation unit. For example, a user's purchase history and behavioral data are updated in real time, and targeted advertising and recommendation systems operate based on the updated data. The real-time data utilization unit utilizes the data integrated by the data linking unit in real time. For example, the moment a user visits a website, the behavioral data is analyzed in real time, and optimal advertisements and content are displayed. This allows the data utilization promotion system according to an embodiment to promote the utilization of data, which is the basis for AI development, and facilitate data utilization between companies within the same group.

[0048] The legal interpretation unit can use a generation AI to automatically analyze the content of the user's consent and generate an optimal consent statement that meets legal requirements. For example, the legal interpretation unit uses a generation AI to automatically analyze the wording used when the user consents and generate an optimal consent statement that meets legal requirements. For example, it analyzes the text entered by the user when consenting and automatically adds wording that meets the necessary legal requirements. The legal interpretation unit also uses a generation AI to analyze the wording used when the user consents and automatically generate a template that meets legal requirements. For example, it creates a template that complements legally required information based on the content entered by the user. The legal interpretation unit also uses a generation AI to analyze the wording used when the user consents in real time and proposes optimal wording that meets legal requirements. For example, it analyzes the content entered by the user and presents legally required information in real time. This automatically analyzes the content of the user's consent and generates a consent statement that meets legal requirements, thereby resolving the problem of legal interpretation.

[0049] The legal interpretation unit analyzes the user's past consent history and understands consent trends, thereby enabling more efficient consent acquisition in the future. For example, the legal interpretation unit analyzes the user's past consent history using generation AI to understand consent trends. For example, it analyzes the content and frequency of past consent and uses this as a reference when obtaining consent in the future. The legal interpretation unit also uses generation AI to build a system that analyzes the user's past consent history and understands consent trends. For example, it proposes an efficient method for obtaining consent in the future based on the content of past consent. The legal interpretation unit also analyzes the user's past consent history and understands consent trends, thereby enabling more efficient consent acquisition in the future. For example, it automatically presents information necessary for consent acquisition based on the past consent history. In this way, by analyzing the user's past consent history and understanding consent trends, it is possible to improve the efficiency of consent acquisition in the future.

[0050] The legal interpretation unit can use the emotion estimation function to detect any anxiety or doubt a user may have during the consent process in real time and provide corresponding explanations or supplemental information. For example, the legal interpretation unit uses the emotion estimation function to build a system that detects any anxiety or doubt a user may have during the consent process in real time. For example, it analyzes the user's facial expressions and voice to detect anxiety or doubt. The legal interpretation unit also uses the emotion estimation function to detect any anxiety or doubt a user may have during the consent process and provide corresponding explanations or supplemental information. For example, it automatically displays additional explanations when the user feels anxious. The legal interpretation unit also uses the emotion estimation function to develop a system that detects any anxiety or doubt a user may have during the consent process in real time and provides corresponding supplemental information. For example, it automatically presents a detailed explanation when the user feels doubt. This makes it possible to detect any anxiety or doubt a user may have during the consent process in real time and provide corresponding explanations or supplemental information, thereby improving the reliability of the consent process.

[0051] The data integration unit uses generation AI to automatically convert differences in data formats that occur during data integration, thereby achieving seamless data integration. The data integration unit, for example, uses generation AI to build a system that automatically converts differences in data formats that occur during data integration. For example, it converts different data formats into a unified format. The data integration unit also uses generation AI to automatically convert differences in data formats that occur during data integration, thereby achieving seamless data integration. For example, it converts data between different databases into a unified format. The data integration unit also uses generation AI to develop a system that automatically converts differences in data formats that occur during data integration. For example, it converts different data formats in real time, thereby achieving seamless data integration. This makes it possible to automatically convert differences in data formats that occur during data integration, thereby achieving seamless data integration.

[0052] The data integration unit can analyze the data integration history and propose the optimal method for improving the success rate and speed of integration. For example, the data integration unit builds a system that analyzes the data integration history using generation AI and proposes the optimal method for improving the success rate and speed of integration. For example, it proposes the optimal method based on past data integration history. The data integration unit also uses generation AI to analyze the data integration history and proposes the optimal method for improving the success rate and speed of integration. For example, it proposes the optimal method based on past success cases. The data integration unit also develops a system that analyzes the data integration history and proposes the optimal method for improving the success rate and speed of integration. For example, it proposes the optimal method in real time based on past data integration history. This makes it possible to analyze the data integration history and propose the optimal method for improving the success rate and speed of integration.

[0053] The data integration unit can use the emotion estimation function to provide a support function for reducing stress and burden on personnel involved in data integration. The data integration unit, for example, uses the emotion estimation function to build a system that provides a support function for reducing stress and burden on personnel involved in data integration. For example, the emotional state of the personnel is analyzed and appropriate support is provided. The data integration unit also uses the emotion estimation function to provide a support function for reducing stress and burden on personnel involved in data integration. For example, appropriate advice and support is provided based on the emotional state of the personnel. The data integration unit also uses the emotion estimation function to develop a system that provides a support function for reducing stress and burden on personnel involved in data integration. For example, the emotional state of the personnel is analyzed in real time and appropriate support is provided. This makes it possible to provide a support function for reducing stress and burden on personnel involved in data integration.

[0054] The real-time data utilization unit can use the generative AI to automatically update the machine learning model based on real-time data, thereby improving accuracy. The real-time data utilization unit, for example, uses the generative AI to build a system that automatically updates the machine learning model based on real-time data. For example, the machine learning model is automatically retrained based on data collected in real time. The real-time data utilization unit also analyzes the real-time data with the generative AI and automatically updates the machine learning model. For example, the model parameters are adjusted based on the data collected in real time. The real-time data utilization unit also uses the generative AI to develop a system that automatically updates the machine learning model based on real-time data. For example, the accuracy of the model is improved based on the data collected in real time. This makes it possible to automatically update the machine learning model based on real-time data, thereby improving accuracy.

[0055] The real-time data utilization unit can develop an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data. The real-time data utilization unit, for example, develops an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data. For example, an advertising campaign is adjusted based on data collected in real time. The real-time data utilization unit also uses generative AI to build a system that instantly adjusts a marketing strategy based on the analysis results of real-time data. For example, targeted advertising is optimized based on data collected in real time. The real-time data utilization unit also develops an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data. For example, a promotion strategy is adjusted based on data collected in real time. This makes it possible to develop an algorithm that instantly adjusts a marketing strategy based on the analysis results of real-time data.

[0056] The real-time data utilization unit can use the emotion estimation function to estimate emotions from the user's real-time behavioral data and provide optimal content based on that. The real-time data utilization unit, for example, uses the emotion estimation function to estimate emotions from the user's real-time behavioral data and builds a system that provides optimal content based on that. For example, it analyzes the user's behavioral data and displays content according to the emotions. The real-time data utilization unit also analyzes the user's real-time behavioral data with the emotion estimation function and provides optimal content based on the emotions. For example, it displays advertisements based on the user's behavioral data. The real-time data utilization unit also uses the emotion estimation function to develop a system that estimates emotions from the user's real-time behavioral data and provides optimal content based on that. For example, it analyzes the user's behavioral data in real time and provides content based on the emotions. This makes it possible to estimate emotions from the user's real-time behavioral data and provide optimal content based on that.

[0057] The Real-Time Data Utilization Department can explore applicability in different industries and applications based on real-time data. For example, the Real-Time Data Utilization Department analyzes real-time data using generative AI and builds a system that explores applicability in different industries and applications. For example, it discovers new business opportunities based on data collected in real time. The Real-Time Data Utilization Department also uses generative AI to explore applicability in different industries and applications based on real-time data. For example, it identifies new market needs based on data collected in real time. The Real-Time Data Utilization Department also develops a system that explores applicability in different industries and applications based on real-time data. For example, it proposes new products and services based on data collected in real time. This makes it possible to explore applicability in different industries and applications based on real-time data.

[0058] The real-time data utilization unit performs predictive analysis by utilizing real-time data, and is able to predict future trends. The real-time data utilization unit, for example, uses a generation AI to perform predictive analysis by utilizing real-time data, and builds a system that predicts future trends. For example, future market trends are predicted based on data collected in real time. The real-time data utilization unit also analyzes the real-time data with a generation AI, performs predictive analysis, and predicts future trends. For example, future consumer behavior is predicted based on data collected in real time. The real-time data utilization unit also uses a generation AI to perform predictive analysis by utilizing real-time data, and develops a system that predicts future trends. For example, future technology trends are predicted based on data collected in real time. This makes it possible to perform predictive analysis by utilizing real-time data, and predict future trends.

[0059] The real-time data utilization unit uses the emotion estimation function to analyze the user's emotional reactions based on the real-time data, and can use the results to improve the service. The real-time data utilization unit, for example, uses the emotion estimation function to analyze the user's emotional reactions based on the real-time data, and builds a system that is useful for improving the service. For example, based on the user's emotional reactions, identifies areas for improvement of the service. The real-time data utilization unit also analyzes the real-time data with the emotion estimation function, and uses the results to improve the service based on the user's emotional reactions. For example, the content of the service is adjusted based on the user's emotional reactions. The real-time data utilization unit also uses the emotion estimation function to analyze the user's emotional reactions based on the real-time data, and develops a system that is useful for improving the service. For example, the user's emotional reactions are analyzed in real time, and areas for improvement of the service are identified. In this way, the user's emotional reactions based on the real-time data can be analyzed, and used to improve the service.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The data utilization promotion system may further include a data anonymization unit. The data anonymization unit performs a process of anonymizing data to protect the user's privacy. For example, the data is converted so that the user's personal information cannot be identified. The data anonymization unit also works with the data linkage unit to integrate the anonymized data into a common data platform. This makes it possible to promote data utilization while protecting the user's privacy. Furthermore, the data anonymization unit works with the real-time data utilization unit to analyze the anonymized data in real time and provide optimal advertisements and content. This makes it possible to maximize data utilization while protecting the user's privacy.

[0062] The data utilization promotion system can further include a data quality evaluation unit. The data quality evaluation unit evaluates the quality of the data and performs processing to utilize only highly reliable data. For example, it detects and corrects missing data and outliers. The data quality evaluation unit also works with the data integration unit to integrate the quality-evaluated data into a common data platform. This allows for the utilization of highly reliable data. Furthermore, the data quality evaluation unit works with the real-time data utilization unit to analyze the quality-evaluated data in real time and provide optimal advertisements and content. This allows for the maximization of data utilization while ensuring data quality.

[0063] The data utilization promotion system can further include a data visualization unit. The data visualization unit performs processing to visually display data. For example, it displays data trends and patterns in graphs and charts. The data visualization unit also works with the data integration unit to visually display integrated data. This makes it easier to understand the data and supports decision-making. Furthermore, the data visualization unit works with the real-time data utilization unit to visually display data collected in real time, allowing optimal advertisements and content to be provided. This makes it possible to maximize data utilization through data visualization.

[0064] The data utilization promotion system can further include a data security unit. The data security unit performs processing to ensure data security. For example, it performs data encryption and access control. The data security unit also works with the data integration unit to integrate secure data into a common data platform. This makes it possible to promote data utilization while ensuring data security. Furthermore, the data security unit works with the real-time data utilization unit to analyze secure data in real time and provide optimal advertisements and content. This makes it possible to maximize data utilization while ensuring data security.

[0065] The data utilization promotion system can further include a data backup unit. The data backup unit performs a process of backing up data. For example, it periodically creates backups of data to prevent data loss. The data backup unit also works with the data integration unit to integrate the backed up data into a common data platform. This makes it possible to promote data utilization while preventing data loss. Furthermore, the data backup unit works with the real-time data utilization unit to analyze the backed up data in real time and provide optimal advertisements and content. This makes it possible to maximize data utilization through data backup.

[0066] The data utilization promotion system can further utilize data based on the user's emotions using an emotion estimation function. For example, the system can estimate the user's emotions and provide data according to the emotions. For example, if the user is feeling stressed, it can provide relaxing content. The emotion estimation function can also be used to provide advertisements and content based on the user's emotions. For example, if the user is excited, it can provide highly entertaining content. In this way, by utilizing data based on the user's emotions, it is possible to improve the user experience.

[0067] The data utilization promotion system can further use an emotion estimation function to provide feedback based on the user's emotions. For example, the system can estimate the user's emotions and provide feedback according to the emotions. For example, if the user is feeling anxious, it can provide feedback that gives a sense of security. The emotion estimation function can also be used to provide advice based on the user's emotions. For example, if the user is unsure, it can provide specific advice. In this way, by providing feedback based on the user's emotions, it is possible to improve the user experience.

[0068] The data utilization promotion system can further use an emotion estimation function to perform customization based on the user's emotions. For example, the system can estimate the user's emotions and perform customization according to the emotions. For example, if the user is tired, it can provide a simple interface. The emotion estimation function can also be used to automatically adjust settings based on the user's emotions. For example, if the user is concentrating, it can set the system to reduce notifications. In this way, customization based on the user's emotions can improve the user experience.

[0069] The data utilization promotion system can further use an emotion estimation function to provide support based on the user's emotions. For example, it can estimate the user's emotions and provide support according to those emotions. For example, if the user is in trouble, it can provide detailed help. The emotion estimation function can also be used to provide guidance based on the user's emotions. For example, if the user is performing an operation for the first time, it can provide a step-by-step guide. In this way, by providing support based on the user's emotions, it is possible to improve the user experience.

[0070] The data utilization promotion system can further use an emotion estimation function to perform personalization based on the user's emotions. For example, the system can estimate the user's emotions and perform personalization according to the emotions. For example, if the user is happy, positive content can be provided. The emotion estimation function can also be used to provide recommendations based on the user's emotions. For example, if the user is interested, related content can be provided. In this way, personalization based on the user's emotions can improve the user experience.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The legal interpretation department obtains blanket consent in a privacy policy. For example, when a user signs up for a new service, they consent to the privacy policy, allowing that service to share their data with other related services. Step 2: The data integration unit integrates the data into a common data platform based on the consent obtained by the legal interpretation unit. For example, user purchase history and behavioral data are updated in real time, and targeted advertising and recommendation systems operate based on this. Step 3: The real-time data utilization unit utilizes the data integrated by the data integration unit in real time. For example, the moment a user visits a website, their behavioral data is analyzed in real time and the most appropriate advertisements and content are displayed.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. Legal interpretation department to obtain comprehensive consent in the privacy policy, a data integration unit that integrates data into a common data platform based on the consent obtained by the legal interpretation unit; a real-time data utilization unit that utilizes the data integrated by the data linking unit in real time. A system characterized by:

2. The legal interpretation department: Generative AI is used to automatically analyze the user's consent and generate the optimal consent text to meet legal requirements.

2. The system of claim 1.

3. The legal interpretation department: Analyze users' past consent history, understand trends in consent, and improve the efficiency of obtaining consent in the future.

2. The system of claim 1.

4. The legal interpretation department: Detect any concerns or doubts users may have during the consent process in real time and provide appropriate explanations or supplementary information 2. The system of claim 1.

5. The data linking unit Using generative AI, we automatically convert differences in data formats that arise when linking data, achieving seamless data integration.

2. The system of claim 1.

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