system

Abstract

An object of a system according to an embodiment is to provide a unified rule in data collection for the LLM and to improve efficiency of data management and point assignment.SOLUTION: A system includes an LLM association, a common ID management part, a tag embedding part, and a point management part. An LLM association consists of companies that hold LLMs. The common ID management unit manages a common ID. The tag embedding unit embeds a [#LLM _ OK] tag in the content. The point management unit manages points.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, there were no unified content rules for collecting data for LLM, making data management and point allocation cumbersome.

[0005] The system according to the embodiment aims to provide unified rules for data collection for LLM and to improve the efficiency of data management and point allocation. [Means for solving the problem]

[0006] The system according to the embodiment includes an LLM association, a common ID management unit, a tag embedding unit, and a point management unit. The LLM association is made up of companies that hold LLMs. The common ID management unit manages common IDs. The tag embedding unit embeds a [#LLM_OK] tag in content. The point management unit manages points. [Effects of the Invention]

[0007] The system according to the embodiment provides unified rules for data collection for LLM, and can improve the efficiency of data management and point allocation. [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 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) A data collection system according to an embodiment of the present invention is a system that establishes content rules for data collection for LLM, manages common IDs, and awards points. This allows the data collection system to efficiently collect data that can be used for LLM learning and to provide appropriate rewards to content providers.

[0029] A data collection system according to an embodiment includes an LLM association, a shared ID management unit, a tag embedding unit, and a point management unit. The LLM association was established through cooperation between companies that hold LLMs and manages shared IDs. The shared ID management unit manages shared IDs that are uniquely assigned to content provided by each company. For example, the shared ID management unit generates a unique ID for each piece of content and stores the ID in a database. The tag embedding unit embeds a [#LLM_OK] tag in the content. For example, the tag embedding unit provides a tool that automatically inserts the [#LLM_OK] tag into the HTML code of a webpage. The tag embedding unit can also embed tags in image data and video data. The point management unit manages points and assigns them to content providers. For example, the point management unit assigns points to content with registered shared IDs and records the points in a database. The point management unit also provides a system for exchanging points for other points in conjunction with PayPay IDs, etc. As a result, the data collection system according to the embodiment can efficiently collect data that can be used for learning LLM and provide appropriate rewards to content providers. For example, content providers can exchange points for PayPay points or other electronic money to use for everyday shopping. Points can also be exchanged for other electronic money or gift cards.

[0030] The common ID management unit tracks the content usage history and can record in detail which LLMs used which content for their studies. For example, the common ID management unit builds a system that records in detail which LLMs used each piece of content for their studies, based on the common ID assigned to that content. For example, the common ID management unit stores the content usage history in a database so that it can be referenced later. This makes it possible to manage the learning history of LLMs by recording the content usage history in detail.

[0031] The common ID management unit can provide a dashboard that allows content providers to monitor the usage status of their content in real time. For example, the common ID management unit uses a common ID to develop a dashboard that allows content providers to monitor the usage status of their content in real time. For example, the common ID management unit provides a dashboard that displays the number of uses and usage time in graphs. This allows content providers to understand the usage status of their content in real time.

[0032] The common ID management unit can promote data sharing between different LLMs and build mutually complementary datasets. The common ID management unit, for example, uses a common ID to build a system that promotes data sharing between different LLMs. For example, the common ID management unit integrates datasets based on the common ID and creates mutually complementary datasets. This promotes data sharing between different LLMs and builds mutually complementary datasets.

[0033] The common ID management unit introduces a content rating system using common IDs and can award bonus points to highly rated content. The common ID management unit, for example, introduces a content rating system using common IDs and develops a system that awards bonus points to highly rated content. For example, the common ID management unit calculates points based on user ratings and frequency of use. This makes it possible to award bonus points to highly rated content.

[0034] The tag embedding unit develops a tool that automates tag embedding, allowing content providers to easily add tags. The tag embedding unit develops an automated tool that allows content providers to easily add, for example, the [#LLM_OK] tag. For example, the tag embedding unit provides a function to automatically insert tags into the HTML code of a web page, thereby allowing content providers to easily add tags.

[0035] The tag embedding unit can analyze the usage of content with embedded tags and identify which format of content most effectively contributes to LLM learning. For example, the tag embedding unit develops a system that analyzes the usage of content with embedded tags and identifies which format of content most effectively contributes to LLM learning. For example, the tag embedding unit evaluates the effectiveness of each format, such as text, image, and video. This makes it possible to identify which format of content most effectively contributes to LLM learning.

[0036] To promote tag embedding, the tag embedding unit can provide content providers with guidelines or tutorials that explain how to embed tags. For example, the tag embedding unit provides content providers with guidelines that explain how to embed the [#LLM_OK] tag. For example, the tag embedding unit creates a document that describes specific steps and points to note. This makes it easier for content providers to understand how to embed tags.

[0037] The tag embedding unit can automatically classify content with embedded tags and build a dataset that is optimal for LLM learning. For example, the tag embedding unit can classify content with embedded tags and build a dataset that is optimal for LLM learning. For example, the tag embedding unit can classify content by content type or theme. This allows for the construction of a dataset that is optimal for LLM learning.

[0038] The point management unit can develop an algorithm in a point system that dynamically adjusts the point award rate according to the quality and frequency of use of content. The point management unit, for example, develops an algorithm in a point system that dynamically adjusts the point award rate according to the quality and frequency of use of content. For example, the point management unit awards more points to content that is used more frequently. This makes it possible to dynamically adjust the point award rate according to the quality and frequency of use of content.

[0039] The point management unit can track the point usage history in detail and analyze which content provider is using the points and how they are using them. The point management unit, for example, develops a system that tracks the point usage history in detail and analyzes which content provider is using the points and how they are using them. For example, the point management unit stores the usage history in a database so that it can be referenced later. This makes it possible to track the point usage history in detail and analyze the usage trends of content providers.

[0040] The point management unit can link the point system with other reward programs, allowing content providers to select from a variety of rewards. For example, the point management unit develops a system that links the point system with other reward programs, allowing content providers to select from a variety of rewards. For example, the point management unit makes it possible to exchange points for electronic money or gift cards. This allows content providers to select from a variety of rewards.

[0041] The point management unit can use a point system to promote competition among content providers and provide a special reward to the provider who has acquired the most points. The point management unit develops a system that uses, for example, a point system to promote competition among content providers. For example, the point management unit provides a special reward to the provider who has acquired the most points. This promotes competition among content providers and provides a special reward to the provider who has acquired the most points.

[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 data collection system may further include a diversity evaluation unit that evaluates the diversity of content. The diversity evaluation unit may evaluate, for example, the diversity of content genres and themes, and award additional points to content providers who provide diverse content. This may improve the diversity of datasets used for LLM studies. For example, the diversity evaluation unit may award additional points to providers who provide content in different genres, such as news articles, essays, and technical documents. The diversity evaluation unit may also evaluate content from different languages ​​and cultural backgrounds, promoting the creation of international datasets.

[0044] The data collection system may further include a reliability evaluation unit that evaluates the reliability of content. For example, the reliability evaluation unit may verify the source and citation of the content and award additional points to highly reliable content. This may improve the quality of data used in LLM learning. For example, by providing content from highly reliable sources such as academic papers or government reports, the reliability evaluation unit may award additional points to the content provider. The reliability evaluation unit may also evaluate the frequency and recency of content updates and award additional points to content providers that provide the most up-to-date information.

[0045] The data collection system may further include an interactivity evaluation unit that evaluates the interactivity of the content. The interactivity evaluation unit, for example, evaluates the degree to which users interact with the content and awards additional points to interactive content. This can improve the quality of data used for LLM learning. For example, by providing content that encourages user participation, such as quiz-style content or articles that allow users to leave comments, the interactivity evaluation unit can award additional points to the provider. The interactivity evaluation unit can also collect user feedback and use it to improve the content.

[0046] The data collection system may further include an educational evaluation unit that evaluates the educational value of content. The educational evaluation unit may, for example, develop a system that awards additional points for educational content. This may improve the educational value of data used in LLM studies. For example, the educational evaluation unit may award additional points to providers of educational content such as science or history articles, educational videos, or online courses. The educational evaluation unit may also evaluate the quality of the educational content and award additional points to content providers that provide high-quality educational content.

[0047] The data collection system can further include an entertainment evaluation unit that evaluates the entertainment value of content. For example, the entertainment evaluation unit can develop a system that awards additional points to highly entertaining content. This can improve the entertainment value of data used in LLM learning. For example, by providing highly entertaining content such as movie reviews, game walkthroughs, and music videos, the entertainment evaluation unit can award additional points to the provider. The entertainment evaluation unit can also analyze user responses and award additional points to particularly popular entertainment content.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The LLM Association is established through cooperation between companies that hold LLMs and manages common IDs. The common ID management unit manages common IDs that are uniquely assigned to content provided by each company. For example, the common ID management unit generates a unique ID for each piece of content and stores that ID in a database. Step 2: The tag embedding unit embeds the [#LLM_OK] tag into the content. For example, the tag embedding unit provides a tool that automatically inserts the [#LLM_OK] tag into the HTML code of a web page. The tag embedding unit can also embed tags in image data and video data in a similar manner. Step 3: The point management unit manages points and grants them to content providers. For example, the point management unit grants points for content with registered common IDs and records the points in a database. The point management unit also provides a system for exchanging points for other points in conjunction with PayPay IDs, etc. This allows content providers to exchange points for PayPay points or other electronic money and use them for everyday shopping. Points can also be exchanged for other electronic money or gift cards.

[0050] (Example 2) A data collection system according to an embodiment of the present invention is a system that establishes content rules for data collection for LLM, manages common IDs, and awards points. This allows the data collection system to efficiently collect data that can be used for LLM learning and to provide appropriate rewards to content providers.

[0051] A data collection system according to an embodiment includes an LLM association, a shared ID management unit, a tag embedding unit, and a point management unit. The LLM association was established through cooperation between companies that hold LLMs and manages shared IDs. The shared ID management unit manages shared IDs that are uniquely assigned to content provided by each company. For example, the shared ID management unit generates a unique ID for each piece of content and stores the ID in a database. The tag embedding unit embeds a [#LLM_OK] tag in the content. For example, the tag embedding unit provides a tool that automatically inserts the [#LLM_OK] tag into the HTML code of a webpage. The tag embedding unit can also embed tags in image data and video data. The point management unit manages points and assigns them to content providers. For example, the point management unit assigns points to content with registered shared IDs and records the points in a database. The point management unit also provides a system for exchanging points for other points in conjunction with PayPay IDs, etc. As a result, the data collection system according to the embodiment can efficiently collect data that can be used for learning LLM and provide appropriate rewards to content providers. For example, content providers can exchange points for PayPay points or other electronic money to use for everyday shopping. Points can also be exchanged for other electronic money or gift cards.

[0052] The common ID management unit tracks the content usage history and can record in detail which LLMs used which content for their studies. For example, the common ID management unit builds a system that records in detail which LLMs used each piece of content for their studies, based on the common ID assigned to that content. For example, the common ID management unit stores the content usage history in a database so that it can be referenced later. This makes it possible to manage the learning history of LLMs by recording the content usage history in detail.

[0053] The common ID management unit can provide a dashboard that allows content providers to monitor the usage status of their content in real time. For example, the common ID management unit uses a common ID to develop a dashboard that allows content providers to monitor the usage status of their content in real time. For example, the common ID management unit provides a dashboard that displays the number of uses and usage time in graphs. This allows content providers to understand the usage status of their content in real time.

[0054] The common ID management unit can use the emotion estimation function to analyze users' emotional reactions to content and award additional points to content that has a large number of positive reactions. The common ID management unit, for example, develops a system that uses the emotion estimation function to analyze users' emotional reactions to content related to a common ID in real time. For example, the common ID management unit awards additional points to content that has a large number of positive emotional reactions. This makes it possible to analyze users' emotional reactions and award additional points to content that has a large number of positive reactions.

[0055] The common ID management unit can promote data sharing between different LLMs and build mutually complementary datasets. The common ID management unit, for example, uses a common ID to build a system that promotes data sharing between different LLMs. For example, the common ID management unit integrates datasets based on the common ID and creates mutually complementary datasets. This promotes data sharing between different LLMs and builds mutually complementary datasets.

[0056] The common ID management unit introduces a content rating system using common IDs and can award bonus points to highly rated content. The common ID management unit, for example, introduces a content rating system using common IDs and develops a system that awards bonus points to highly rated content. For example, the common ID management unit calculates points based on user ratings and frequency of use. This makes it possible to award bonus points to highly rated content.

[0057] The common ID management unit can use the emotion estimation function to evaluate the emotional value of content and provide special rewards for content that is likely to resonate emotionally. The common ID management unit, for example, develops a system that uses the emotion estimation function to evaluate the emotional value of content associated with a common ID. For example, the common ID management unit provides special rewards for content with a high emotion score. This makes it possible to provide special rewards for content that is likely to resonate emotionally.

[0058] The tag embedding unit develops a tool that automates tag embedding, allowing content providers to easily add tags. The tag embedding unit develops an automated tool that allows content providers to easily add, for example, the [#LLM_OK] tag. For example, the tag embedding unit provides a function to automatically insert tags into the HTML code of a web page, thereby allowing content providers to easily add tags.

[0059] The tag embedding unit can analyze the usage of content with embedded tags and identify which format of content most effectively contributes to LLM learning. For example, the tag embedding unit develops a system that analyzes the usage of content with embedded tags and identifies which format of content most effectively contributes to LLM learning. For example, the tag embedding unit evaluates the effectiveness of each format, such as text, image, and video. This makes it possible to identify which format of content most effectively contributes to LLM learning.

[0060] The tag embedding unit can use the emotion estimation function to analyze the emotional reactions of users to content in which tags are embedded, and can award additional points to content that has a large number of positive reactions. The tag embedding unit, for example, develops a system that uses the emotion estimation function to analyze the emotional reactions of users to content in which tags are embedded in real time. For example, the tag embedding unit awards additional points to content that has a large number of positive emotional reactions. This makes it possible to analyze the emotional reactions of users and award additional points to content that has a large number of positive reactions.

[0061] To promote tag embedding, the tag embedding unit can provide content providers with guidelines or tutorials that explain how to embed tags. For example, the tag embedding unit provides content providers with guidelines that explain how to embed the [#LLM_OK] tag. For example, the tag embedding unit creates a document that describes specific steps and points to note. This makes it easier for content providers to understand how to embed tags.

[0062] The tag embedding unit can automatically classify content with embedded tags and build a dataset that is optimal for LLM learning. For example, the tag embedding unit can classify content with embedded tags and build a dataset that is optimal for LLM learning. For example, the tag embedding unit can classify content by content type or theme. This allows for the construction of a dataset that is optimal for LLM learning.

[0063] The tag embedding unit can use the emotion estimation function to evaluate the emotional value of content in which the tag is embedded and provide a special reward for content that is likely to evoke emotional empathy. The tag embedding unit, for example, uses the emotion estimation function to develop a system that evaluates the emotional value of content in which the tag is embedded. For example, the tag embedding unit provides a special reward for content with a high emotion score. This makes it possible to provide a special reward for content that is likely to evoke emotional empathy.

[0064] The point management unit can develop an algorithm in a point system that dynamically adjusts the point award rate according to the quality and frequency of use of content. The point management unit, for example, develops an algorithm in a point system that dynamically adjusts the point award rate according to the quality and frequency of use of content. For example, the point management unit awards more points to content that is used more frequently. This makes it possible to dynamically adjust the point award rate according to the quality and frequency of use of content.

[0065] The point management unit can track the point usage history in detail and analyze which content provider is using the points and how they are using them. The point management unit, for example, develops a system that tracks the point usage history in detail and analyzes which content provider is using the points and how they are using them. For example, the point management unit stores the usage history in a database so that it can be referenced later. This makes it possible to track the point usage history in detail and analyze the usage trends of content providers.

[0066] The point management unit uses the emotion estimation function to analyze the user's emotional responses in the point system and can award additional points if there are many positive responses. The point management unit, for example, uses the emotion estimation function to develop a system that analyzes the user's emotional responses in the point system in real time. For example, the point management unit awards additional points if there are many positive emotional responses. This makes it possible to analyze the user's emotional responses and award additional points if there are many positive responses.

[0067] The point management unit can link the point system with other reward programs, allowing content providers to select from a variety of rewards. For example, the point management unit develops a system that links the point system with other reward programs, allowing content providers to select from a variety of rewards. For example, the point management unit makes it possible to exchange points for electronic money or gift cards. This allows content providers to select from a variety of rewards.

[0068] The point management unit can use a point system to promote competition among content providers and provide a special reward to the provider who has acquired the most points. The point management unit develops a system that uses, for example, a point system to promote competition among content providers. For example, the point management unit provides a special reward to the provider who has acquired the most points. This promotes competition among content providers and provides a special reward to the provider who has acquired the most points.

[0069] The point management unit uses the emotion estimation function to monitor users' emotional reactions in the point system in real time, and can develop a reward program that is likely to resonate emotionally. The point management unit, for example, uses the emotion estimation function to develop a system that monitors users' emotional reactions in the point system in real time. For example, the point management unit adjusts the reward program based on the users' emotional reactions. This makes it possible to monitor users' emotional reactions in real time and develop a reward program that is likely to resonate emotionally.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The data collection system may further include a diversity evaluation unit that evaluates the diversity of content. The diversity evaluation unit may evaluate, for example, the diversity of content genres and themes, and award additional points to content providers who provide diverse content. This may improve the diversity of datasets used for LLM studies. For example, the diversity evaluation unit may award additional points to providers who provide content in different genres, such as news articles, essays, and technical documents. The diversity evaluation unit may also evaluate content from different languages ​​and cultural backgrounds, promoting the creation of international datasets.

[0072] The data collection system may further include a reliability evaluation unit that evaluates the reliability of content. For example, the reliability evaluation unit may verify the source and citation of the content and award additional points to highly reliable content. This may improve the quality of data used in LLM learning. For example, by providing content from highly reliable sources such as academic papers or government reports, the reliability evaluation unit may award additional points to the content provider. The reliability evaluation unit may also evaluate the frequency and recency of content updates and award additional points to content providers that provide the most up-to-date information.

[0073] The data collection system may further include an interactivity evaluation unit that evaluates the interactivity of the content. The interactivity evaluation unit, for example, evaluates the degree to which users interact with the content and awards additional points to interactive content. This can improve the quality of data used for LLM learning. For example, by providing content that encourages user participation, such as quiz-style content or articles that allow users to leave comments, the interactivity evaluation unit can award additional points to the provider. The interactivity evaluation unit can also collect user feedback and use it to improve the content.

[0074] The data collection system may further include an educational evaluation unit that evaluates the educational value of content. The educational evaluation unit may, for example, develop a system that awards additional points for educational content. This may improve the educational value of data used in LLM studies. For example, the educational evaluation unit may award additional points to providers of educational content such as science or history articles, educational videos, or online courses. The educational evaluation unit may also evaluate the quality of the educational content and award additional points to content providers that provide high-quality educational content.

[0075] The data collection system can further include an entertainment evaluation unit that evaluates the entertainment value of content. For example, the entertainment evaluation unit can develop a system that awards additional points to highly entertaining content. This can improve the entertainment value of data used in LLM learning. For example, by providing highly entertaining content such as movie reviews, game walkthroughs, and music videos, the entertainment evaluation unit can award additional points to the provider. The entertainment evaluation unit can also analyze user responses and award additional points to particularly popular entertainment content.

[0076] The data collection system can further include a personalization unit that uses an emotion estimation function to personalize content based on the user's emotions. The personalization unit, for example, develops a system that estimates the user's emotions and recommends optimal content based on those emotions. This makes it possible to provide content that matches the user's emotions and improve the user experience. For example, if the user is feeling stressed, relaxing content can be recommended, and if the user is excited, highly entertaining content can be recommended. The personalization unit can also analyze long-term emotional trends based on the user's emotional history to perform more accurate personalization.

[0077] The data collection system can further include an advertisement personalization unit that uses an emotion estimation function to personalize advertisements based on the user's emotions. The advertisement personalization unit, for example, develops a system that estimates the user's emotions and displays optimal advertisements based on those emotions. This makes it possible to provide advertisements that match the user's emotions and improve advertising effectiveness. For example, if the user has positive emotions, advertisements that stimulate purchasing motivation are displayed, and if the user has negative emotions, advertisements that help the user relax are displayed. The advertisement personalization unit can also develop long-term advertising strategies based on the user's emotion history.

[0078] The data collection system may further include a feedback unit that uses the emotion estimation function to provide feedback based on the user's emotions. The feedback unit may, for example, develop a system that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. This may provide feedback according to the user's emotions, improving the user experience. For example, if the user has positive emotions, further positive feedback may be provided, and if the user has negative emotions, encouraging feedback may be provided. The feedback unit may also develop a long-term feedback strategy based on the user's emotion history.

[0079] The data collection system can further include a learning support unit that uses an emotion estimation function to provide learning content based on the user's emotions. The learning support unit, for example, develops a system that estimates the user's emotions and recommends optimal learning content based on those emotions. This allows for providing learning content that matches the user's emotions and improving learning effectiveness. For example, if the user lacks concentration, it recommends content that can be learned in a short amount of time, and if the user is concentrating, it recommends content that promotes deeper understanding. The learning support unit can also create long-term learning plans based on the user's emotion history.

[0080] The data collection system can further include a health management unit that uses the emotion estimation function to support health management based on the user's emotions. The health management unit, for example, develops a system that estimates the user's emotions and provides appropriate health management advice based on those emotions. This supports health management according to the user's emotions and improves the user's health. For example, if the user is feeling stressed, it suggests relaxation methods, and if the user is feeling positive, it recommends exercise and a healthy diet. The health management unit can also create a long-term health management plan based on the user's emotional history.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The LLM Association is established through cooperation between companies that hold LLMs and manages common IDs. The common ID management unit manages common IDs that are uniquely assigned to content provided by each company. For example, the common ID management unit generates a unique ID for each piece of content and stores that ID in a database. Step 2: The tag embedding unit embeds the [#LLM_OK] tag into the content. For example, the tag embedding unit provides a tool that automatically inserts the [#LLM_OK] tag into the HTML code of a web page. The tag embedding unit can also embed tags in image data and video data in a similar manner. Step 3: The point management unit manages points and grants them to content providers. For example, the point management unit grants points for content with registered common IDs and records the points in a database. The point management unit also provides a system for exchanging points for other points in conjunction with PayPay IDs, etc. This allows content providers to exchange points for PayPay points or other electronic money and use them for everyday shopping. Points can also be exchanged for other electronic money or gift cards.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. The LLM Society and a common ID management unit that manages common IDs; A tag embedding section that embeds the [#LLM_OK] tag in the content, A point management unit that manages points A system characterized by:

2. The common ID management unit Track the usage history of said content, and record in detail which LLMs have used which content for their studies.

2. The system of claim 1.

3. The common ID management unit Promote data sharing among the different LLMs and build mutually complementary datasets.

2. The system of claim 1.

4. The tag embedding unit Develop a tool to automate the embedding of the tag, making it easy for content providers to add the tag.

2. The system of claim 1.

5. The point management unit Develop an algorithm that dynamically adjusts the point allocation rate in a points system according to the quality and frequency of use of the content.

2. The system of claim 1.

6. The common ID management unit Using an emotion estimation function, the user's emotional reaction to the content is analyzed, and additional points are awarded to the content that has a large number of positive reactions.

2. The system of claim 1.

7. The tag embedding unit Using an emotion estimation function, the emotional reactions of users to the content in which the tag is embedded are analyzed, and additional points are awarded to the content that has a large number of positive reactions.

2. The system of claim 1.

8. The point management unit Using emotion estimation, we analyze users' emotional responses in the point system and award additional points if the responses are mostly positive.

2. The system of claim 1.

Images