system

Abstract

An object of a system according to an embodiment is to determine which LLM is used to create a deliverable created in a generation AI.SOLUTION: A system according to an embodiment includes a generation AI deliverable collection unit, an LLM learning unit, and a generation AI detection unit. The generation AI deliverable collection unit collects deliverables created in each generation AI. The LLM learning unit causes the LLM to learn the deliverables collected by the generation AI deliverable collection unit. The generation AI detection unit determines the possibility that the deliverable learned by the LLM learning unit was created using which LLM.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 determine which LLM was used to create the artifacts created by generative AI.

[0005] The system according to the embodiment aims to determine which LLM was used to create the artifact created by the generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI artifact collection unit, an LLM learning unit, and a generation AI detection unit. The generation AI artifact collection unit collects artifacts created by each generation AI. The LLM learning unit trains the artifacts collected by the generation AI artifact collection unit using an LLM. The generation AI detection unit determines which LLM is likely to have been used to create the artifact learned by the LLM learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can determine which LLM was used to create the artifact created by the generation AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The generative AI detection system according to an embodiment of the present invention is a system that trains an LLM to learn the results of various content created by each generative AI, such as music, illustrations, videos, and documents, and then uses a generative AI detection AI to determine the possibility that each LLM was used. This allows the generative AI detection system to identify the source of the content and understand the usage status of the generative AI.

[0029] A generative AI detection system according to an embodiment includes a generative AI artifact collection unit, an LLM learning unit, and a generative AI detection unit. The generative AI artifact collection unit collects artifacts created by each generative AI. For example, it collects music created using a music generation AI, pictures drawn using an illustration generation AI, videos produced using a video generation AI, and text written using a text generation AI. The LLM learning unit trains the LLM on the artifacts collected by the generative AI artifact collection unit. For example, the LLM learns based on the collected music, pictures, videos, and text. The generative AI detection unit determines the likelihood that an LLM was used to create the artifact learned by the LLM learning unit. For example, the generative AI detection unit determines the likelihood that the collected music was created using a music generation AI, or the likelihood that the collected pictures were created using an illustration generation AI, etc. As a result, the generative AI detection system according to an embodiment trains the LLM on the artifacts created by each generative AI, and then uses the generative AI detection AI to determine the likelihood that each LLM was used.

[0030] The generation AI artifact collection unit can add details of the generation AI version and algorithm as metadata. For example, the generation AI artifact collection unit adds generation AI version information as metadata to artifacts created by each generation AI. For example, for a song created with music generation AI version 1.2, the version information is added as metadata and the LLM is trained on it. The generation AI artifact collection unit also adds details of the algorithm used as metadata. For example, details of the algorithm used by an illustration generation AI are added as metadata and the LLM is trained on it. In this way, adding details of the generation AI version and algorithm as metadata improves the learning accuracy of the LLM.

[0031] The generation AI artifact collection unit can record details of the generation process. For example, the generation AI artifact collection unit records the time it took to generate a generated artifact and has the LLM learn that information. For example, if a song created by a music generation AI takes five minutes, the time information is recorded. The generation AI artifact collection unit also records the resources used. For example, it records the type of GPU and memory usage used by the video generation AI and has the LLM learn that information. By recording details of the generation process, the LLM's learning data becomes richer.

[0032] The generative AI artifact collection unit can convert artifacts created by the generative AI into different formats. For example, the generative AI artifact collection unit converts songs created by a music generation AI into text format and trains the LLM on that text data. For example, it records the lyrics and melody structure of the song as text data. The generative AI artifact collection unit also converts pictures drawn by an illustration generation AI into image format and trains the LLM on that image data. For example, it records the color and composition of the picture as image data. In this way, by converting artifacts created by the generative AI into different formats, the LLM's training data is diversified.

[0033] The generative AI artifact collection unit can compare artifacts created by the generative AI with similar artifacts created in different cultures and languages. For example, the generative AI artifact collection unit compares music created by music generation AI with music from different cultures, allowing the LLM to learn the differences and similarities between them. For example, comparing traditional Japanese music with Western classical music. The generative AI artifact collection unit also compares sentences created by document generation AI with sentences in different languages, allowing the LLM to learn the differences and similarities between them. For example, comparing English sentences with Japanese sentences. This diversifies the LLM's learning data by comparing artifacts created by the generative AI with similar artifacts created in different cultures and languages.

[0034] The generation AI detection unit can perform a detailed analysis of the style and theme of the generated artifact. For example, the generation AI detection unit performs a detailed analysis of the style of the generated music (e.g., classical, pop, rock, etc.) to determine the characteristics of each LLM. The generation AI detection unit also performs a detailed analysis of the theme of the generated illustration (e.g., landscape, portrait, etc.) to determine the characteristics of each LLM. This detailed analysis of the style and theme of the generated artifact allows for more precise determination of the characteristics of each LLM.

[0035] The generation AI detection unit can be based on background information of the generated artifact. The generation AI detection unit improves the accuracy of the determination by taking into account, for example, background information of the generated music (e.g., the date, time, and location of the music being generated). For example, it determines music related to a specific event or season. The generation AI detection unit also improves the accuracy of the determination by taking into account background information of the generated video (e.g., the location and time of the video). In this way, by taking into account the background information of the generated artifact, the accuracy of the determination is improved.

[0036] The generative AI detection unit can be based on a combination of different generative AIs. The generative AI detection unit improves the accuracy of its judgment by taking into account, for example, deliverables created through the joint work of a music generation AI and an illustration generation AI. For example, it can detect multimedia works that combine music and illustrations. The generative AI detection unit also improves the accuracy of its judgment by taking into account deliverables created through the joint work of a video generation AI and a document generation AI. For example, it can detect documentaries that combine video and text. In this way, the accuracy of its judgment is improved by taking into account different combinations of generative AIs.

[0037] The generative AI detection unit can be based on the intended use of the generated artifact. The generative AI detection unit improves the accuracy of the judgment by taking into account, for example, the intended use of the generated music (e.g., commercial or personal use). For example, it distinguishes between commercial music and personal music. The generative AI detection unit also improves the accuracy of the judgment by taking into account the intended use of the generated text (e.g., educational or research use). For example, it distinguishes between educational materials and research papers. In this way, the accuracy of the judgment is improved by taking into account the intended use of the generated artifact.

[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 generative AI artifact collection unit can evaluate the environmental impact of generated artifacts. For example, it can record the energy consumption required to generate a song created by a music generation AI and train the LLM on that data. The generative AI artifact collection unit can also record the resources (e.g., electricity consumption and materials used) used to generate a picture drawn by an illustration generation AI and train the LLM on that data. This allows the LLM to more diversify its training data by evaluating the environmental impact of generated artifacts, promoting the use of environmentally friendly generative AI.

[0040] The generative AI artifact collection unit can evaluate the social impact of generated artifacts. For example, it evaluates the social impact (e.g., trend and cultural influence) of songs created by music generation AI and trains the LLM on that data. Similarly, the generative AI artifact collection unit evaluates the social impact (e.g., education and awareness-raising) of texts created by document generation AI and trains the LLM on that data. By evaluating the social impact of generated artifacts, the LLM's training data becomes more diverse, promoting the use of generative AI in a socially beneficial way.

[0041] The generative AI artifact collection unit can record user interactions with generated artifacts. For example, it can record the number of user plays and ratings for songs created by the music generation AI and have the LLM learn from that data. The generative AI artifact collection unit can also record user comments and the number of shares for pictures drawn by the illustration generation AI and have the LLM learn from that data. By recording user interactions with generated artifacts, the LLM's learning data becomes richer, promoting the use of generative AI that reflects user responses.

[0042] The generative AI artifact collection unit can evaluate the long-term impact of generated artifacts. For example, it evaluates the long-term impact of a song created by a music generation AI on a user (e.g., changes in memory or emotions) and trains the LLM with that data. The generative AI artifact collection unit also evaluates the long-term impact of a text created by a document generation AI on society (e.g., the sustainability of education and awareness-raising) and trains the LLM with that data. By evaluating the long-term impact of generated artifacts, the LLM's training data becomes richer, promoting the sustainable use of generative AI.

[0043] The generative AI artifact collection unit can perform real-time evaluation of generated artifacts. For example, it can record how users evaluate songs created by music generation AI in real time and have the LLM learn from that data. The generative AI artifact collection unit can also record how users evaluate generated videos in real time and have the LLM learn from that data. This allows for real-time evaluation of generated artifacts, making the LLM's learning data more immediate, promoting the use of generative AI that reflects rapid feedback.

[0044] The generative AI detection unit can evaluate the ethical aspects of generated deliverables. For example, it can evaluate whether songs created by music generation AI are ethically sound and train the LLM with that data. Similarly, the generative AI detection unit can evaluate whether texts created by document generation AI are ethically sound and train the LLM with that data. By evaluating the ethical aspects of generated deliverables, the LLM's training data can be made more diverse, promoting the use of ethically sound generative AI.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The generation AI artifact collection unit collects artifacts created by each generation AI. For example, it collects music created using music generation AI, pictures drawn using illustration generation AI, videos produced using video generation AI, and texts written using document generation AI. Step 2: The LLM learning unit trains the LLM on the artifacts collected by the generative AI artifact collection unit. For example, the LLM learns from collected music, pictures, videos, and text. Step 3: The generative AI detection unit determines the likelihood that the LLM used to create the artifacts learned by the LLM learning unit. For example, the generative AI detection unit may determine that the collected songs were created with a music generation AI with an 80% probability, and the collected pictures were created with an illustration generation AI with a 90% probability.

[0047] (Example 2) The generative AI detection system according to an embodiment of the present invention is a system that trains an LLM to learn the results of various content created by each generative AI, such as music, illustrations, videos, and documents, and then uses a generative AI detection AI to determine the possibility that each LLM was used. This allows the generative AI detection system to identify the source of the content and understand the usage status of the generative AI.

[0048] A generative AI detection system according to an embodiment includes a generative AI artifact collection unit, an LLM learning unit, and a generative AI detection unit. The generative AI artifact collection unit collects artifacts created by each generative AI. For example, it collects music created using a music generation AI, pictures drawn using an illustration generation AI, videos produced using a video generation AI, and text written using a text generation AI. The LLM learning unit trains the LLM on the artifacts collected by the generative AI artifact collection unit. For example, the LLM learns based on the collected music, pictures, videos, and text. The generative AI detection unit determines the likelihood that an LLM was used to create the artifact learned by the LLM learning unit. For example, the generative AI detection unit determines the likelihood that the collected music was created using a music generation AI, or the likelihood that the collected pictures were created using an illustration generation AI, etc. As a result, the generative AI detection system according to an embodiment trains the LLM on the artifacts created by each generative AI, and then uses the generative AI detection AI to determine the likelihood that each LLM was used.

[0049] The generation AI artifact collection unit can add details of the generation AI version and algorithm as metadata. For example, the generation AI artifact collection unit adds generation AI version information as metadata to artifacts created by each generation AI. For example, for a song created with music generation AI version 1.2, the version information is added as metadata and the LLM is trained on it. The generation AI artifact collection unit also adds details of the algorithm used as metadata. For example, details of the algorithm used by an illustration generation AI are added as metadata and the LLM is trained on it. In this way, adding details of the generation AI version and algorithm as metadata improves the learning accuracy of the LLM.

[0050] The generation AI artifact collection unit can record details of the generation process. For example, the generation AI artifact collection unit records the time it took to generate a generated artifact and has the LLM learn that information. For example, if a song created by a music generation AI takes five minutes, the time information is recorded. The generation AI artifact collection unit also records the resources used. For example, it records the type of GPU and memory usage used by the video generation AI and has the LLM learn that information. By recording details of the generation process, the LLM's learning data becomes richer.

[0051] The generation AI artifact collection unit can evaluate the emotional impact that artifacts generated using the emotion estimation function have on the user. For example, the generation AI artifact collection unit uses the emotion estimation function on the generated artifacts to evaluate the emotions felt by the user. For example, it quantifies the emotions (joy, sadness, etc.) that a song created by a music generation AI evokes in the user and trains the LLM on that data. The generation AI artifact collection unit also evaluates the emotional impact that a generated illustration has on the user. For example, it evaluates the emotions (surprise, excitement, etc.) that a picture drawn by an illustration generation AI evokes in the user and trains the LLM on that data. In this way, by using the emotion estimation function to evaluate the emotional impact on the user, the LLM's training data becomes more diverse.

[0052] The generative AI artifact collection unit can convert artifacts created by the generative AI into different formats. For example, the generative AI artifact collection unit converts songs created by a music generation AI into text format and trains the LLM on that text data. For example, it records the lyrics and melody structure of the song as text data. The generative AI artifact collection unit also converts pictures drawn by an illustration generation AI into image format and trains the LLM on that image data. For example, it records the color and composition of the picture as image data. In this way, by converting artifacts created by the generative AI into different formats, the LLM's training data is diversified.

[0053] The generative AI artifact collection unit can compare artifacts created by the generative AI with similar artifacts created in different cultures and languages. For example, the generative AI artifact collection unit compares music created by music generation AI with music from different cultures, allowing the LLM to learn the differences and similarities between them. For example, comparing traditional Japanese music with Western classical music. The generative AI artifact collection unit also compares sentences created by document generation AI with sentences in different languages, allowing the LLM to learn the differences and similarities between them. For example, comparing English sentences with Japanese sentences. This diversifies the LLM's learning data by comparing artifacts created by the generative AI with similar artifacts created in different cultures and languages.

[0054] The generative AI artifact collection unit can evaluate the emotional impact of artifacts generated using the emotion estimation function on different user groups. For example, the generative AI artifact collection unit uses the emotion estimation function on the generated artifacts to evaluate the emotions felt by users of different age groups. For example, it evaluates the difference in emotions felt by young people and older people when a song created by a music generation AI is generated. The generative AI artifact collection unit also evaluates the emotions felt by users of different occupations. For example, it evaluates the difference in emotions felt by students and business people when a video created by a video generation AI is generated. In this way, by using the emotion estimation function to evaluate the emotional impact on different user groups, the learning data for the LLM is diversified.

[0055] The generation AI detection unit can perform a detailed analysis of the style and theme of the generated artifact. For example, the generation AI detection unit performs a detailed analysis of the style of the generated music (e.g., classical, pop, rock, etc.) to determine the characteristics of each LLM. The generation AI detection unit also performs a detailed analysis of the theme of the generated illustration (e.g., landscape, portrait, etc.) to determine the characteristics of each LLM. This detailed analysis of the style and theme of the generated artifact allows for more precise determination of the characteristics of each LLM.

[0056] The generation AI detection unit can be based on background information of the generated artifact. The generation AI detection unit improves the accuracy of the determination by taking into account, for example, background information of the generated music (e.g., the date, time, and location of the music being generated). For example, it determines music related to a specific event or season. The generation AI detection unit also improves the accuracy of the determination by taking into account background information of the generated video (e.g., the location and time of the video). In this way, by taking into account the background information of the generated artifact, the accuracy of the determination is improved.

[0057] The generation AI detection unit can determine the usability of an LLM based on the emotional impact that a deliverable generated using the emotion estimation function has on the user. The generation AI detection unit determines the usability of each LLM based on, for example, the emotional impact that a generated song has on the user. For example, it evaluates the emotion (joy, sadness, etc.) that a song has on the user and determines the usability of the LLM. The generation AI detection unit also determines the usability of each LLM based on the emotional impact that a generated sentence has on the user. For example, it evaluates the emotion (surprise, emotion, etc.) that a sentence has on the user and determines the usability of the LLM. This improves the accuracy of the determination by using the emotion estimation function to determine the usability of each LLM based on the emotional impact it has on the user.

[0058] The generative AI detection unit can be based on a combination of different generative AIs. The generative AI detection unit improves the accuracy of its judgment by taking into account, for example, deliverables created through the joint work of a music generation AI and an illustration generation AI. For example, it can detect multimedia works that combine music and illustrations. The generative AI detection unit also improves the accuracy of its judgment by taking into account deliverables created through the joint work of a video generation AI and a document generation AI. For example, it can detect documentaries that combine video and text. In this way, the accuracy of its judgment is improved by taking into account different combinations of generative AIs.

[0059] The generative AI detection unit can be based on the intended use of the generated artifact. The generative AI detection unit improves the accuracy of the judgment by taking into account, for example, the intended use of the generated music (e.g., commercial or personal use). For example, it distinguishes between commercial music and personal music. The generative AI detection unit also improves the accuracy of the judgment by taking into account the intended use of the generated text (e.g., educational or research use). For example, it distinguishes between educational materials and research papers. In this way, the accuracy of the judgment is improved by taking into account the intended use of the generated artifact.

[0060] The generation AI detection unit can determine the usability of an LLM based on the emotional impact of the deliverables generated using the emotion estimation function on different user groups. The generation AI detection unit determines the usability of each LLM, for example, based on the emotional impact the generated music has on users of different age groups. For example, it evaluates the difference in emotional responses between younger and older users. The generation AI detection unit also determines the usability of each LLM based on the emotional impact the generated video has on users of different occupations. For example, it evaluates the difference in emotional responses between students and business people. This improves the accuracy of the determination by using the emotion estimation function to determine the usability of each LLM based on the emotional impact it has on different user groups.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The generative AI artifact collection unit can evaluate the environmental impact of generated artifacts. For example, it can record the energy consumption required to generate a song created by a music generation AI and train the LLM on that data. The generative AI artifact collection unit can also record the resources (e.g., electricity consumption and materials used) used to generate a picture drawn by an illustration generation AI and train the LLM on that data. This allows the LLM to more diversify its training data by evaluating the environmental impact of generated artifacts, promoting the use of environmentally friendly generative AI.

[0063] The generative AI artifact collection unit can evaluate the social impact of generated artifacts. For example, it evaluates the social impact (e.g., trend and cultural influence) of songs created by music generation AI and trains the LLM on that data. Similarly, the generative AI artifact collection unit evaluates the social impact (e.g., education and awareness-raising) of texts created by document generation AI and trains the LLM on that data. By evaluating the social impact of generated artifacts, the LLM's training data becomes more diverse, promoting the use of generative AI in a socially beneficial way.

[0064] The generative AI artifact collection unit can record user interactions with generated artifacts. For example, it can record the number of user plays and ratings for songs created by the music generation AI and have the LLM learn from that data. The generative AI artifact collection unit can also record user comments and the number of shares for pictures drawn by the illustration generation AI and have the LLM learn from that data. By recording user interactions with generated artifacts, the LLM's learning data becomes richer, promoting the use of generative AI that reflects user responses.

[0065] The generative AI artifact collection unit can use the emotion estimation function to evaluate the emotional impact of generated artifacts in specific events or situations. For example, it can evaluate the emotional impact that a song created by a music generation AI has on users at specific events such as weddings or funerals, and train the LLM with that data. The generative AI artifact collection unit can also evaluate the emotional impact that a generated video has on users during disasters or celebrations, and train the LLM with that data. By evaluating the emotional impact of specific events and situations, the LLM's training data becomes more diverse, promoting the use of emotion-conscious generative AI.

[0066] The generative AI artifact collection unit can evaluate the long-term impact of generated artifacts. For example, it evaluates the long-term impact of a song created by a music generation AI on a user (e.g., changes in memory or emotions) and trains the LLM with that data. The generative AI artifact collection unit also evaluates the long-term impact of a text created by a document generation AI on society (e.g., the sustainability of education and awareness-raising) and trains the LLM with that data. By evaluating the long-term impact of generated artifacts, the LLM's training data becomes richer, promoting the sustainable use of generative AI.

[0067] The generative AI artifact collection unit can use the emotion estimation function to evaluate the emotional impact of artifacts generated on users from different cultures. For example, it evaluates the emotional impact of music created by a music generation AI on users from different cultures and trains the LLM with that data. The generative AI artifact collection unit also evaluates the emotional impact of generated illustrations on users from different cultures and trains the LLM with that data. This evaluation of the emotional impact on users from different cultures further diversifies the LLM's training data and promotes the international use of generative AI.

[0068] The generative AI artifact collection unit can perform real-time evaluation of generated artifacts. For example, it can record how users evaluate songs created by music generation AI in real time and have the LLM learn from that data. The generative AI artifact collection unit can also record how users evaluate generated videos in real time and have the LLM learn from that data. This allows for real-time evaluation of generated artifacts, making the LLM's learning data more immediate, promoting the use of generative AI that reflects rapid feedback.

[0069] The generative AI detection unit can use the emotion estimation function to evaluate the impact of a generated product on a user in a specific emotional state. For example, it can evaluate the emotional impact of a song created by a music generation AI on a user in a stressful state and train the LLM with that data. The generative AI detection unit can also evaluate the emotional impact of a generated sentence on a user feeling happy and train the LLM with that data. By evaluating the impact on users in specific emotional states, the LLM's training data becomes more diverse, promoting the use of emotion-conscious generative AI.

[0070] The generative AI detection unit can evaluate the ethical aspects of generated deliverables. For example, it can evaluate whether songs created by music generation AI are ethically sound and train the LLM with that data. Similarly, the generative AI detection unit can evaluate whether texts created by document generation AI are ethically sound and train the LLM with that data. By evaluating the ethical aspects of generated deliverables, the LLM's training data can be made more diverse, promoting the use of ethically sound generative AI.

[0071] The generative AI detection unit can use the emotion estimation function to evaluate the impact of the generated results on the user's emotional health. For example, it can evaluate the extent to which a song created by a music generation AI contributes to reducing the user's stress, and train the LLM with that data. The generative AI detection unit can also evaluate the extent to which a generated sentence contributes to improving the user's sense of happiness, and train the LLM with that data. This evaluation of the impact on the user's emotional health will further diversify the LLM's training data, promoting the use of generative AI that takes emotional health into consideration.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The generation AI artifact collection unit collects artifacts created by each generation AI. For example, it collects music created using music generation AI, pictures drawn using illustration generation AI, videos produced using video generation AI, and texts written using document generation AI. Step 2: The LLM learning unit trains the LLM on the artifacts collected by the generative AI artifact collection unit. For example, the LLM learns from collected music, pictures, videos, and text. Step 3: The generative AI detection unit determines the likelihood that the LLM used to create the artifacts learned by the LLM learning unit. For example, the generative AI detection unit may determine that the collected songs were created with a music generation AI with an 80% probability, and the collected pictures were created with an illustration generation AI with a 90% probability.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation AI artifact collection unit that collects artifacts created by each generation AI; an LLM learning unit that causes an LLM to learn the artifacts collected by the generation AI artifact collection unit; a generation AI detection unit that determines the likelihood that the artifact learned by the LLM learning unit was created using an LLM; A system characterized by:

2. The generated AI artifact collection unit Add details of the AI ​​generation version and algorithm as metadata.

2. The system of claim 1.

3. The generated AI artifact collection unit Convert the deliverables created by the generation AI into a different format 2. The system of claim 1.

4. The generation AI detection unit Conduct a detailed analysis of the style or theme of the resulting artifacts 2. The system of claim 1.

5. The generated AI artifact collection unit Evaluating the emotional impact of the generated deliverable on the user using an emotion estimation function.

2. The system of claim 1.

Images