System

Abstract

An object of the system according to the embodiment is to allow a user to ask an appropriate question to generated AI and to obtain an answer with high satisfaction.SOLUTION: A system according to an embodiment includes a generation AI, a prompt display unit, and a user input device. The generative AI learns user questions. The prompt display unit displays the supplementary item learned by the generation AI. The user input unit receives an input of the supplementary item displayed by the prompt display unit from the user.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 for users to ask appropriate questions to the generated AI, making it difficult to obtain satisfactory answers.

[0005] The system according to the embodiment aims to enable users to ask appropriate questions to the generating AI and obtain highly satisfactory answers. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a prompt display unit, and a user input unit. The generation AI learns user questions. The prompt display unit displays supplemental items learned by the generation AI. The user input unit accepts user input of the supplemental items displayed by the prompt display unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to ask appropriate questions to the generating AI and obtain highly satisfying answers. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) A system according to an embodiment of the present invention uses a generation AI that acts as an intermediary between the generation AI and the user to learn what kind of questions the user should ask to get a better answer. In this system, the generation AI prompts the user with supplementary items that will increase the accuracy of the user's question, and the user can obtain a more satisfying answer by inputting those supplementary items. This allows the system to enable the user to ask the generation AI more specific and satisfying questions, and the generation AI to provide more accurate answers.

[0029] A system according to an embodiment includes a generation AI, a prompt display unit, and a user input unit. The generation AI learns user questions. For example, the generation AI analyzes questions posed to the generation AI by the user and learns which questions elicit better answers. The generation AI also evaluates the generation AI's answers to user questions and learns what supplemental information will result in a more specific and satisfying answer. The prompt display unit displays supplemental items learned by the generation AI. For example, if a user asks, "What books do you recommend?", the prompt display unit displays supplemental items such as "genre," "author," and "year of publication." The user input unit accepts user input of the supplemental items displayed by the prompt display unit. For example, if the user inputs "mystery" and "after 2020," the user input unit accepts this information. This allows the system to suggest appropriate supplemental items for the user's question and obtain a satisfying answer.

[0030] The generation AI can analyze the user's past question history and learn question patterns. For example, the generation AI can analyze the user's past question history and identify frequently occurring question patterns. For example, if a user frequently asks, "What books do you recommend?", the generation AI can suggest supplementary items such as "genre," "author," and "year of publication" as supplementary items to that question. This allows the generation AI to analyze the user's past question history and suggest more appropriate supplementary items for the next question.

[0031] The generation AI can evaluate the quality of the generation AI's answer to a user's question and automatically re-learn if the answer quality is low. For example, the generation AI evaluates the generation AI's answer to a user's question and identifies the cause if the answer quality is low. For example, if the answer is vague and lacks specificity, the cause is analyzed and re-learning is performed. This allows the generation AI to automatically re-learn if the answer quality is low and generate a more appropriate answer for the next question.

[0032] The generation AI can suggest supplementary items for a user's question by referring to related past questions and their answers. For example, when a user asks, "What books do you recommend?", if a similar question has been asked in the past, the generation AI can suggest supplementary items based on the answers. This allows the generation AI to suggest appropriate supplementary items by referring to related past questions and their answers.

[0033] Generative AI can learn questions from different fields and utilize that knowledge to suggest supplementary items. For example, if a user asks, "What books do you recommend?", generative AI can suggest supplementary items such as "genre," "author," and "year of publication" based on knowledge from different fields. This makes it possible to suggest supplementary items by utilizing knowledge from different fields.

[0034] The generation AI can display multiple supplementary items in order of priority in response to a user's question, making it easier for the user to select. For example, if a user asks, "What books do you recommend?", the generation AI will display supplementary items such as "genre," "author," and "year of publication" in order of priority. This allows the supplementary items to be displayed in order of priority, making it easier for the user to select.

[0035] The generation AI can customize the supplementary items for a user's question based on the user's past selection history. For example, the generation AI analyzes the user's past selection history and customizes the supplementary items based on that data. For example, if the user previously selected "mystery novels," the generation AI will suggest supplementary items such as "mystery" and "suspense" for the next question. This allows the generation AI to customize supplementary items based on the user's past selection history.

[0036] In response to a user's question, the generation AI can display multimodal supplementary items including images and audio data. For example, if a user asks, "What books do you recommend?", the generation AI can display images of the book's cover and audio of an interview with the author. This allows the display of multimodal supplementary items to enable the user to obtain more specific information.

[0037] The generative AI can automatically translate supplementary items in different languages, making it possible to accommodate international users. For example, if a user asks, "What books do you recommend?", the generative AI can display supplementary items in multiple languages ​​and allow the user to select one. This allows the generative AI to automatically translate supplementary items in different languages, making it possible to accommodate international users.

[0038] The generative AI can analyze the user's input and optimize its answers in real time based on the input. The generative AI can, for example, analyze the user's input and optimize its answers in real time based on that data. For example, if a user asks, "What books do you recommend?" and enters "mystery" and "after 2020" as supplementary items, the generative AI's answers will be optimized based on that information. This allows the generative AI's answers to be optimized in real time based on the user's input.

[0039] The generation AI evaluates the generation AI's response to the user's input and can improve its next response based on the evaluation results. The generation AI, for example, evaluates the generation AI's response to the user's input and improves its next response based on the evaluation results. For example, if a user asks, "What books do you recommend?" and the generation AI's response is insufficient, the generation AI can improve its next response based on the evaluation results. This allows the generation AI to evaluate its response to the user's input and improve its next response.

[0040] The generative AI can analyze the user's input and automatically provide related additional information. For example, the generative AI can analyze the user's input and automatically provide related additional information based on that data. For example, if a user asks, "What books do you recommend?" and enters "mystery" and "after 2020" as supplementary items, related additional information can be provided based on that information. This makes it possible to automatically provide related additional information based on the user's input.

[0041] Generative AI can utilize knowledge from different fields to generate answers from multiple perspectives to the user's input. For example, if a user asks, "What books do you recommend?", generative AI can generate an answer by utilizing knowledge from different fields such as literature, history, and science. This makes it possible to utilize knowledge from different fields to generate answers from multiple perspectives.

[0042] The generative AI continuously analyzes the user's questions and the generative AI's answers, and can improve the accuracy of the generative AI's answers through a feedback loop. For example, the generative AI continuously analyzes the user's questions and the generative AI's answers, and builds a feedback loop based on that data. For example, if a user asks, "What books do you recommend?" and the generative AI's answer is insufficient, the data can be used to improve the next answer. In this way, the generative AI continuously analyzes the user's questions and the generative AI's answers, and can improve the accuracy of the answer through a feedback loop.

[0043] The generative AI can dynamically adjust its answering algorithm based on the user's satisfaction rating. For example, the generative AI builds a system that dynamically adjusts the generative AI's answering algorithm based on the user's satisfaction rating. For example, if a user asks, "What books do you recommend?" and the satisfaction level with the generative AI's answer is low, the algorithm is adjusted based on that rating. This allows the generative AI's answering algorithm to be dynamically adjusted based on the user's satisfaction rating.

[0044] Generative AI can compare questions and answers from different users and extract common feedback patterns. For example, generative AI can build a system that compares questions and answers from different users and extracts common feedback patterns. For example, if a user asks, "What books do you recommend?", it compares similar questions and answers from other users and extracts common feedback patterns. This makes it possible to compare questions and answers from different users and extract common feedback patterns.

[0045] Generative AI can analyze questions and answers from different fields and utilize knowledge from different fields to optimize feedback loops. For example, generative AI can analyze questions and answers from different fields and utilize that knowledge to build a system that optimizes feedback loops. For example, if a user asks, "What books do you recommend?", the feedback loop can be optimized based on knowledge from different fields. This makes it possible to optimize feedback loops by utilizing knowledge from different fields.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] In response to a user's question, generative AI can automatically search for relevant news articles and academic papers and provide them as supplementary information. For example, if a user asks, "Please tell me about the latest AI technology," generative AI will search for the latest related news articles and academic papers and provide links to them. This allows the user to gain a deeper understanding of the question. Similarly, if a user asks, "What books do you recommend?" generative AI can provide reviews and author interviews related to that book. Furthermore, if a user asks, "I would like to know more about a specific disease," generative AI can provide relevant medical papers and expert opinions.

[0048] Generative AI can suggest related video content in response to user questions. For example, if a user asks, "What movies do you recommend?", generative AI can suggest trailers and review videos of related movies. This allows users to deepen their understanding of the question through visual information. Also, if a user asks, "Please tell me the recipe for a specific dish," generative AI can suggest videos that explain how to make that dish. Furthermore, if a user asks, "I would like to learn about a specific technology," generative AI can suggest educational videos related to that technology.

[0049] In response to a user's question, the generative AI can provide links to related community forums and Q&A sites. For example, if a user asks "A question about a specific programming language," the generative AI can provide links to related programming forums and Q&A sites. This allows the user to interact with other experts and people with the same interests and gain diverse perspectives on their question. Also, if a user asks "I want to know information about a specific disease," the generative AI can provide links to related medical forums and Q&A sites. Furthermore, if a user asks "I want to know information about a specific hobby," the generative AI can provide links to community forums related to that hobby.

[0050] In response to a user's question, generative AI can provide summaries of related books and papers. For example, if a user asks, "Tell me about a particular historical event," generative AI can provide summaries of related books and papers. This allows the user to quickly grasp the overview of the question. Also, if a user asks, "I want to know about a particular scientific theory," generative AI can provide summaries of papers related to that theory. Furthermore, if a user asks, "I want to learn about a particular business strategy," generative AI can provide summaries of books related to that strategy.

[0051] Generative AI can provide relevant statistical data and graphs in response to user questions. For example, if a user asks, "Please tell me about a specific economic indicator," the generative AI will provide relevant statistical data and graphs. This allows the user to visually understand the specific data in response to the question. Also, if a user asks, "I would like to know about a specific market trend," the generative AI can provide statistical data and graphs related to that market. Furthermore, if a user asks, "I would like to know about a specific health indicator," the generative AI can provide statistical data and graphs related to that health indicator.

[0052] Generative AI can provide relevant expert opinions and interviews in response to user questions. For example, if a user asks, "I would like to know information about a specific medical issue," generative AI can provide opinions and interviews of relevant medical experts. This allows the user to obtain reliable information in response to their question. Also, if a user asks, "I would like to learn about a specific technology," generative AI can provide expert opinions and interviews on that technology. Furthermore, if a user asks, "I would like to know about a specific business strategy," generative AI can provide opinions and interviews of business experts on that strategy.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The generating AI learns from user questions. For example, the generating AI analyzes the questions users ask the generating AI and learns what questions elicit better answers. The generating AI also evaluates its answers to user questions and learns what supplementary information will result in more specific and satisfying answers. Step 2: The prompt display unit displays the supplementary items learned by the generation AI. For example, if a user asks, "What books do you recommend?", the prompt display unit displays supplementary items such as "genre," "author," and "year of publication." Step 3: The user input unit accepts the user's input of the supplementary items displayed by the prompt display unit. For example, if the user inputs "mystery" and "after 2020," the user input unit accepts this information.

[0055] (Example 2) A system according to an embodiment of the present invention uses a generation AI that acts as an intermediary between the generation AI and the user to learn what kind of questions the user should ask to get a better answer. In this system, the generation AI prompts the user with supplementary items that will increase the accuracy of the user's question, and the user can obtain a more satisfying answer by inputting those supplementary items. This allows the system to enable the user to ask the generation AI more specific and satisfying questions, and the generation AI to provide more accurate answers.

[0056] A system according to an embodiment includes a generation AI, a prompt display unit, and a user input unit. The generation AI learns user questions. For example, the generation AI analyzes questions posed to the generation AI by the user and learns which questions elicit better answers. The generation AI also evaluates the generation AI's answers to user questions and learns what supplemental information will result in a more specific and satisfying answer. The prompt display unit displays supplemental items learned by the generation AI. For example, if a user asks, "What books do you recommend?", the prompt display unit displays supplemental items such as "genre," "author," and "year of publication." The user input unit accepts user input of the supplemental items displayed by the prompt display unit. For example, if the user inputs "mystery" and "after 2020," the user input unit accepts this information. This allows the system to suggest appropriate supplemental items for the user's question and obtain a satisfying answer.

[0057] The generation AI can analyze the user's past question history and learn question patterns. For example, the generation AI can analyze the user's past question history and identify frequently occurring question patterns. For example, if a user frequently asks, "What books do you recommend?", the generation AI can suggest supplementary items such as "genre," "author," and "year of publication" as supplementary items to that question. This allows the generation AI to analyze the user's past question history and suggest more appropriate supplementary items for the next question.

[0058] The generation AI can evaluate the quality of the generation AI's answer to a user's question and automatically re-learn if the answer quality is low. For example, the generation AI evaluates the generation AI's answer to a user's question and identifies the cause if the answer quality is low. For example, if the answer is vague and lacks specificity, the cause is analyzed and re-learning is performed. This allows the generation AI to automatically re-learn if the answer quality is low and generate a more appropriate answer for the next question.

[0059] The generation AI can use the emotion estimation function to analyze the emotion a user has when asking a question and suggest supplementary items that correspond to that emotion. For example, the generation AI uses the emotion estimation function to analyze the emotion a user has when entering a question. For example, when a user asks "What books do you recommend?" and has positive emotions, the generation AI will suggest supplementary items that correspond to that emotion. This makes it possible to suggest supplementary items that correspond to the user's emotions and obtain a highly satisfying answer.

[0060] The generation AI can suggest supplementary items for a user's question by referring to related past questions and their answers. For example, when a user asks, "What books do you recommend?", if a similar question has been asked in the past, the generation AI can suggest supplementary items based on the answers. This allows the generation AI to suggest appropriate supplementary items by referring to related past questions and their answers.

[0061] Generative AI can learn questions from different fields and utilize that knowledge to suggest supplementary items. For example, if a user asks, "What books do you recommend?", generative AI can suggest supplementary items such as "genre," "author," and "year of publication" based on knowledge from different fields. This makes it possible to suggest supplementary items by utilizing knowledge from different fields.

[0062] The generative AI can use its emotion estimation function to analyze the emotions of users when they input questions in real time and suggest supplementary items that will elicit positive emotions. For example, the generative AI can use its emotion estimation function to analyze the emotions of users when they input questions in real time and suggest supplementary items that will elicit positive emotions. For example, when a user asks, "What books do you recommend?", the generative AI can suggest supplementary items that will elicit positive emotions. This makes it possible to analyze the user's emotions in real time and suggest supplementary items that will elicit positive emotions.

[0063] The generation AI can display multiple supplementary items in order of priority in response to a user's question, making it easier for the user to select. For example, if a user asks, "What books do you recommend?", the generation AI will display supplementary items such as "genre," "author," and "year of publication" in order of priority. This allows the supplementary items to be displayed in order of priority, making it easier for the user to select.

[0064] The generation AI can customize the supplementary items for a user's question based on the user's past selection history. For example, the generation AI analyzes the user's past selection history and customizes the supplementary items based on that data. For example, if the user previously selected "mystery novels," the generation AI will suggest supplementary items such as "mystery" and "suspense" for the next question. This allows the generation AI to customize supplementary items based on the user's past selection history.

[0065] The generation AI can use the emotion estimation function to dynamically change supplementary items according to the user's emotions, thereby improving user satisfaction. For example, the generation AI can use the emotion estimation function to build a system that dynamically changes supplementary items according to the user's emotions. For example, if the user has positive emotions when entering a question, the generation AI can suggest supplementary items according to that emotion. This makes it possible to dynamically change supplementary items according to the user's emotions and improve user satisfaction.

[0066] In response to a user's question, the generation AI can display multimodal supplementary items including images and audio data. For example, if a user asks, "What books do you recommend?", the generation AI can display images of the book's cover and audio of an interview with the author. This allows the display of multimodal supplementary items to enable the user to obtain more specific information.

[0067] The generative AI can automatically translate supplementary items in different languages, making it possible to accommodate international users. For example, if a user asks, "What books do you recommend?", the generative AI can display supplementary items in multiple languages ​​and allow the user to select one. This allows the generative AI to automatically translate supplementary items in different languages, making it possible to accommodate international users.

[0068] The generation AI can use the emotion estimation function to analyze the emotions of the user when entering supplementary items in real time and suggest supplementary items that elicit positive emotions. For example, the generation AI can use the emotion estimation function to analyze the emotions of the user when entering supplementary items in real time and suggest supplementary items that elicit positive emotions. For example, when a user asks, "What books do you recommend?", the generation AI can suggest supplementary items that elicit positive emotions. This makes it possible to analyze the user's emotions in real time and suggest supplementary items that elicit positive emotions.

[0069] The generative AI can analyze the user's input and optimize its answers in real time based on the input. The generative AI can, for example, analyze the user's input and optimize its answers in real time based on that data. For example, if a user asks, "What books do you recommend?" and enters "mystery" and "after 2020" as supplementary items, the generative AI's answers will be optimized based on that information. This allows the generative AI's answers to be optimized in real time based on the user's input.

[0070] The generation AI evaluates the generation AI's response to the user's input and can improve its next response based on the evaluation results. The generation AI, for example, evaluates the generation AI's response to the user's input and improves its next response based on the evaluation results. For example, if a user asks, "What books do you recommend?" and the generation AI's response is insufficient, the generation AI can improve its next response based on the evaluation results. This allows the generation AI to evaluate its response to the user's input and improve its next response.

[0071] The generation AI can use the emotion estimation function to analyze the emotion a user is feeling when they enter text and generate an answer that matches that emotion. For example, the generation AI can use the emotion estimation function to build a system that analyzes the emotion a user is feeling when they enter text and generates an answer that matches that emotion. For example, when a user asks, "What books do you recommend?" and has positive emotions, an answer that matches that emotion can be generated. This allows the generation of answers that match the user's emotions and improves satisfaction.

[0072] The generative AI can analyze the user's input and automatically provide related additional information. For example, the generative AI can analyze the user's input and automatically provide related additional information based on that data. For example, if a user asks, "What books do you recommend?" and enters "mystery" and "after 2020" as supplementary items, related additional information can be provided based on that information. This makes it possible to automatically provide related additional information based on the user's input.

[0073] Generative AI can utilize knowledge from different fields to generate answers from multiple perspectives to the user's input. For example, if a user asks, "What books do you recommend?", generative AI can generate an answer by utilizing knowledge from different fields such as literature, history, and science. This makes it possible to utilize knowledge from different fields to generate answers from multiple perspectives.

[0074] The generative AI can use its emotion estimation function to analyze the emotions of users when they enter text in real time and generate answers that elicit positive emotions. For example, the generative AI can use its emotion estimation function to analyze the emotions of users when they enter text in real time and generate answers that elicit positive emotions. For example, when a user asks, "What books do you recommend?", it generates an answer that elicits positive emotions. This makes it possible to analyze the user's emotions in real time and generate answers that elicit positive emotions.

[0075] The generative AI continuously analyzes the user's questions and the generative AI's answers, and can improve the accuracy of the generative AI's answers through a feedback loop. For example, the generative AI continuously analyzes the user's questions and the generative AI's answers, and builds a feedback loop based on that data. For example, if a user asks, "What books do you recommend?" and the generative AI's answer is insufficient, the data can be used to improve the next answer. In this way, the generative AI continuously analyzes the user's questions and the generative AI's answers, and can improve the accuracy of the answer through a feedback loop.

[0076] The generative AI can dynamically adjust its answering algorithm based on the user's satisfaction rating. For example, the generative AI builds a system that dynamically adjusts the generative AI's answering algorithm based on the user's satisfaction rating. For example, if a user asks, "What books do you recommend?" and the satisfaction level with the generative AI's answer is low, the algorithm is adjusted based on that rating. This allows the generative AI's answering algorithm to be dynamically adjusted based on the user's satisfaction rating.

[0077] The generation AI can use the emotion estimation function to analyze the emotions of the user when rating satisfaction and generate feedback based on those emotions. For example, the generation AI can use the emotion estimation function to build a system that analyzes the emotions of the user when rating satisfaction and generates feedback based on those emotions. For example, if a user asks, "What books do you recommend?" and has positive emotions about the generation AI's answer, feedback based on those emotions is generated. This makes it possible to analyze the emotions of the user when rating satisfaction and generate feedback based on those emotions.

[0078] Generative AI can compare questions and answers from different users and extract common feedback patterns. For example, generative AI can build a system that compares questions and answers from different users and extracts common feedback patterns. For example, if a user asks, "What books do you recommend?", it compares similar questions and answers from other users and extracts common feedback patterns. This makes it possible to compare questions and answers from different users and extract common feedback patterns.

[0079] Generative AI can analyze questions and answers from different fields and utilize knowledge from different fields to optimize feedback loops. For example, generative AI can analyze questions and answers from different fields and utilize that knowledge to build a system that optimizes feedback loops. For example, if a user asks, "What books do you recommend?", the feedback loop can be optimized based on knowledge from different fields. This makes it possible to optimize feedback loops by utilizing knowledge from different fields.

[0080] The generative AI can use the emotion estimation function to analyze the user's emotions when rating satisfaction in real time and generate feedback that elicits positive emotions. For example, the generative AI can use the emotion estimation function to build a system that analyzes the user's emotions when rating satisfaction in real time and generates feedback that elicits positive emotions. For example, if a user asks, "What book do you recommend?" and has positive emotions about the generative AI's answer, feedback based on that emotion can be generated. This makes it possible to analyze the user's emotions when rating satisfaction in real time and generate feedback that elicits positive emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] In response to a user's question, generative AI can automatically search for relevant news articles and academic papers and provide them as supplementary information. For example, if a user asks, "Please tell me about the latest AI technology," generative AI will search for the latest related news articles and academic papers and provide links to them. This allows the user to gain a deeper understanding of the question. Similarly, if a user asks, "What books do you recommend?" generative AI can provide reviews and author interviews related to that book. Furthermore, if a user asks, "I would like to know more about a specific disease," generative AI can provide relevant medical papers and expert opinions.

[0083] Generative AI can suggest related video content in response to user questions. For example, if a user asks, "What movies do you recommend?", generative AI can suggest trailers and review videos of related movies. This allows users to deepen their understanding of the question through visual information. Also, if a user asks, "Please tell me the recipe for a specific dish," generative AI can suggest videos that explain how to make that dish. Furthermore, if a user asks, "I would like to learn about a specific technology," generative AI can suggest educational videos related to that technology.

[0084] In response to a user's question, the generative AI can provide links to related community forums and Q&A sites. For example, if a user asks "A question about a specific programming language," the generative AI can provide links to related programming forums and Q&A sites. This allows the user to interact with other experts and people with the same interests and gain diverse perspectives on their question. Also, if a user asks "I want to know information about a specific disease," the generative AI can provide links to related medical forums and Q&A sites. Furthermore, if a user asks "I want to know information about a specific hobby," the generative AI can provide links to community forums related to that hobby.

[0085] Using its emotion estimation function, the generative AI can analyze the user's emotions when asking a question and adjust the tone of the response accordingly. For example, if a user asks, "I'm feeling stressed at work. What should I do?", the generative AI can analyze the user's emotions and suggest stress relief methods in a gentle tone. This makes the user more likely to accept the answer to their question. Also, if a user asks, "I want to find a new hobby," the generative AI can suggest hobbies in a fun tone that elicits positive emotions. Furthermore, if a user asks, "I want to learn about a specific technology," the generative AI can provide an answer in an excited tone that piques the user's interest.

[0086] In response to a user's question, generative AI can provide summaries of related books and papers. For example, if a user asks, "Tell me about a particular historical event," generative AI can provide summaries of related books and papers. This allows the user to quickly grasp the overview of the question. Also, if a user asks, "I want to know about a particular scientific theory," generative AI can provide summaries of papers related to that theory. Furthermore, if a user asks, "I want to learn about a particular business strategy," generative AI can provide summaries of books related to that strategy.

[0087] Using its emotion estimation function, the generative AI can analyze the user's emotions when asking a question and suggest relevant entertainment content based on those emotions. For example, if a user asks, "I'm feeling down. Please tell me something fun to do," the generative AI can analyze the user's emotions and suggest movies or music to lift their spirits. This allows the user to improve their mood through answers to questions. Also, if a user asks, "I want to relax," the generative AI can suggest relaxing music or meditation guides. Furthermore, if a user asks, "I want to find a new hobby," the generative AI can suggest entertainment content that will pique the user's interest.

[0088] Generative AI can provide relevant statistical data and graphs in response to user questions. For example, if a user asks, "Please tell me about a specific economic indicator," the generative AI will provide relevant statistical data and graphs. This allows the user to visually understand the specific data in response to the question. Also, if a user asks, "I would like to know about a specific market trend," the generative AI can provide statistical data and graphs related to that market. Furthermore, if a user asks, "I would like to know about a specific health indicator," the generative AI can provide statistical data and graphs related to that health indicator.

[0089] Using its emotion estimation function, the generative AI can analyze the user's emotions when asking a question and provide reminders and notifications based on those emotions. For example, if a user asks, "I'm feeling stressed. What should I do?", the generative AI can analyze the user's emotions and provide reminders and notifications to relax. This allows the user to reduce stress through answers to questions. Also, if a user asks, "I want to live a healthy life," the generative AI can provide reminders and notifications to maintain healthy lifestyle habits. Furthermore, if a user asks, "I want to learn a new skill," the generative AI can provide reminders and notifications to learn that skill.

[0090] Generative AI can provide relevant expert opinions and interviews in response to user questions. For example, if a user asks, "I would like to know information about a specific medical issue," generative AI can provide opinions and interviews of relevant medical experts. This allows the user to obtain reliable information in response to their question. Also, if a user asks, "I would like to learn about a specific technology," generative AI can provide expert opinions and interviews on that technology. Furthermore, if a user asks, "I would like to know about a specific business strategy," generative AI can provide opinions and interviews of business experts on that strategy.

[0091] Using its emotion estimation function, the generative AI can analyze the user's emotions when asking a question and suggest a personalized study plan based on those emotions. For example, if a user asks, "I want to learn a new skill," the generative AI can analyze the user's emotions and suggest a study plan that elicits positive emotions. This allows the user to increase their motivation to study through their answers to questions. Also, if a user asks, "I want to pass a specific exam," the generative AI can suggest a personalized study plan for that exam. Furthermore, if a user asks, "I want to learn a new language," the generative AI can suggest a personalized study plan for learning that language.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The generating AI learns from user questions. For example, the generating AI analyzes the questions users ask the generating AI and learns what questions elicit better answers. The generating AI also evaluates its answers to user questions and learns what supplementary information will result in more specific and satisfying answers. Step 2: The prompt display unit displays the supplementary items learned by the generation AI. For example, if a user asks, "What books do you recommend?", the prompt display unit displays supplementary items such as "genre," "author," and "year of publication." Step 3: The user input unit accepts the user's input of the supplementary items displayed by the prompt display unit. For example, if the user inputs "mystery" and "after 2020," the user input unit accepts this information.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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. Note that 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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. Generative AI and a prompt display section; a user input unit; The generated AI is Learn user questions, The prompt display unit Displaying the supplementary items learned by the generation AI, The user input unit and accepting input of the supplementary items displayed by the prompt display unit by the user. A system characterized by:

2. The generated AI is Evaluate the quality of the AI's answers to user questions, and automatically retrain if the quality of the answers is low.

2. The system of claim 1.

3. The generated AI is In response to a user's question, the system refers to related past questions and their answers and suggests the supplementary items.

2. The system of claim 1.

4. The generated AI is In response to the user's question, a plurality of supplementary items are displayed in order of priority, making it easier for the user to select.

2. The system of claim 1.

5. The generated AI is Analyze user input and optimize the AI's response in real time based on the input.

2. The system of claim 1.

6. The generated AI is The user's questions and the AI's answers are continuously analyzed, and the AI's accuracy is improved through a feedback loop.

2. The system of claim 1.

7. The generated AI is Using an emotion estimation function, the emotion of the user when asking a question is analyzed and the supplementary items are suggested according to the emotion.

2. The system of claim 1.

8. The generated AI is By using an emotion estimation function, the supplementary items are dynamically changed according to the user's emotions, thereby improving the user's satisfaction.

2. The system of claim 1.

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