System

Abstract

An object of the system according to the embodiment is to efficiently summarize and edit user's opinions and questions and provide appropriate feedback.SOLUTION: A system according to an embodiment includes an opinion acquisition unit, a summary editing unit, and a feedback providing unit. The opinion acquisition unit acquires an opinion or a question of a user. The summary editor summarizes and edits the opinion and the question acquired by the opinion acquirer. The feedback providing unit provides feedback to the user based on the content summarized and edited by the summary editing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of efficiently summarizing and editing user opinions and questions and providing appropriate feedback.

[0005] The system according to the embodiment aims to efficiently summarize and edit user opinions and questions and provide appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes an opinion acquisition unit, a summary editing unit, and a feedback providing unit. The opinion acquisition unit acquires opinions and questions from users. The summary editing unit summarizes and edits the opinions and questions acquired by the opinion acquisition unit. The feedback providing unit provides feedback to the user based on the content summarized and edited by the summary editing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently summarize and edit user opinions and questions and provide appropriate feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The suggestion box system according to an embodiment of the present invention is a system in which a generation AI summarizes and edits user opinions and questions, and converts them into a format that can be easily understood and responded to by an administrator. This enables the suggestion box system to efficiently acquire, manage, and respond to user opinions and questions.

[0029] A suggestion box system according to an embodiment includes an opinion acquisition unit, a summary editing unit, and a feedback providing unit. The opinion acquisition unit acquires user opinions and questions. For example, the user inputs opinions in text format. The opinion acquisition unit can also support voice input and handwritten input. The summary editing unit uses a generation AI to summarize and edit the opinions and questions acquired by the opinion acquisition unit. For example, the generation AI concisely summarizes the opinions and questions using a text generation AI (e.g., LLM). The generation AI can also summarize the content of the opinions and questions using a multimodal generation AI. The generation AI can also extract and summarize important parts of a sentence. The feedback providing unit provides feedback to the user based on the content summarized and edited by the summary editing unit. For example, the generation AI automatically generates feedback and sends it to the user. This allows the suggestion box system according to an embodiment to efficiently acquire, summarize, edit, and provide feedback on user opinions and questions.

[0030] The opinion acquisition unit can analyze the input content in real time and suggest appropriate question and opinion formats. For example, the generation AI in the opinion acquisition unit analyzes the user's input content in real time and suggests appropriate question and opinion formats. For example, when a user starts to input "The school lunch is not tasty," the generation AI will suggest "Which menu items specifically are not tasty?" Also, when a user starts to input "The books in the library are old," the generation AI will analyze in real time and suggest a format such as "What genre of books would you like us to update?" Also, the generation AI will analyze the user's input content in real time and suggest an appropriate format. For example, when a user starts to input "I don't understand how to use the suggestion box," the generation AI will suggest "What specific parts are difficult to understand?" In this way, by analyzing the user's input content in real time and suggesting an appropriate format, the quality of opinions and questions is improved.

[0031] The opinion acquisition unit can refer to the input history and automatically complete content that is highly relevant to past opinions and questions. For example, the generation AI in the opinion acquisition unit refers to the user's past input history and automatically completes content that is highly relevant. For example, if the user previously input "I would like the school lunch menu to be improved," the generation AI will newly complete "Whether the taste of the school lunch has improved." Also, if the user previously input "I would like the library books to be updated," the generation AI will refer to that history and automatically complete "Opinions about new book genres." Also, the generation AI will refer to the user's past input history and automatically complete content that is highly relevant. For example, if the user previously input "It's difficult to understand how to use the suggestion box," the generation AI will newly complete "specific areas for improvement." In this way, by referring to the user's past input history and completing content that is highly relevant, more specific opinions and questions can be acquired.

[0032] The opinion acquisition unit also supports voice and handwritten input, which the generation AI converts into text for summarization and editing. For example, if a user voice-inputs, "The school lunch is not tasty," the generation AI converts this into text and summarizes it as, "I hope the school lunch menu will be improved." Handwritten input is also converted into text, which the generation AI summarizes and edits. For example, if a user handwrites, "The books in the library are old," the generation AI converts this into text and summarizes it as, "I hope the library books will be updated." The generation AI also converts voice and handwritten input into text, which the AI ​​summarizes and edits. For example, if a user voice-inputs, "I don't understand how to use the suggestion box," the generation AI converts this into text and summarizes it as, "I hope the suggestion box will be improved." This improves user convenience by supporting voice and handwritten input.

[0033] The opinion acquisition unit automatically translates opinions and questions entered in different languages, and the generation AI can perform multilingual summarization and editing. For example, if a user enters "The school lunch is not tasty" in English, the generation AI translates it and summarizes it as "I hope the school lunch menu will be improved." Multilingual summarization and editing is also performed. For example, if a user enters "Les livres de la bibliotheque sont vieux" in French, the generation AI translates it and summarizes it as "I hope the library books will be updated." The generation AI also automatically translates, summarizes, and edits opinions and questions entered in different languages. For example, if a user enters "The usage method of the suggestion box is not clear" in Chinese, the generation AI translates it and summarizes it as "I hope the usage of the suggestion box will be improved." This allows automatic translation of opinions and questions entered in different languages ​​and multilingual summarization and editing, making it possible to accommodate international users.

[0034] The summary editing unit can automatically tag summarized and edited content and group related opinions and questions. For example, the generation AI automatically tags summarized and edited content and groups related opinions and questions. For example, tags such as "improving school lunches" and "updating the library" are added. The automatic tagging function also groups summarized and edited content. For example, a tag such as "how to use the suggestion box" is added to group related opinions. The generation AI also automatically tags summarized and edited content and groups related opinions and questions. For example, a tag such as "improving school facilities" is added to group related opinions. This allows administrators to respond efficiently by automatically tagging and grouping related opinions and questions.

[0035] The summary editing unit can synchronize the summaries and edited content across different devices (smartphones, tablets, PCs), allowing administrators to access the content from anywhere. For example, the summary editing unit synchronizes the summaries and edited content created by the generation AI across different devices, allowing administrators to access the content from anywhere. For example, opinions and questions can be checked on a smartphone or tablet. The summary editing unit can also synchronize the summaries and edited content created by the generation AI across different devices, allowing administrators to access the content from anywhere. For example, opinions and questions can be checked on a tablet and responded to on a smartphone. This allows administrators to access the content from anywhere by synchronizing it across different devices.

[0036] The summary editing unit can link the summarized and edited content with other management systems (for example, CRM or ERP) and manage it in an integrated manner. The summary editing unit, for example, links the content summarized and edited by the generation AI with a CRM system and manages it in an integrated manner. For example, it automatically registers customer opinions and questions in CRM. It also links with other management systems (for example, ERP) and manages the summarized and edited content in an integrated manner. For example, it automatically reflects opinions and questions in an ERP system. It also links the summarized and edited content by the generation AI with other management systems and manages it in an integrated manner. For example, it automatically registers opinions and questions in a project management system. This allows it to link with other management systems and manage it in an integrated manner.

[0037] The feedback providing unit can provide personalized feedback based on the user's past opinions and questions. For example, the generation AI provides personalized feedback by referring to the user's past opinions and questions. For example, if the user previously input "I would like the school lunch menu to be improved," the generation AI will provide new feedback of "specific menu suggestions." The generation AI also provides personalized feedback based on the user's past opinions and questions. For example, if the user previously input "I would like the library books to be updated," the generation AI will provide new feedback of "specific genre suggestions." The generation AI also provides personalized feedback by referring to the user's past opinions and questions. For example, if the user previously input "I find it difficult to use the suggestion box," the generation AI will provide new feedback of "specific suggestions for improvement." This makes it possible to provide more specific responses by providing personalized feedback based on the user's past opinions and questions.

[0038] The feedback providing unit can automatically attach relevant reference materials and links when providing feedback. For example, the feedback providing unit automatically attaches relevant reference materials and links when the generation AI provides feedback. For example, if a user inputs "I would like the school lunch menu to be improved," links to related recipes and nutritional information are attached. In addition, relevant reference materials and links are automatically attached to the feedback, allowing the user to easily obtain detailed information. For example, if a user inputs "I would like the library books to be updated," links to a list of new books and reviews are attached. In addition, when the generation AI provides feedback, relevant reference materials and links are automatically attached. For example, if a user inputs "I find it difficult to use the suggestion box," links to a usage guide and FAQ are attached. In this way, by automatically attaching relevant reference materials and links, the user can easily obtain detailed information.

[0039] The feedback providing unit can provide feedback not only in text but also in audio or video messages. For example, the generation AI provides feedback not only in text but also in audio or video messages. For example, if a user inputs, "I would like the school lunch menu improved," the feedback unit provides a voice message saying, "Thank you for your opinion. We will consider improving the school lunch menu." Feedback can also be provided in audio or video messages to provide more diverse responses to users. For example, if a user inputs, "I would like the library books updated," the feedback unit provides a video message saying, "Thank you for your opinion. We will consider adding new books." Feedback can also be provided in text but also in audio or video messages. For example, if a user inputs, "I find the suggestion box difficult to use," the feedback unit provides a voice message saying, "Thank you for your opinion. We will consider improving how it is used." Providing feedback in audio or video messages in this way provides more diverse responses to users.

[0040] The feedback providing unit can automatically generate feedback in different languages to achieve multi-language support. For example, the generating AI can automatically generate feedback in different languages to achieve multi-language support. For example, when the user inputs "The school lunch is not tasty" in English, the generating AI provides feedback in Japanese as "Thank you for your opinion. We will consider improving the school lunch." Also, it can automatically generate feedback in different languages to achieve multi-language support. For example, when the user inputs "Les livres de la bibliotheque sont vieux" in French, the generating AI provides feedback in English as "Thank you for your feedback. We will consider updating the library books." Also, the generating AI can automatically generate feedback in different languages to achieve multi-language support. For example, when the user inputs "The usage method of the suggestion box is not clear" in Chinese, the generating AI provides feedback in Japanese as "Thank you for your opinion. We will consider improving the usage method of the suggestion box." Thus, by automatically generating feedback in different languages, multi-language support is achieved.

[0041] The system according to the embodiment is not limited to the above examples, and various changes are possible, for example, as follows.

[0042] The opinion acquisition unit can analyze input content in real time and suggest appropriate question and opinion formats. For example, if a user starts to type, "The school lunch is not tasty," it will suggest, "Which specific menu item is not tasty?" Similarly, if a user starts to type, "The books in the library are old," the generation AI will analyze in real time and suggest a format such as, "What genre of books would you like us to update?" The generation AI can also analyze user input in real time and suggest an appropriate format. For example, if a user starts to type, "I don't understand how to use the suggestion box," it will suggest, "Which specific part is difficult to understand?" This improves the quality of opinions and questions by analyzing user input in real time and suggesting an appropriate format.

[0043] The opinion acquisition unit can reference input history and automatically complete content that is highly relevant to past opinions and questions. For example, if a user previously input "I would like the school lunch menu to be improved," the new input would be "Whether the taste of the school lunch has improved." Also, if a user previously input "I would like the library books to be updated," the generation AI would reference that history and automatically complete "Opinions about new book genres." The generation AI also references the user's past input history and automatically completes content that is highly relevant. For example, if a user previously input "It's difficult to understand how to use the suggestion box," the new input would be "Specific areas for improvement." In this way, by referencing the user's past input history and completing content that is highly relevant, more specific opinions and questions can be acquired.

[0044] The opinion acquisition unit also supports voice and handwritten input, which the generation AI converts into text and summarizes and edits. For example, the generation AI converts voice input into text and summarizes and edits it. For example, if a user voice-inputs, "The school lunch is not tasty," the generation AI converts this into text and summarizes it as, "I hope the school lunch menu will be improved." Handwritten input is also converted into text and summarized and edited by the generation AI. For example, if a user hand-inputs, "The books in the library are old," the generation AI converts this into text and summarizes it as, "I hope the library books will be updated." The generation AI also converts voice and handwritten input into text and summarizes and edits it. For example, if a user voice-inputs, "I don't understand how to use the suggestion box," the generation AI converts this into text and summarizes it as, "I hope the suggestion box will be improved." This improves user convenience by supporting voice and handwritten input.

[0045] The opinion acquisition unit automatically translates opinions and questions entered in different languages, and the generation AI can perform multilingual summarization and editing. For example, if a user enters "The school lunch is not tasty" in English, the generation AI translates it and summarizes it as "I hope the school lunch menu will be improved." Multilingual summarization and editing is also performed. For example, if a user enters "Les livres de la bibliotheque sont vieux" in French, the generation AI translates it and summarizes it as "I hope the library books will be updated." The generation AI also automatically translates, summarizes, and edits opinions and questions entered in different languages. For example, if a user enters "The usage method of the suggestion box is not clear" in Chinese, the generation AI translates it and summarizes it as "I hope the usage of the suggestion box will be improved." This allows automatic translation of opinions and questions entered in different languages ​​and multilingual summarization and editing, making it possible to accommodate international users.

[0046] The summary editing department can automatically tag summarized and edited content and group related opinions and questions. For example, the generation AI can automatically tag summarized and edited content and group related opinions and questions. For example, it can add tags such as "improving school lunches" and "updating the library." The automatic tagging function can also be used to group summarized and edited content. For example, it can add a tag such as "how to use the suggestion box" to group related opinions. The generation AI can also automatically tag summarized and edited content and group related opinions and questions. For example, it can add a tag such as "improving school facilities" to group related opinions into one group. This allows administrators to respond efficiently by automatically tagging and grouping related opinions and questions.

[0047] The summary editing department can synchronize the summaries and edited content across different devices (smartphones, tablets, PCs), allowing administrators to access them from anywhere. For example, the generation AI can synchronize the summaries and edited content across different devices, allowing administrators to access them from anywhere. For example, opinions and questions can be checked on a smartphone or tablet. The generation AI can also synchronize the summaries and edited content across different devices, allowing administrators to access them from anywhere. For example, content edited on a PC can be checked on a smartphone. The generation AI can also synchronize the summaries and edited content across different devices, allowing administrators to access them from anywhere. For example, opinions and questions can be checked on a tablet and responded to on a smartphone. This allows administrators to access the content from anywhere by synchronizing it across different devices.

[0048] The summary editing department can link the summarized and edited content with other management systems (for example, CRM or ERP) and manage it in an integrated manner. For example, the content summarized and edited by the generation AI can be linked with a CRM system and managed in an integrated manner. For example, customer opinions and questions can be automatically registered in CRM. The summary and edited content can also be linked with other management systems (for example, ERP) and managed in an integrated manner. For example, opinions and questions can be automatically reflected in the ERP system. The summary and edited content can also be linked with other management systems and managed in an integrated manner. For example, opinions and questions can be automatically registered in a project management system. This allows for integrated management by linking with other management systems.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The opinion acquisition unit acquires the user's opinions and questions. For example, the user inputs their opinions in text format. The opinion acquisition unit can also handle voice input and handwritten input. Step 2: The summary editor uses the generation AI to summarize and edit the opinions and questions acquired by the opinion acquisition unit. For example, the generation AI may use a text generation AI (e.g., LLM) to concisely summarize the opinions and questions. The generation AI may also use a multimodal generation AI to summarize the content of the opinions and questions. The generation AI may also extract and summarize important parts of the text. Step 3: The feedback provider provides feedback to the user based on the content summarized and edited by the summary editor. For example, the generation AI automatically generates feedback and sends it to the user.

[0051] (Example 2) The suggestion box system according to an embodiment of the present invention is a system in which a generation AI summarizes and edits user opinions and questions, and converts them into a format that can be easily understood and responded to by an administrator. This enables the suggestion box system to efficiently acquire, manage, and respond to user opinions and questions.

[0052] A suggestion box system according to an embodiment includes an opinion acquisition unit, a summary editing unit, and a feedback providing unit. The opinion acquisition unit acquires user opinions and questions. For example, the user inputs opinions in text format. The opinion acquisition unit can also support voice input and handwritten input. The summary editing unit uses a generation AI to summarize and edit the opinions and questions acquired by the opinion acquisition unit. For example, the generation AI concisely summarizes the opinions and questions using a text generation AI (e.g., LLM). The generation AI can also summarize the content of the opinions and questions using a multimodal generation AI. The generation AI can also extract and summarize important parts of a sentence. The feedback providing unit provides feedback to the user based on the content summarized and edited by the summary editing unit. For example, the generation AI automatically generates feedback and sends it to the user. This allows the suggestion box system according to an embodiment to efficiently acquire, summarize, edit, and provide feedback on user opinions and questions.

[0053] The opinion acquisition unit can analyze the input content, infer the emotion at the time of input, and adjust the tone of the summary based on the emotion. For example, the generation AI in the opinion acquisition unit analyzes the user's input content and infers the emotion at the time of input. For example, if a user inputs, "The school lunches these days aren't tasty," the emotion inference function detects the emotion of dissatisfaction and adjusts the tone of the summary. Similarly, if a user inputs, "The books in the school library are old, so I'd like new books," the generation AI infers the emotion and adjusts the tone of the summary. For example, if the emotion is positive, the summary is translated as, "I hope the library books will be updated." The generation AI also analyzes the user's input content and summarizes it based on the emotion using the emotion inference function. For example, if a user inputs, "I don't understand how to use the suggestion box," if the emotion is negative, the summary is translated as, "I hope the suggestion box will be improved." This allows for more appropriate feedback by adjusting the tone of the summary based on the user's emotion.

[0054] The opinion acquisition unit can analyze the input content in real time and suggest appropriate question and opinion formats. For example, the generation AI in the opinion acquisition unit analyzes the user's input content in real time and suggests appropriate question and opinion formats. For example, when a user starts to input "The school lunch is not tasty," the generation AI will suggest "Which menu items specifically are not tasty?" Also, when a user starts to input "The books in the library are old," the generation AI will analyze in real time and suggest a format such as "What genre of books would you like us to update?" Also, the generation AI will analyze the user's input content in real time and suggest an appropriate format. For example, when a user starts to input "I don't understand how to use the suggestion box," the generation AI will suggest "What specific parts are difficult to understand?" In this way, by analyzing the user's input content in real time and suggesting an appropriate format, the quality of opinions and questions is improved.

[0055] The opinion acquisition unit can refer to the input history and automatically complete content that is highly relevant to past opinions and questions. For example, the generation AI in the opinion acquisition unit refers to the user's past input history and automatically completes content that is highly relevant. For example, if the user previously input "I would like the school lunch menu to be improved," the generation AI will newly complete "Whether the taste of the school lunch has improved." Also, if the user previously input "I would like the library books to be updated," the generation AI will refer to that history and automatically complete "Opinions about new book genres." Also, the generation AI will refer to the user's past input history and automatically complete content that is highly relevant. For example, if the user previously input "It's difficult to understand how to use the suggestion box," the generation AI will newly complete "specific areas for improvement." In this way, by referring to the user's past input history and completing content that is highly relevant, more specific opinions and questions can be acquired.

[0056] The opinion acquisition unit also supports voice and handwritten input, which the generation AI converts into text for summarization and editing. For example, if a user voice-inputs, "The school lunch is not tasty," the generation AI converts this into text and summarizes it as, "I hope the school lunch menu will be improved." Handwritten input is also converted into text, which the generation AI summarizes and edits. For example, if a user handwrites, "The books in the library are old," the generation AI converts this into text and summarizes it as, "I hope the library books will be updated." The generation AI also converts voice and handwritten input into text, which the AI ​​summarizes and edits. For example, if a user voice-inputs, "I don't understand how to use the suggestion box," the generation AI converts this into text and summarizes it as, "I hope the suggestion box will be improved." This improves user convenience by supporting voice and handwritten input.

[0057] The opinion acquisition unit automatically translates opinions and questions entered in different languages, and the generation AI can perform multilingual summarization and editing. For example, if a user enters "The school lunch is not tasty" in English, the generation AI translates it and summarizes it as "I hope the school lunch menu will be improved." Multilingual summarization and editing is also performed. For example, if a user enters "Les livres de la bibliotheque sont vieux" in French, the generation AI translates it and summarizes it as "I hope the library books will be updated." The generation AI also automatically translates, summarizes, and edits opinions and questions entered in different languages. For example, if a user enters "The usage method of the suggestion box is not clear" in Chinese, the generation AI translates it and summarizes it as "I hope the usage of the suggestion box will be improved." This allows automatic translation of opinions and questions entered in different languages ​​and multilingual summarization and editing, making it possible to accommodate international users.

[0058] The opinion acquisition unit can use the emotion estimation function to analyze the user's emotions in real time as they enter text and provide input support to elicit positive emotions. For example, the generation AI in the opinion acquisition unit uses the emotion estimation function to analyze the user's emotions in real time as they enter text and provide input support to elicit positive emotions. For example, when a user begins to enter, "The school lunches are not tasty," the generation AI suggests, "Please tell us specific improvements." When a user begins to enter, "The books in the library are old," the generation AI analyzes the user's emotions and provides positive suggestions such as, "What genre of books would you like us to update?" The generation AI also uses the emotion estimation function to analyze the user's emotions in real time as they enter text and provide input support to elicit positive emotions. For example, when a user begins to enter, "I don't understand how to use the suggestion box," the generation AI suggests, "What specific part is difficult to understand?" This allows the system to analyze the user's emotions in real time and elicit positive emotions, thereby encouraging more constructive opinions and questions.

[0059] The summary editor can use the emotion estimation function to classify the summarized and edited content based on emotional value, allowing the administrator to prioritize review of emotionally important opinions. For example, the summary editor can classify the content summarized and edited by the generation AI using the emotion estimation function and prioritize displaying emotionally important opinions. For example, opinions with strong positive emotions can be prioritized and notified to the administrator. The emotion estimation function can also be used to classify the summarized and edited content based on emotional value, allowing the administrator to prioritize review of important opinions. For example, opinions with strong negative emotions can be addressed quickly. The generation AI can also use the emotion estimation function to classify the summarized and edited content based on emotional value, allowing the administrator to prioritize review of emotionally important opinions. For example, opinions with high emotion scores can be displayed preferentially. This allows the administrator to respond quickly by prioritizing review of emotionally important opinions.

[0060] The summary editing unit can automatically tag summarized and edited content and group related opinions and questions. For example, the generation AI automatically tags summarized and edited content and groups related opinions and questions. For example, tags such as "improving school lunches" and "updating the library" are added. The automatic tagging function also groups summarized and edited content. For example, a tag such as "how to use the suggestion box" is added to group related opinions. The generation AI also automatically tags summarized and edited content and groups related opinions and questions. For example, a tag such as "improving school facilities" is added to group related opinions. This allows administrators to respond efficiently by automatically tagging and grouping related opinions and questions.

[0061] The summary editing unit can synchronize the summaries and edited content across different devices (smartphones, tablets, PCs), allowing administrators to access the content from anywhere. For example, the summary editing unit synchronizes the summaries and edited content created by the generation AI across different devices, allowing administrators to access the content from anywhere. For example, opinions and questions can be checked on a smartphone or tablet. The summary editing unit can also synchronize the summaries and edited content created by the generation AI across different devices, allowing administrators to access the content from anywhere. For example, opinions and questions can be checked on a tablet and responded to on a smartphone. This allows administrators to access the content from anywhere by synchronizing it across different devices.

[0062] The summary editing unit can link the summarized and edited content with other management systems (for example, CRM or ERP) and manage it in an integrated manner. The summary editing unit, for example, links the content summarized and edited by the generation AI with a CRM system and manages it in an integrated manner. For example, it automatically registers customer opinions and questions in CRM. It also links with other management systems (for example, ERP) and manages the summarized and edited content in an integrated manner. For example, it automatically reflects opinions and questions in an ERP system. It also links the summarized and edited content by the generation AI with other management systems and manages it in an integrated manner. For example, it automatically registers opinions and questions in a project management system. This allows it to link with other management systems and manage it in an integrated manner.

[0063] The summary editing unit can use the emotion estimation function to analyze the emotional reaction of the administrator to the summarized and edited content and prioritize displaying the content that the administrator is most interested in. The summary editing unit, for example, uses the emotion estimation function to analyze the emotional reaction of the administrator to the summarized and edited content and prioritize displaying the content that the administrator is most interested in. For example, opinions to which the administrator has a positive reaction are prioritized. The summary editing unit also analyzes the administrator's emotional reaction and classifies the summarized and edited content based on its emotional value. For example, opinions to which the administrator has a negative reaction are addressed promptly. The emotion estimation function also analyzes the administrator's emotional reaction to the summarized and edited content and prioritize displaying the content that the administrator is most interested in. For example, opinions with a high emotion score are prioritized. This allows the administrator to respond efficiently by analyzing the administrator's emotional reaction and prioritize displaying the content that the administrator is most interested in.

[0064] The feedback providing unit can generate feedback according to the user's emotions using the emotion estimation function. For example, the generation AI in the feedback providing unit estimates the user's emotions and generates feedback accordingly. For example, if a user inputs "The school lunch is not tasty," if the emotion is negative, the feedback is "Thank you for your opinion. We will consider improving the school lunch." Furthermore, the emotion estimation function is used to generate feedback according to the user's emotions. For example, if a user inputs "The books in the library are old," if the emotion is positive, the feedback is "Thank you for your opinion. We will consider adding new books." Furthermore, the generation AI estimates the user's emotions and generates feedback according to them. For example, if a user inputs "It's difficult to understand how to use the suggestion box," if the emotion is negative, the feedback is "Thank you for your opinion. We will consider improving how it's used." This allows for more appropriate responses by generating feedback according to the user's emotions.

[0065] The feedback providing unit can provide personalized feedback based on the user's past opinions and questions. For example, the generation AI provides personalized feedback by referring to the user's past opinions and questions. For example, if the user previously input "I would like the school lunch menu to be improved," the generation AI will provide new feedback of "specific menu suggestions." The generation AI also provides personalized feedback based on the user's past opinions and questions. For example, if the user previously input "I would like the library books to be updated," the generation AI will provide new feedback of "specific genre suggestions." The generation AI also provides personalized feedback by referring to the user's past opinions and questions. For example, if the user previously input "I find it difficult to use the suggestion box," the generation AI will provide new feedback of "specific suggestions for improvement." This makes it possible to provide more specific responses by providing personalized feedback based on the user's past opinions and questions.

[0066] The feedback providing unit can automatically attach relevant reference materials and links when providing feedback. For example, the feedback providing unit automatically attaches relevant reference materials and links when the generation AI provides feedback. For example, if a user inputs "I would like the school lunch menu to be improved," links to related recipes and nutritional information are attached. In addition, relevant reference materials and links are automatically attached to the feedback, allowing the user to easily obtain detailed information. For example, if a user inputs "I would like the library books to be updated," links to a list of new books and reviews are attached. In addition, when the generation AI provides feedback, relevant reference materials and links are automatically attached. For example, if a user inputs "I find it difficult to use the suggestion box," links to a usage guide and FAQ are attached. In this way, by automatically attaching relevant reference materials and links, the user can easily obtain detailed information.

[0067] The feedback providing unit can provide feedback not only in text but also in audio or video messages. For example, the generation AI provides feedback not only in text but also in audio or video messages. For example, if a user inputs, "I would like the school lunch menu improved," the feedback unit provides a voice message saying, "Thank you for your opinion. We will consider improving the school lunch menu." Feedback can also be provided in audio or video messages to provide more diverse responses to users. For example, if a user inputs, "I would like the library books updated," the feedback unit provides a video message saying, "Thank you for your opinion. We will consider adding new books." Feedback can also be provided in text but also in audio or video messages. For example, if a user inputs, "I find the suggestion box difficult to use," the feedback unit provides a voice message saying, "Thank you for your opinion. We will consider improving how it is used." Providing feedback in audio or video messages in this way provides more diverse responses to users.

[0068] The feedback providing unit can automatically generate feedback in different languages to achieve multi-language support. For example, a generative AI automatically generates feedback in different languages to achieve multi-language support. For example, when a user inputs "The school lunch is not tasty" in English, the generative AI provides feedback in Japanese as "Thank you for your opinion. We will consider improving the school lunch." Also, it automatically generates feedback in different languages to achieve multi-language support. For example, when a user inputs "Les livres de la bibliotheque sont vieux" in French, the generative AI provides feedback in English as "Thank you for your feedback. We will consider updating the library books." Also, the generative AI automatically generates feedback in different languages to achieve multi-language support. For example, when a user inputs "The usage method of the suggestion box is not clear" in Chinese, the generative AI provides feedback in Japanese as "Thank you for your opinion. We will consider improving the usage method of the suggestion box." Thus, by automatically generating feedback in different languages, multi-language support is achieved.

[0069] The feedback providing unit can use the emotion estimation function to monitor the user's emotional response to the feedback in real time and continuously improve the content of the feedback. The feedback providing unit, for example, uses the emotion estimation function to monitor the user's emotional response to the feedback in real time and continuously improve the content of the feedback. For example, the feedback providing unit monitors the emotional response when the user inputs, "I would like the school lunch menu to be improved," and adjusts the feedback content. The feedback providing unit also monitors the user's emotional response in real time and continuously improves the feedback content. For example, the feedback providing unit monitors the emotional response when the user inputs, "I would like the library books updated," and adjusts the feedback content. The feedback providing unit also uses the emotion estimation function to monitor the user's emotional response to the feedback in real time and continuously improves the feedback content. For example, the feedback providing unit monitors the emotional response when the user inputs, "I don't understand how to use the suggestion box," and adjusts the feedback content. In this way, by monitoring the user's emotional response in real time and continuously improving the feedback content, a more appropriate response is possible.

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

[0071] The opinion acquisition unit analyzes the user's input, infers the emotion at the time of input, and adjusts the tone of the summary based on the emotion. For example, if a user inputs, "The school lunches these days aren't tasty," the emotion inference function detects the emotion of dissatisfaction and adjusts the tone of the summary. Similarly, if a user inputs, "The books in the school library are old, so I'd like new books added," the generation AI infers the emotion and adjusts the tone of the summary. For example, if the emotion is positive, the summary will read, "I'd like the library books updated." The generation AI also analyzes the user's input and uses the emotion inference function to summarize based on the emotion. For example, if a user inputs, "I don't understand how to use the suggestion box," if the emotion is negative, the summary will read, "I'd like the suggestion box to be improved." This allows the system to provide more appropriate feedback by adjusting the tone of the summary based on the user's emotion.

[0072] The opinion acquisition unit can analyze input content in real time and suggest appropriate question and opinion formats. For example, if a user starts to type, "The school lunch is not tasty," it will suggest, "Which specific menu item is not tasty?" Similarly, if a user starts to type, "The books in the library are old," the generation AI will analyze in real time and suggest a format such as, "What genre of books would you like us to update?" The generation AI can also analyze user input in real time and suggest an appropriate format. For example, if a user starts to type, "I don't understand how to use the suggestion box," it will suggest, "Which specific part is difficult to understand?" This improves the quality of opinions and questions by analyzing user input in real time and suggesting an appropriate format.

[0073] The opinion acquisition unit can reference input history and automatically complete content that is highly relevant to past opinions and questions. For example, if a user previously input "I would like the school lunch menu to be improved," the new input would be "Whether the taste of the school lunch has improved." Also, if a user previously input "I would like the library books to be updated," the generation AI would reference that history and automatically complete "Opinions about new book genres." The generation AI also references the user's past input history and automatically completes content that is highly relevant. For example, if a user previously input "It's difficult to understand how to use the suggestion box," the new input would be "Specific areas for improvement." In this way, by referencing the user's past input history and completing content that is highly relevant, more specific opinions and questions can be acquired.

[0074] The opinion acquisition unit also supports voice and handwritten input, which the generation AI converts into text and summarizes and edits. For example, the generation AI converts voice input into text and summarizes and edits it. For example, if a user voice-inputs, "The school lunch is not tasty," the generation AI converts this into text and summarizes it as, "I hope the school lunch menu will be improved." Handwritten input is also converted into text and summarized and edited by the generation AI. For example, if a user hand-inputs, "The books in the library are old," the generation AI converts this into text and summarizes it as, "I hope the library books will be updated." The generation AI also converts voice and handwritten input into text and summarizes and edits it. For example, if a user voice-inputs, "I don't understand how to use the suggestion box," the generation AI converts this into text and summarizes it as, "I hope the suggestion box will be improved." This improves user convenience by supporting voice and handwritten input.

[0075] The opinion acquisition unit automatically translates opinions and questions entered in different languages, and the generation AI can perform multilingual summarization and editing. For example, if a user enters "The school lunch is not tasty" in English, the generation AI translates it and summarizes it as "I hope the school lunch menu will be improved." Multilingual summarization and editing is also performed. For example, if a user enters "Les livres de la bibliotheque sont vieux" in French, the generation AI translates it and summarizes it as "I hope the library books will be updated." The generation AI also automatically translates, summarizes, and edits opinions and questions entered in different languages. For example, if a user enters "The usage method of the suggestion box is not clear" in Chinese, the generation AI translates it and summarizes it as "I hope the usage of the suggestion box will be improved." This allows automatic translation of opinions and questions entered in different languages ​​and multilingual summarization and editing, making it possible to accommodate international users.

[0076] The opinion acquisition unit uses the emotion estimation function to analyze the user's emotions in real time as they type, providing input support to elicit positive emotions. For example, the generation AI uses the emotion estimation function to analyze the user's emotions in real time as they type, providing input support to elicit positive emotions. For example, when a user begins to type, "The school lunches are not tasty," the generation AI suggests, "Please tell us specific improvements." When a user begins to type, "The books in the library are old," the generation AI analyzes the user's emotions and makes positive suggestions, such as, "What genre of books would you like us to update?" The generation AI also uses the emotion estimation function to analyze the user's emotions in real time as they type, providing input support to elicit positive emotions. For example, when a user begins to type, "I don't understand how to use the suggestion box," the generation AI suggests, "What specific part is difficult to understand?" This allows the system to analyze the user's emotions in real time and elicit positive emotions, encouraging more constructive opinions and questions.

[0077] The summary editor can use the emotion estimation function to classify the summarized and edited content based on its emotional value, allowing administrators to prioritize review of emotionally important opinions. For example, the generation AI can classify the summarized and edited content using the emotion estimation function, and prioritize displaying emotionally important opinions. For example, opinions with strong positive emotions can be prioritized and notified to the administrator. The emotion estimation function can also be used to classify the summarized and edited content based on its emotional value, allowing administrators to prioritize review of important opinions. For example, opinions with strong negative emotions can be addressed quickly. The generation AI can also use the emotion estimation function to classify the summarized and edited content based on its emotional value, allowing administrators to prioritize review of emotionally important opinions. For example, opinions with high emotion scores can be displayed preferentially. This allows administrators to respond quickly by prioritizing review of emotionally important opinions.

[0078] The summary editing department can automatically tag summarized and edited content and group related opinions and questions. For example, the generation AI can automatically tag summarized and edited content and group related opinions and questions. For example, it can add tags such as "improving school lunches" and "updating the library." The automatic tagging function can also be used to group summarized and edited content. For example, it can add a tag such as "how to use the suggestion box" to group related opinions. The generation AI can also automatically tag summarized and edited content and group related opinions and questions. For example, it can add a tag such as "improving school facilities" to group related opinions into one group. This allows administrators to respond efficiently by automatically tagging and grouping related opinions and questions.

[0079] The summary editing department can synchronize the summaries and edited content across different devices (smartphones, tablets, PCs), allowing administrators to access them from anywhere. For example, the generation AI can synchronize the summaries and edited content across different devices, allowing administrators to access them from anywhere. For example, opinions and questions can be checked on a smartphone or tablet. The generation AI can also synchronize the summaries and edited content across different devices, allowing administrators to access them from anywhere. For example, content edited on a PC can be checked on a smartphone. The generation AI can also synchronize the summaries and edited content across different devices, allowing administrators to access them from anywhere. For example, opinions and questions can be checked on a tablet and responded to on a smartphone. This allows administrators to access the content from anywhere by synchronizing it across different devices.

[0080] The summary editing department can link the summarized and edited content with other management systems (for example, CRM or ERP) and manage it in an integrated manner. For example, the content summarized and edited by the generation AI can be linked with a CRM system and managed in an integrated manner. For example, customer opinions and questions can be automatically registered in CRM. The summary and edited content can also be linked with other management systems (for example, ERP) and managed in an integrated manner. For example, opinions and questions can be automatically reflected in the ERP system. The summary and edited content can also be linked with other management systems and managed in an integrated manner. For example, opinions and questions can be automatically registered in a project management system. This allows for integrated management by linking with other management systems.

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

[0082] Step 1: The opinion acquisition unit acquires the user's opinions and questions. For example, the user inputs their opinions in text format. The opinion acquisition unit can also handle voice input and handwritten input. Step 2: The summary editor uses the generation AI to summarize and edit the opinions and questions acquired by the opinion acquisition unit. For example, the generation AI may use a text generation AI (e.g., LLM) to concisely summarize the opinions and questions. The generation AI may also use a multimodal generation AI to summarize the content of the opinions and questions. The generation AI may also extract and summarize important parts of the text. Step 3: The feedback provider provides feedback to the user based on the content summarized and edited by the summary editor. For example, the generation AI automatically generates feedback and sends it to the user.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following 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.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an opinion acquisition unit that acquires opinions and questions from users; a summary editing unit that summarizes and edits the opinions and questions acquired by the opinion acquisition unit; a feedback providing unit that provides feedback to the user based on the content summarized and edited by the summary editing unit. A system characterized by:

2. The opinion acquisition unit Analyzes input, estimates the emotion of the input, and adjusts the tone of the summary based on the emotion 2. The system of claim 1.

3. The opinion acquisition unit Analyzes input in real time and suggests appropriate question or comment formats 2. The system of claim 1.

4. The opinion acquisition unit Refer to input history and automatically complete content that is highly relevant to past opinions and questions 2. The system of claim 1.

5. The opinion acquisition unit It also supports voice input and handwritten input, and the generative AI converts them into text for summarization and editing.

2. The system of claim 1.

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