System

Abstract

An object of a system according to an embodiment is to appropriately analyze the content of a memo and generate an output based on the context.SOLUTION: A system according to an embodiment comprises an analyzer, an output generator, and a context analyzer. The analysis part analyzes the memo by using the generation AI. The output generation unit generates an output based on the memo analyzed by the analysis unit. The context analyzer adjusts the output generated by the output generator based on the context.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 had the problem of making it difficult to properly analyze the contents of notes and generate context-based output.

[0005] The system according to the embodiment aims to appropriately analyze the contents of notes and generate context-based output. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an output generation unit, and a context analysis unit. The analysis unit analyzes notes using a generation AI. The output generation unit generates output based on the notes analyzed by the analysis unit. The context analysis unit adjusts the output generated by the output generation unit based on the context. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately analyze the content of the notes and generate context-based output. [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 memo output system according to an embodiment of the present invention is a system that uses a generation AI to analyze memos and generate output in order to improve the productivity of middle-aged and senior employees and effectively utilize their accumulated knowledge. As a result, the memo output system can provide a means for middle-aged and senior employees to efficiently perform their work and share important information.

[0029] A memo output system according to an embodiment includes an analysis unit, an output generation unit, and a context analysis unit. The analysis unit analyzes memos using a generation AI. For example, the analysis unit understands the context and intent of memos using natural language processing technology. The analysis unit can also appropriately grasp the content of memos using machine learning technology. The output generation unit generates output based on the memos analyzed by the analysis unit. For example, the output generation unit summarizes meeting minutes using the generation AI. The output generation unit can also organize project progress reports using the generation AI. The context analysis unit adjusts the output generated by the output generation unit based on the context. For example, the context analysis unit generates output tailored to the context and purpose of the memos using the generation AI. The context analysis unit can also provide flexible output tailored to user needs using the generation AI. This allows the memo output system according to an embodiment to provide a means for middle-aged and senior employees to efficiently complete their work and share important information. For example, by quickly summarizing meeting minutes and organizing project progress reports, work efficiency can be improved. Furthermore, by utilizing user feedback, the performance of the generation AI can be improved, resulting in more appropriate output.

[0030] The analysis unit can refer to the user's past note history and learn the user's writing style and expression habits. For example, the analysis unit analyzes the user's past note history and learns the user's writing style and expression habits. For example, it identifies the tendency to frequently use certain phrases and expressions. The analysis unit also extracts the user's writing patterns based on the past note history, and the generation AI learns those patterns. For example, it recognizes technical terms and abbreviations that the user frequently uses. The analysis unit also refers to the user's note history and learns the expression habits in specific contexts and situations. For example, if meeting minutes and project progress reports are written in different styles, it can learn the differences. This enables analysis that is optimized for the user.

[0031] The analysis unit can add voice input and convert voice memos into text for analysis. For example, the analysis unit collects notes using voice input and converts them into text using voice recognition technology. For example, statements made during a meeting are converted into text in real time and used for analysis. The analysis unit also converts voice memos into text, and the generation AI analyzes the text. For example, the content of the voice memos is analyzed using natural language processing technology to extract important points. The analysis unit can also add voice input to record what the user says as a memo and use it for analysis. For example, a project progress report can be recorded as audio, converted into text, and analyzed. This makes it possible to analyze voice memos.

[0032] The analysis unit can automatically translate and analyze notes written in different languages. For example, the analysis unit automatically translates notes written in different languages, and the generation AI analyzes the translation results. For example, notes written in English can be translated into Japanese and used for analysis. The analysis unit also uses an automatic translation function to convert notes written in different languages ​​into a unified language and analyze them. For example, meeting minutes written in multiple languages ​​can be translated into one language and analyzed. The analysis unit also automatically translates notes written in different languages, and the generation AI analyzes their contents. For example, progress reports on an international project can be collected in multiple languages, translated, and analyzed. This makes it possible to analyze notes in different languages.

[0033] The output generation unit can refer to the user's past output history and generate output in a format optimized for the user. For example, the output generation unit refers to the user's past output history, and the generation AI generates optimized output based on that history. For example, it reflects the format and style preferred by the user. The output generation unit also generates output tailored to the user's preferences based on the past output history. For example, if a particular expression or phrase is frequently used, it reflects that pattern. The output generation unit also refers to the user's output history, and the generation AI generates optimal output based on that history. For example, it references past success stories and generates output in a similar format. In this way, output optimized for the user is generated.

[0034] The output generation unit can automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, in the output generation unit, the generation AI refers to a technical dictionary to automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, medical terminology is used for notes in the medical field. In addition, in the output generation unit, the generation AI refers to a database related to the user's industry to generate industry-specific output. For example, technical terminology is used for notes in the technical field. In addition, in the output generation unit, the generation AI learns literature and materials in that field to generate output specialized to the user's field of expertise. For example, legal terminology is used for notes in the legal field. In this way, output specialized for the industry or field of expertise is generated.

[0035] When generating output, the output generation unit can automatically generate visual elements and provide them in a format that is visually easy to understand. For example, the output generation unit automatically generates visual elements when generating output. For example, it creates graphs and charts based on data and provides them in a format that is visually easy to understand. In order to automatically generate visual elements, the output generation unit has a generation AI analyze the data and select appropriate graphs and charts. For example, it creates a line graph based on sales data. In addition, the output generation unit adds visual elements to the output to enable users to intuitively understand the information. For example, it displays the progress of a project in a Gantt chart. This generates output that is visually easy to understand.

[0036] The output generation unit can generate output in different formats and provide it in a format that meets the user's needs. For example, in order to generate output in different formats, the generation AI outputs data in formats such as PDF, Word, and Excel. For example, meeting minutes are provided in PDF format. In addition, in order to provide output in a format that meets the user's needs, the generation AI generates data in multiple formats. For example, a project progress report is provided in Excel format. In addition, by generating output in different formats, the output generation unit allows the user to use information in the format they require. For example, a report is provided in Word format and data analysis is provided in Excel format. In this way, output is provided in a format that meets the user's needs.

[0037] The context analysis unit can enhance output by referencing not only the context of the note but also related external information. For example, the context analysis unit references related news and industry reports when generating output based on the context of the note. For example, the latest market trends can be incorporated to enhance a project progress report. The context analysis unit also complements the context of the note by referencing external information to generate more detailed output. For example, the latest technology reports in the industry can be referenced to supplement the technical background. The context analysis unit also allows the generation AI to automatically collect external information and enhance output based on the context of the note. For example, related news articles can be quoted to enrich meeting minutes. This enhances the output by referencing external information.

[0038] The context analysis unit can generate output including future predictions and proposals based on the context of the notes. For example, the context analysis unit generates output including future predictions based on the context of the notes. For example, predicting future challenges and risks based on the progress of a project. In addition, the context analysis unit uses a generative AI to analyze the context of the notes and generate output including specific proposals. For example, proposing points to be discussed at the next meeting based on meeting minutes. In addition, the context analysis unit generates output including future predictions and proposals based on the context of the notes. For example, proposing future strategies based on industry trends. This generates output including future predictions and proposals.

[0039] The context analysis unit can generate output according to different scenarios based on the context of the memo. For example, the context analysis unit generates output according to an optimistic scenario based on the context of the memo. For example, a progress report assuming the success of the project is created. The context analysis unit also generates output according to a pessimistic scenario based on the context of the memo. For example, a progress report that emphasizes risks and challenges is created. The context analysis unit also generates output according to a neutral scenario based on the context of the memo. For example, an objective report on the current progress. In this way, outputs according to different scenarios are generated.

[0040] The context analysis unit can generate output from different perspectives based on the context of the memo. For example, the context analysis unit generates output from the perspective of management based on the context of the memo. For example, a report including proposals for management strategy is created. The context analysis unit also generates output from the perspective of employees based on the context of the memo. For example, a report including proposals for business improvement is created. The context analysis unit also generates output from the perspective of customers based on the context of the memo. For example, a report including proposals for improving customer satisfaction is created. As a result, outputs from different perspectives are generated.

[0041] The system collects user feedback in real time, allowing the generation AI to instantly learn and improve the output. For example, the system collects user feedback in real time, allowing the generation AI to instantly learn and improve the output. For example, the system adjusts the next output based on the feedback provided by the user. The system also builds a feedback collection system to collect user opinions and requests in real time. For example, feedback is collected using an online form or chatbot. The system also allows the generation AI to learn from the feedback in real time and improve the output. For example, the content and format of the output is adjusted based on the user feedback. This allows feedback to be collected in real time and the output to be improved instantly.

[0042] The system can analyze the content of the feedback, extract common areas for improvement and requests, and reflect them in the generative AI algorithm. For example, the system analyzes the content of the feedback and extracts common areas for improvement and requests. For example, if similar feedback is received from multiple users, the system will prioritize reflecting that content. The system also builds a feedback analysis system to automatically classify user opinions and requests. For example, it analyzes feedback using text mining technology. The system also reflects the content of the feedback in the generative AI algorithm to improve the quality of the output. For example, it adjusts the algorithm based on the common areas for improvement. This allows common areas for improvement and requests to be extracted and reflected in the generative AI algorithm.

[0043] The system may add a function to accept audio input or visual feedback when collecting feedback. For example, the system may add an audio input function to the feedback collection system to enable users to provide feedback by voice. For example, audio feedback may be collected using a microphone. The system may also add a visual feedback function to enable users to provide feedback by images or videos. For example, feedback may be provided by uploading screenshots or videos. The system may also add a function to accept audio input or visual feedback to enable users to provide feedback in various ways. For example, audio memos or visual notes may be collected. This allows feedback to be collected in various ways by accepting audio input or visual feedback.

[0044] The system can automatically classify the content of feedback and manage it by dividing it into different categories. For example, the system builds a system that automatically classifies the content of feedback and manages it by dividing it into different categories. For example, it classifies it into feature improvements, bug reports, requests, etc. The system also uses an automatic classification algorithm to analyze the content of feedback and classify it into an appropriate category. For example, it analyzes the feedback using text mining technology. The system also builds a system that automatically classifies the content of feedback and manages it by dividing it into different categories. For example, it notifies the appropriate person in charge depending on the content of the feedback. In this way, feedback can be automatically classified and managed efficiently.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The analysis unit can refer to the user's past note history and learn the user's writing style and expression habits. For example, it can identify the tendency to frequently use certain phrases and expressions. The analysis unit also extracts the user's writing patterns based on the past note history, and the generation AI learns those patterns. For example, it can recognize technical terms and abbreviations that the user frequently uses. The analysis unit also refers to the user's note history and learns the expression habits in specific contexts and situations. For example, if meeting minutes and project progress reports are written in different styles, it can learn the differences. This enables analysis that is optimized for the user.

[0047] The analysis unit can add voice input and convert voice memos into text for analysis. For example, statements made during a meeting can be converted into text in real time and used for analysis. The analysis unit also converts voice memos into text, which is then analyzed by the generation AI. For example, the content of the voice memos can be analyzed using natural language processing technology to extract important points. The analysis unit can also add voice input to record what the user says as a memo and use it for analysis. For example, a project progress report can be recorded as audio, converted into text, and analyzed. This makes it possible to analyze voice memos.

[0048] The analysis unit can automatically translate and analyze notes written in different languages. For example, notes written in different languages ​​are automatically translated, and the generation AI analyzes the translation results. For example, notes written in English are translated into Japanese and used for analysis. The analysis unit also uses the automatic translation function to convert notes written in different languages ​​into a unified language and analyze them. For example, meeting minutes written in multiple languages ​​are translated into one language and analyzed. The analysis unit also automatically translates notes written in different languages, and the generation AI analyzes their contents. For example, progress reports on an international project are collected in multiple languages, translated, and analyzed. This makes it possible to analyze notes in different languages.

[0049] The output generation unit can refer to the user's past output history and generate output in a format optimized for the user. For example, the generation AI can refer to the user's past output history and generate optimized output based on that history. For example, it can reflect the user's preferred format or style. The output generation unit can also refer to the user's past output history and generate output tailored to the user's preferences. For example, if a particular expression or phrase is frequently used, it can reflect that pattern. The output generation unit can also refer to the user's output history and generate optimal output based on that history. For example, it can refer to past success stories and generate output in a similar format. In this way, output optimized for the user is generated.

[0050] The output generation unit can automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, the generation AI refers to a technical dictionary to automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, medical terminology is used for notes in the medical field. In addition, the output generation unit refers to a database related to the user's industry to generate industry-specific output. For example, technical terminology is used for notes in the technical field. In addition, in order to generate output specialized to the user's field of expertise, the generation AI learns literature and materials in that field. For example, legal terminology is used for notes in the legal field. In this way, output specialized for the industry or field of expertise is generated.

[0051] When generating output, the output generation unit can automatically generate visual elements and provide them in a format that is visually easy to understand. For example, visual elements are automatically generated when generating output. For example, graphs and charts are created based on data and provided in a format that is visually easy to understand. In order to automatically generate visual elements, the output generation unit uses a generation AI to analyze data and select appropriate graphs and charts. For example, a line graph is created based on sales data. The output generation unit also adds visual elements to the output to enable users to intuitively understand the information. For example, the progress of a project is displayed in a Gantt chart. This generates output that is visually easy to understand.

[0052] The output generation unit can generate output in different formats and provide it in a format that meets the user's needs. For example, to generate output in different formats, the generation AI outputs data in formats such as PDF, Word, and Excel. For example, meeting minutes are provided in PDF format. The output generation unit also generates data in multiple formats to provide output in a format that meets the user's needs. For example, a project progress report is provided in Excel format. The output generation unit also generates output in different formats so that the user can use the information in the format they need. For example, a report is provided in Word format and data analysis is provided in Excel format. In this way, output is provided in a format that meets the user's needs.

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

[0054] Step 1: The analysis unit analyzes the memo using generative AI. For example, the analysis unit uses natural language processing technology to understand the context and intent of the memo. The analysis unit can also use machine learning technology to properly understand the content of the memo. Step 2: The output generation unit generates output based on the notes analyzed by the analysis unit. For example, the output generation unit can use generation AI to summarize meeting minutes. The output generation unit can also use generation AI to organize project progress reports. Step 3: The context analysis unit adjusts the output generated by the output generation unit based on the context. For example, the context analysis unit uses the generation AI to generate output that is tailored to the context and purpose of the note. The context analysis unit can also use the generation AI to provide flexible output tailored to the user's needs.

[0055] (Example 2) The memo output system according to an embodiment of the present invention is a system that uses a generation AI to analyze memos and generate output in order to improve the productivity of middle-aged and senior employees and effectively utilize their accumulated knowledge. As a result, the memo output system can provide a means for middle-aged and senior employees to efficiently perform their work and share important information.

[0056] A memo output system according to an embodiment includes an analysis unit, an output generation unit, and a context analysis unit. The analysis unit analyzes memos using a generation AI. For example, the analysis unit understands the context and intent of memos using natural language processing technology. The analysis unit can also appropriately grasp the content of memos using machine learning technology. The output generation unit generates output based on the memos analyzed by the analysis unit. For example, the output generation unit summarizes meeting minutes using the generation AI. The output generation unit can also organize project progress reports using the generation AI. The context analysis unit adjusts the output generated by the output generation unit based on the context. For example, the context analysis unit generates output tailored to the context and purpose of the memos using the generation AI. The context analysis unit can also provide flexible output tailored to user needs using the generation AI. This allows the memo output system according to an embodiment to provide a means for middle-aged and senior employees to efficiently complete their work and share important information. For example, by quickly summarizing meeting minutes and organizing project progress reports, work efficiency can be improved. Furthermore, by utilizing user feedback, the performance of the generation AI can be improved, resulting in more appropriate output.

[0057] The analysis unit can analyze the emotional nuances of notes and track changes in emotion. For example, the analysis unit uses generative AI to analyze the emotional nuances of notes and track changes in emotion. For example, it analyzes the tone of the words and expressions used in notes and records changes in emotion over time. The analysis unit also calculates an emotion score using natural language processing technology. For example, it identifies positive and negative expressions and quantifies the intensity of emotion. The analysis unit also calculates an emotion score for each section of notes and graphs changes in emotion. For example, it can visualize changes in emotion as the discussion progresses in meeting minutes. This allows for a deeper understanding of the intent of the notes.

[0058] The analysis unit can refer to the user's past note history and learn the user's writing style and expression habits. For example, the analysis unit analyzes the user's past note history and learns the user's writing style and expression habits. For example, it identifies the tendency to frequently use certain phrases and expressions. The analysis unit also extracts the user's writing patterns based on the past note history, and the generation AI learns those patterns. For example, it recognizes technical terms and abbreviations that the user frequently uses. The analysis unit also refers to the user's note history and learns the expression habits in specific contexts and situations. For example, if meeting minutes and project progress reports are written in different styles, it can learn the differences. This enables analysis that is optimized for the user.

[0059] The analysis unit can use the emotion estimation function to analyze the user's emotion regarding the content of the note and provide an analysis result that takes emotional factors into consideration. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the content of the note. For example, it analyzes the tone of the words and expressions used in the note and calculates an emotion score. The analysis unit also analyzes the user's emotion regarding the content of the note and provides an analysis result that takes emotional factors into consideration. For example, it highlights parts with strong positive emotions. The analysis unit also uses the emotion estimation function to analyze the user's emotion regarding the content of the note and provides feedback that takes emotional factors into consideration. For example, it makes improvement suggestions for parts with strong negative emotions. In this way, an analysis result that takes emotional factors into consideration is provided.

[0060] The analysis unit can add voice input and convert voice memos into text for analysis. For example, the analysis unit collects notes using voice input and converts them into text using voice recognition technology. For example, statements made during a meeting are converted into text in real time and used for analysis. The analysis unit also converts voice memos into text, and the generation AI analyzes the text. For example, the content of the voice memos is analyzed using natural language processing technology to extract important points. The analysis unit can also add voice input to record what the user says as a memo and use it for analysis. For example, a project progress report can be recorded as audio, converted into text, and analyzed. This makes it possible to analyze voice memos.

[0061] The analysis unit can automatically translate and analyze notes written in different languages. For example, the analysis unit automatically translates notes written in different languages, and the generation AI analyzes the translation results. For example, notes written in English can be translated into Japanese and used for analysis. The analysis unit also uses an automatic translation function to convert notes written in different languages ​​into a unified language and analyze them. For example, meeting minutes written in multiple languages ​​can be translated into one language and analyzed. The analysis unit also automatically translates notes written in different languages, and the generation AI analyzes their contents. For example, progress reports on an international project can be collected in multiple languages, translated, and analyzed. This makes it possible to analyze notes in different languages.

[0062] The analysis unit can analyze the emotional nuances of the voice memo using the emotion estimation function and provide an analysis result based on the emotion. The analysis unit, for example, analyzes the emotional nuances of the voice memo using the emotion estimation function. For example, it analyzes the tone and intonation of the voice and calculates an emotion score. The analysis unit also analyzes the emotional nuances of the voice memo and provides an analysis result based on the emotion. For example, it highlights parts with strong positive emotions. The analysis unit also analyzes the emotional nuances of the voice memo using the emotion estimation function and provides feedback based on the emotion. For example, it makes improvement suggestions for parts with strong negative emotions. This makes it possible to analyze the emotional nuances of the voice memo in consideration of the emotional nuances.

[0063] The output generation unit can refer to the user's past output history and generate output in a format optimized for the user. For example, the output generation unit refers to the user's past output history, and the generation AI generates optimized output based on that history. For example, it reflects the format and style preferred by the user. The output generation unit also generates output tailored to the user's preferences based on the past output history. For example, if a particular expression or phrase is frequently used, it reflects that pattern. The output generation unit also refers to the user's output history, and the generation AI generates optimal output based on that history. For example, it references past success stories and generates output in a similar format. In this way, output optimized for the user is generated.

[0064] The output generation unit can automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, in the output generation unit, the generation AI refers to a technical dictionary to automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, medical terminology is used for notes in the medical field. In addition, in the output generation unit, the generation AI refers to a database related to the user's industry to generate industry-specific output. For example, technical terminology is used for notes in the technical field. In addition, in the output generation unit, the generation AI learns literature and materials in that field to generate output specialized to the user's field of expertise. For example, legal terminology is used for notes in the legal field. In this way, output specialized for the industry or field of expertise is generated.

[0065] The output generation unit can use the emotion estimation function to consider the emotional impact that the content of the output has on the user and adjust it to elicit positive emotions. The output generation unit, for example, uses the emotion estimation function to analyze the emotional impact that the content of the output has on the user. For example, by using a lot of positive expressions, the user's emotions are enhanced. The output generation unit also considers the emotional impact that the content of the output has on the user and adjusts it to elicit positive emotions. For example, it adds words of encouragement or positive messages. The output generation unit also uses the emotion estimation function to analyze the emotional impact that the content of the output has on the user and adjusts it to elicit positive emotions. For example, it replaces negative expressions with positive expressions. In this way, output that elicits positive emotions is generated.

[0066] When generating output, the output generation unit can automatically generate visual elements and provide them in a format that is visually easy to understand. For example, the output generation unit automatically generates visual elements when generating output. For example, it creates graphs and charts based on data and provides them in a format that is visually easy to understand. In order to automatically generate visual elements, the output generation unit has a generation AI analyze the data and select appropriate graphs and charts. For example, it creates a line graph based on sales data. In addition, the output generation unit adds visual elements to the output to enable users to intuitively understand the information. For example, it displays the progress of a project in a Gantt chart. This generates output that is visually easy to understand.

[0067] The output generation unit can generate output in different formats and provide it in a format that meets the user's needs. For example, in order to generate output in different formats, the generation AI outputs data in formats such as PDF, Word, and Excel. For example, meeting minutes are provided in PDF format. In addition, in order to provide output in a format that meets the user's needs, the generation AI generates data in multiple formats. For example, a project progress report is provided in Excel format. In addition, by generating output in different formats, the output generation unit allows the user to use information in the format they require. For example, a report is provided in Word format and data analysis is provided in Excel format. In this way, output is provided in a format that meets the user's needs.

[0068] The output generation unit can use the emotion estimation function to analyze the user's emotional response to the visual elements and select optimal visual elements. The output generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the visual elements. For example, it evaluates the emotional impact that the design of a graph or diagram has on the user. The output generation unit also selects optimal visual elements based on the user's emotional response. For example, it preferentially adopts designs that have a high number of positive emotional responses. The output generation unit also uses the emotion estimation function to analyze the user's emotional response to the visual elements and selects optimal visual elements. For example, it selects designs that have a low number of negative emotional responses. In this way, optimal visual elements are selected based on the user's emotional response.

[0069] The context analysis unit can enhance output by referencing not only the context of the note but also related external information. For example, the context analysis unit references related news and industry reports when generating output based on the context of the note. For example, the latest market trends can be incorporated to enhance a project progress report. The context analysis unit also complements the context of the note by referencing external information to generate more detailed output. For example, the latest technology reports in the industry can be referenced to supplement the technical background. The context analysis unit also allows the generation AI to automatically collect external information and enhance output based on the context of the note. For example, related news articles can be quoted to enrich meeting minutes. This enhances the output by referencing external information.

[0070] The context analysis unit can generate output including future predictions and proposals based on the context of the notes. For example, the context analysis unit generates output including future predictions based on the context of the notes. For example, predicting future challenges and risks based on the progress of a project. In addition, the context analysis unit uses a generative AI to analyze the context of the notes and generate output including specific proposals. For example, proposing points to be discussed at the next meeting based on meeting minutes. In addition, the context analysis unit generates output including future predictions and proposals based on the context of the notes. For example, proposing future strategies based on industry trends. This generates output including future predictions and proposals.

[0071] The context analysis unit can use the emotion estimation function to consider the user's emotions regarding the context of the note and generate output that is easy to empathize with emotionally. The context analysis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the context of the note and generate output that is easy to empathize with emotionally. For example, it uses expressions that elicit positive emotions. The context analysis unit also considers the user's emotions regarding the context of the note and generates output that is easy to empathize with emotionally. For example, it makes suggestions that are in line with the user's emotions. The context analysis unit also uses the emotion estimation function to analyze the user's emotions regarding the context of the note and generate output that is easy to empathize with emotionally. For example, it uses expressions that alleviate negative emotions. This generates output that is easy to empathize with emotionally.

[0072] The context analysis unit can generate output according to different scenarios based on the context of the memo. For example, the context analysis unit generates output according to an optimistic scenario based on the context of the memo. For example, a progress report assuming the success of the project is created. The context analysis unit also generates output according to a pessimistic scenario based on the context of the memo. For example, a progress report that emphasizes risks and challenges is created. The context analysis unit also generates output according to a neutral scenario based on the context of the memo. For example, an objective report on the current progress. In this way, outputs according to different scenarios are generated.

[0073] The context analysis unit can generate output from different perspectives based on the context of the memo. For example, the context analysis unit generates output from the perspective of management based on the context of the memo. For example, a report including proposals for management strategy is created. The context analysis unit also generates output from the perspective of employees based on the context of the memo. For example, a report including proposals for business improvement is created. The context analysis unit also generates output from the perspective of customers based on the context of the memo. For example, a report including proposals for improving customer satisfaction is created. As a result, outputs from different perspectives are generated.

[0074] The context analysis unit can use the emotion estimation function to analyze the user's emotional responses to different scenarios and perspectives and select the optimal output. For example, the context analysis unit uses the emotion estimation function to analyze the user's emotional responses to different scenarios and select the optimal output. For example, it prioritizes scenarios with a large number of positive emotional responses. The context analysis unit also uses the emotion estimation function to analyze the user's emotional responses to different perspectives and select the optimal output. For example, if output from the perspective of a manager is preferred, it prioritizes that perspective. The context analysis unit also uses the emotion estimation function to analyze the user's emotional responses to different scenarios and perspectives and select the optimal output. For example, it selects output with a small number of negative emotional responses. In this way, the optimal output is selected based on the user's emotional responses.

[0075] The system collects user feedback in real time, allowing the generation AI to instantly learn and improve the output. For example, the system collects user feedback in real time, allowing the generation AI to instantly learn and improve the output. For example, the system adjusts the next output based on the feedback provided by the user. The system also builds a feedback collection system to collect user opinions and requests in real time. For example, feedback is collected using an online form or chatbot. The system also allows the generation AI to learn from the feedback in real time and improve the output. For example, the content and format of the output is adjusted based on the user feedback. This allows feedback to be collected in real time and the output to be improved instantly.

[0076] The system can analyze the content of the feedback, extract common areas for improvement and requests, and reflect them in the generative AI algorithm. For example, the system analyzes the content of the feedback and extracts common areas for improvement and requests. For example, if similar feedback is received from multiple users, the system will prioritize reflecting that content. The system also builds a feedback analysis system to automatically classify user opinions and requests. For example, it analyzes feedback using text mining technology. The system also reflects the content of the feedback in the generative AI algorithm to improve the quality of the output. For example, it adjusts the algorithm based on the common areas for improvement. This allows common areas for improvement and requests to be extracted and reflected in the generative AI algorithm.

[0077] The system can use the emotion estimation function to analyze the emotional elements contained in the feedback and make improvements based on the emotions. For example, the system uses the emotion estimation function to analyze the emotional elements contained in the feedback. For example, it distinguishes between positive feedback and negative feedback. The system also analyzes the emotional elements of the feedback and makes improvements based on the emotions. For example, it focuses on improving areas with a lot of negative feedback. The system also uses the emotion estimation function to analyze the emotional elements contained in the feedback and makes improvements based on the emotions. For example, it makes improvements that bring out positive emotions. In this way, improvements based on the emotions are made.

[0078] The system may add a function to accept audio input or visual feedback when collecting feedback. For example, the system may add an audio input function to the feedback collection system to enable users to provide feedback by voice. For example, audio feedback may be collected using a microphone. The system may also add a visual feedback function to enable users to provide feedback by images or videos. For example, feedback may be provided by uploading screenshots or videos. The system may also add a function to accept audio input or visual feedback to enable users to provide feedback in various ways. For example, audio memos or visual notes may be collected. This allows feedback to be collected in various ways by accepting audio input or visual feedback.

[0079] The system can automatically classify the content of feedback and manage it by dividing it into different categories. For example, the system builds a system that automatically classifies the content of feedback and manages it by dividing it into different categories. For example, it classifies it into feature improvements, bug reports, requests, etc. The system also uses an automatic classification algorithm to analyze the content of feedback and classify it into an appropriate category. For example, it analyzes the feedback using text mining technology. The system also builds a system that automatically classifies the content of feedback and manages it by dividing it into different categories. For example, it notifies the appropriate person in charge depending on the content of the feedback. In this way, feedback can be automatically classified and managed efficiently.

[0080] The system can use the emotion estimation function to analyze the emotional tone of the feedback and process it by distinguishing between positive and negative feedback. For example, the system uses the emotion estimation function to analyze the emotional tone of the feedback. For example, it distinguishes between positive and negative feedback. The system also analyzes the emotional tone of the feedback and process it by distinguishing between positive and negative feedback. For example, it responds quickly to negative feedback. The system also analyzes the emotional tone of the feedback and process it by distinguishing between positive and negative feedback. For example, it reinforces areas for improvement based on positive feedback. In this way, the emotional tone of the feedback can be analyzed and processed appropriately.

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

[0082] The analysis unit can refer to the user's past note history and learn the user's writing style and expression habits. For example, it can identify the tendency to frequently use certain phrases and expressions. The analysis unit also extracts the user's writing patterns based on the past note history, and the generation AI learns those patterns. For example, it can recognize technical terms and abbreviations that the user frequently uses. The analysis unit also refers to the user's note history and learns the expression habits in specific contexts and situations. For example, if meeting minutes and project progress reports are written in different styles, it can learn the differences. This enables analysis that is optimized for the user.

[0083] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding the content of the note and provide an analysis result that takes emotional factors into consideration. For example, it analyzes the tone of the words and expressions used in the note and calculates an emotion score. The analysis unit also analyzes the user's emotions regarding the content of the note and provides an analysis result that takes emotional factors into consideration. For example, it highlights parts with strong positive emotions. The analysis unit also uses the emotion estimation function to analyze the user's emotions regarding the content of the note and provides feedback that takes emotional factors into consideration. For example, it makes improvement suggestions for parts with strong negative emotions. This provides an analysis result that takes emotional factors into consideration.

[0084] The analysis unit can add voice input and convert voice memos into text for analysis. For example, statements made during a meeting can be converted into text in real time and used for analysis. The analysis unit also converts voice memos into text, which is then analyzed by the generation AI. For example, the content of the voice memos can be analyzed using natural language processing technology to extract important points. The analysis unit can also add voice input to record what the user says as a memo and use it for analysis. For example, a project progress report can be recorded as audio, converted into text, and analyzed. This makes it possible to analyze voice memos.

[0085] The analysis unit can automatically translate and analyze notes written in different languages. For example, notes written in different languages ​​are automatically translated, and the generation AI analyzes the translation results. For example, notes written in English are translated into Japanese and used for analysis. The analysis unit also uses the automatic translation function to convert notes written in different languages ​​into a unified language and analyze them. For example, meeting minutes written in multiple languages ​​are translated into one language and analyzed. The analysis unit also automatically translates notes written in different languages, and the generation AI analyzes their contents. For example, progress reports on an international project are collected in multiple languages, translated, and analyzed. This makes it possible to analyze notes in different languages.

[0086] The analysis unit can use the emotion estimation function to analyze the emotional nuances of the voice memo and provide an analysis result based on the emotion. For example, it analyzes the tone and intonation of the voice and calculates an emotion score. The analysis unit also analyzes the emotional nuances of the voice memo and provides an analysis result based on the emotion. For example, it highlights parts with strong positive emotions. The analysis unit also analyzes the emotional nuances of the voice memo using the emotion estimation function and provides feedback based on the emotion. For example, it makes improvement suggestions for parts with strong negative emotions. This makes it possible to analyze the emotional nuances of the voice memo taking into account the emotional nuances.

[0087] The output generation unit can refer to the user's past output history and generate output in a format optimized for the user. For example, the generation AI can refer to the user's past output history and generate optimized output based on that history. For example, it can reflect the user's preferred format or style. The output generation unit can also refer to the user's past output history and generate output tailored to the user's preferences. For example, if a particular expression or phrase is frequently used, it can reflect that pattern. The output generation unit can also refer to the user's output history and generate optimal output based on that history. For example, it can refer to past success stories and generate output in a similar format. In this way, output optimized for the user is generated.

[0088] The output generation unit can automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, the generation AI refers to a technical dictionary to automatically incorporate terms and expressions specialized to the user's industry or field of expertise. For example, medical terminology is used for notes in the medical field. In addition, the output generation unit refers to a database related to the user's industry to generate industry-specific output. For example, technical terminology is used for notes in the technical field. In addition, in order to generate output specialized to the user's field of expertise, the generation AI learns literature and materials in that field. For example, legal terminology is used for notes in the legal field. In this way, output specialized for the industry or field of expertise is generated.

[0089] The output generation unit can use the emotion estimation function to consider the emotional impact that the content of the output has on the user and adjust it to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotional impact that the content of the output has on the user. For example, the user's emotions can be enhanced by using a lot of positive expressions. The output generation unit also considers the emotional impact that the content of the output has on the user and adjusts it to elicit positive emotions. For example, encouraging words and positive messages can be added. The output generation unit also uses the emotion estimation function to analyze the emotional impact that the content of the output has on the user and adjusts it to elicit positive emotions. For example, negative expressions can be replaced with positive expressions. In this way, output that elicits positive emotions is generated.

[0090] When generating output, the output generation unit can automatically generate visual elements and provide them in a format that is visually easy to understand. For example, visual elements are automatically generated when generating output. For example, graphs and charts are created based on data and provided in a format that is visually easy to understand. In order to automatically generate visual elements, the output generation unit uses a generation AI to analyze data and select appropriate graphs and charts. For example, a line graph is created based on sales data. The output generation unit also adds visual elements to the output to enable users to intuitively understand the information. For example, the progress of a project is displayed in a Gantt chart. This generates output that is visually easy to understand.

[0091] The output generation unit can generate output in different formats and provide it in a format that meets the user's needs. For example, to generate output in different formats, the generation AI outputs data in formats such as PDF, Word, and Excel. For example, meeting minutes are provided in PDF format. The output generation unit also generates data in multiple formats to provide output in a format that meets the user's needs. For example, a project progress report is provided in Excel format. The output generation unit also generates output in different formats so that the user can use the information in the format they need. For example, a report is provided in Word format and data analysis is provided in Excel format. In this way, output is provided in a format that meets the user's needs.

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

[0093] Step 1: The analysis unit analyzes the memo using generative AI. For example, the analysis unit uses natural language processing technology to understand the context and intent of the memo. The analysis unit can also use machine learning technology to properly understand the content of the memo. Step 2: The output generation unit generates output based on the notes analyzed by the analysis unit. For example, the output generation unit can use generation AI to summarize meeting minutes. The output generation unit can also use generation AI to organize project progress reports. Step 3: The context analysis unit adjusts the output generated by the output generation unit based on the context. For example, the context analysis unit uses the generation AI to generate output that is tailored to the context and purpose of the note. The context analysis unit can also use the generation AI to provide flexible output tailored to the user's needs.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0122] 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.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0128] 7, 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0138] 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.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following 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.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis part that analyzes notes using generative AI, an output generation unit that generates an output based on the memo analyzed by the analysis unit; a context analysis unit that adjusts the output generated by the output generation unit based on the context. A system characterized by:

2. The analysis unit Analyze the emotional nuances of the notes and track changes in sentiment 2. The system of claim 1.

3. The analysis unit Refer to the user's past memo history and learn the user's writing style and expression habits.

2. The system of claim 1.

4. The analysis unit Analyzing the user's feelings about the contents of the memo and providing an analysis result that takes emotional factors into account 2. The system of claim 1.

5. The analysis unit Add voice input and convert voice memos into text for analysis 2. The system of claim 1.

Images