System

Abstract

An object of a system according to an embodiment is to accurately and efficiently convert communication performed in business into sentences.SOLUTION: A system includes a dedicated program language creation part and a generation AI cooperation part. The dedicated program language creation unit converts communication performed in business into a dedicated program language. A generation AI cooperation part analyzes a dedicated program language to generate a business sentence.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 techniques have had the problem of making it difficult to accurately and efficiently document business communications.

[0005] The system according to the embodiment aims to accurately and efficiently document business communications. [Means for solving the problem]

[0006] The system according to the embodiment includes a dedicated programming language creation unit and a generation AI collaboration unit. The dedicated programming language creation unit converts business communications into the dedicated programming language. The generation AI collaboration unit analyzes the dedicated programming language and generates business documents. [Effects of the Invention]

[0007] The system according to the embodiment can accurately and efficiently document business communications. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication system according to an embodiment of the present invention converts business communications into a programming language and generates business documents using a generation AI. This enables the communication system to carry out business communications accurately, quickly, and efficiently.

[0029] A communication system according to an embodiment includes a dedicated programming language creation unit and a generation AI linkage unit. The dedicated programming language creation unit converts business communications into a dedicated programming language. For example, the dedicated programming language creation unit includes syntax for various business documents, such as meeting minutes, reports, and email templates. The dedicated programming language creation unit has grammar and vocabulary specialized for each business, and is designed to concisely and clearly describe business content. The generation AI linkage unit analyzes content written in the dedicated programming language using a generation AI and generates business documents based on that content. For example, if an instruction such as "Create minutes of the meeting" is written in a programming language, the generation AI analyzes the instruction and generates specific minutes. The generation AI also has the ability to automatically detect and correct typos and misuse when generating business documents. For example, if the generation AI mistakenly inputs the expression "Thank you for your kind support" as "Thank you for your kind support," the AI ​​automatically corrects it. Furthermore, the generation AI uses natural, non-intuitive expressions when generating business documents. For example, it converts the expression "Please confirm" into a polite expression such as "I'm sorry to trouble you, but could you please confirm?" As a result, the communication system according to the embodiment can carry out business communication accurately, quickly, and efficiently. For example, the generation AI can handle documents in various formats, such as meeting minutes, reports, emails, and presentation materials. This improves business efficiency.

[0030] The dedicated programming language creation unit can create different dedicated programming languages ​​for different types of business and provide grammar and vocabulary optimized for each business. For example, the dedicated programming language creation unit creates a dedicated programming language for meeting minutes and provides grammar and vocabulary specialized for minutes. For example, the dedicated programming language creation unit includes syntax such as agenda, speaker, speech content, and decisions. The dedicated programming language creation unit also creates a dedicated programming language for sales and provides grammar and vocabulary specialized for sales activities. For example, the dedicated programming language creation unit includes syntax such as customer information, business negotiation content, and contract terms. The dedicated programming language creation unit also creates a dedicated programming language for marketing and provides grammar and vocabulary specialized for marketing activities. For example, the dedicated programming language creation unit includes syntax such as campaign information, advertising content, and target market. This provides grammar and vocabulary optimized for each business, thereby improving business efficiency.

[0031] When analyzing a dedicated programming language, the generative AI collaboration unit automatically collects background information to understand the business context, thereby improving analysis accuracy. For example, the generative AI collaboration unit builds a system that automatically collects relevant background information so that the generative AI can understand the business context. For example, it collects detailed project information and past meeting records. The generative AI collaboration unit also refers to relevant literature and databases to understand the business context. For example, it collects information on industry trends and market movements. Furthermore, the generative AI collaboration unit collects relevant news articles and academic papers to understand the business context. For example, it collects information on the latest research results and technological trends. This allows background information to be automatically collected to understand the business context, thereby improving analysis accuracy.

[0032] The generation AI linkage unit can add an interactive function that feeds back the analysis results to the user and allows the user to provide corrections or additional information. For example, the generation AI linkage unit adds an interactive function that allows the generation AI to feed back the analysis results to the user and allows the user to provide corrections or additional information. For example, comments and corrections can be made to the analysis results in real time. The generation AI linkage unit also provides an interface that allows the user to provide corrections or additional information. For example, it provides a form that allows the user to check the analysis results and enter corrections or additional information. Furthermore, when the user provides corrections or additional information, the generation AI linkage unit analyzes the content and provides feedback again. For example, the generation AI regenerates the business document based on the corrections and additional information provided by the user. In this way, by adding an interactive function that allows the user to provide corrections and additional information, the accuracy of the analysis can be improved.

[0033] The generation AI linkage unit can detect and correct typos and misuse. For example, when the generation AI detects typos and misuse, the generation AI linkage unit registers business terminology and abbreviations in a database and detects typos and misuse based on that. For example, it accurately recognizes terminology and abbreviations used in a specific industry. The generation AI linkage unit also uses natural language processing technology to detect typos and misuse. For example, it performs grammar analysis and spell checking. Furthermore, the generation AI linkage unit uses an automatic correction algorithm to correct typos and misuse. For example, it automatically corrects typos and misuse. This makes it possible to detect and correct typos and misuse, thereby generating accurate business documents.

[0034] The generation AI linkage unit can add an interactive function that provides feedback to the user on the results of detection of typos and misuses and allows the user to confirm corrections. The generation AI linkage unit, for example, provides feedback to the user on the results of detection of typos and misuses and allows the user to confirm corrections. For example, the detected typos and misuses can be highlighted so the user can confirm the corrections. The generation AI linkage unit also provides an interface for the user to confirm the corrections. For example, it provides a button for the user to check the detection results and approve the corrections. Furthermore, when the user approves the corrections, the generation AI analyzes the content and provides feedback again. For example, after the user approves the corrections, the generation AI generates the business text again. In this way, by adding an interactive function that allows the user to confirm the corrections, the accuracy of the text can be improved.

[0035] The Generative AI Collaboration Unit can automatically translate business documents into other languages ​​and support international business communication. For example, the Generative AI Collaboration Unit builds a system that automatically translates business documents generated by the Generative AI into multiple languages, such as English, French, and Chinese. For example, meeting minutes can be shared in multiple languages. The Generative AI Collaboration Unit also uses machine translation algorithms to automatically translate business documents. For example, it uses neural machine translation (NMT) to perform highly accurate translations. Furthermore, the Generative AI Collaboration Unit sets standards for evaluating translation accuracy. For example, it evaluates translation accuracy using the BLEU score or TER score. This makes it possible to automatically translate business documents into other languages ​​and support international business communication.

[0036] The generation AI linkage unit can visualize business documents to enable users to intuitively understand them. For example, the generation AI linkage unit builds a system that visualizes business documents generated by the generation AI to enable users to intuitively understand them. For example, it displays the generated documents using graphs and charts. The generation AI linkage unit also uses data visualization technology to visualize business documents. For example, it creates graphs and charts using D3.js or Chart.js. Furthermore, the generation AI linkage unit provides a function that allows users to interactively manipulate visualized information to enable users to intuitively understand it. For example, a user can click on a graph or chart to display detailed information. In this way, business documents can be visualized to enable users to intuitively understand them.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The Generative AI Collaboration Unit can evaluate the user's work performance and suggest areas for improvement. For example, it can evaluate the user's work speed and accuracy and provide specific advice for improving efficiency. It can also evaluate the user's communication skills and suggest training programs for improvement. It can also evaluate the user's stress level and provide resources for stress management. This allows it to make specific suggestions to improve the user's work performance.

[0039] The Generative AI Collaboration Unit can analyze the user's work history and provide support for creating future work plans. For example, it analyzes the success and failure factors of past projects and reflects these in future project plans. It can also analyze the user's skill set and propose training plans for skill improvement. It can also analyze the user's workload and provide advice on appropriate task allocation. This allows it to support the user in creating efficient work plans.

[0040] The Generative AI Collaboration Unit can monitor the user's work environment and provide advice to provide an optimal work environment. For example, it can monitor the lighting and temperature of the workspace and provide advice to maintain an optimal environment. It can also monitor the user's posture and movements and provide advice to maintain a healthy working posture. It can also monitor the user's working hours and provide advice to encourage them to take appropriate breaks. This makes it possible to provide specific advice to optimize the user's work environment.

[0041] The Generative AI Collaboration Unit can analyze the user's business data and propose improvements to business processes. For example, it can identify bottlenecks in the business flow and propose specific improvements to improve efficiency. It can also analyze the user's work patterns and propose optimal work schedules. Furthermore, it can identify parts of the business that can be automated based on the user's business data and make specific proposals for automation. This makes it possible to make specific proposals to improve the efficiency of business processes.

[0042] The Generative AI Collaboration Unit can visualize the user's work results, allowing them to understand them intuitively. For example, it can display project progress in graphs and charts to provide a visual understanding. It can also represent work results in infographics to highlight important points. It can also display the user's work performance on a dashboard, allowing them to monitor it in real time. This allows users to intuitively understand their work results, thereby improving work efficiency.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The dedicated programming language creation unit converts business communications into a dedicated programming language. For example, it includes syntax for various business documents such as meeting minutes, reports, and email templates. The dedicated programming language creation unit has grammar and vocabulary specialized for the business, and is designed to allow business content to be described concisely and clearly. Step 2: In the generation AI collaboration unit, the generation AI analyzes the content written in a dedicated programming language and generates business documents based on that content. For example, if an instruction to "create minutes of a meeting" is written in a programming language, the generation AI analyzes the instruction and generates specific minutes of the meeting. The generation AI also has the ability to automatically detect and correct typos and misuses when generating business documents. Furthermore, the generation AI uses natural, non-intuitive expressions when generating business documents. This enables the communication system according to the embodiment to carry out business communication accurately, quickly, and efficiently.

[0045] (Example 2) A communication system according to an embodiment of the present invention converts business communications into a programming language and generates business documents using a generation AI. This enables the communication system to carry out business communications accurately, quickly, and efficiently.

[0046] A communication system according to an embodiment includes a dedicated programming language creation unit and a generation AI linkage unit. The dedicated programming language creation unit converts business communications into a dedicated programming language. For example, the dedicated programming language creation unit includes syntax for various business documents, such as meeting minutes, reports, and email templates. The dedicated programming language creation unit has grammar and vocabulary specialized for each business, and is designed to concisely and clearly describe business content. The generation AI linkage unit analyzes content written in the dedicated programming language using a generation AI and generates business documents based on that content. For example, if an instruction such as "Create minutes of the meeting" is written in a programming language, the generation AI analyzes the instruction and generates specific minutes. The generation AI also has the ability to automatically detect and correct typos and misuse when generating business documents. For example, if the generation AI mistakenly inputs the expression "Thank you for your kind support" as "Thank you for your kind support," the AI ​​automatically corrects it. Furthermore, the generation AI uses natural, non-intuitive expressions when generating business documents. For example, it converts the expression "Please confirm" into a polite expression such as "I'm sorry to trouble you, but could you please confirm?" As a result, the communication system according to the embodiment can carry out business communication accurately, quickly, and efficiently. For example, the generation AI can handle documents in various formats, such as meeting minutes, reports, emails, and presentation materials. This improves business efficiency.

[0047] The dedicated programming language creation unit can create different dedicated programming languages ​​for different types of business and provide grammar and vocabulary optimized for each business. For example, the dedicated programming language creation unit creates a dedicated programming language for meeting minutes and provides grammar and vocabulary specialized for minutes. For example, the dedicated programming language creation unit includes syntax such as agenda, speaker, speech content, and decisions. The dedicated programming language creation unit also creates a dedicated programming language for sales and provides grammar and vocabulary specialized for sales activities. For example, the dedicated programming language creation unit includes syntax such as customer information, business negotiation content, and contract terms. The dedicated programming language creation unit also creates a dedicated programming language for marketing and provides grammar and vocabulary specialized for marketing activities. For example, the dedicated programming language creation unit includes syntax such as campaign information, advertising content, and target market. This provides grammar and vocabulary optimized for each business, thereby improving business efficiency.

[0048] When analyzing a dedicated programming language, the generative AI collaboration unit automatically collects background information to understand the business context, thereby improving analysis accuracy. For example, the generative AI collaboration unit builds a system that automatically collects relevant background information so that the generative AI can understand the business context. For example, it collects detailed project information and past meeting records. The generative AI collaboration unit also refers to relevant literature and databases to understand the business context. For example, it collects information on industry trends and market movements. Furthermore, the generative AI collaboration unit collects relevant news articles and academic papers to understand the business context. For example, it collects information on the latest research results and technological trends. This allows background information to be automatically collected to understand the business context, thereby improving analysis accuracy.

[0049] The generation AI linkage unit can add an interactive function that feeds back the analysis results to the user and allows the user to provide corrections or additional information. For example, the generation AI linkage unit adds an interactive function that allows the generation AI to feed back the analysis results to the user and allows the user to provide corrections or additional information. For example, comments and corrections can be made to the analysis results in real time. The generation AI linkage unit also provides an interface that allows the user to provide corrections or additional information. For example, it provides a form that allows the user to check the analysis results and enter corrections or additional information. Furthermore, when the user provides corrections or additional information, the generation AI linkage unit analyzes the content and provides feedback again. For example, the generation AI regenerates the business document based on the corrections and additional information provided by the user. In this way, by adding an interactive function that allows the user to provide corrections and additional information, the accuracy of the analysis can be improved.

[0050] The generation AI linkage unit uses the emotion estimation function to provide analysis results according to the user's emotions and generate emotionally appropriate sentences. The generation AI linkage unit, for example, uses the emotion estimation function to build a system that provides analysis results according to the user's emotions. For example, if the user has positive emotions, it provides analysis results using cheerful expressions. The generation AI linkage unit also uses an emotion estimation algorithm to provide analysis results according to the user's emotions. For example, it analyzes the user's facial expressions and voice to estimate their emotions. Furthermore, the generation AI linkage unit references an emotion database to provide analysis results according to the user's emotions. For example, it estimates the user's emotions based on past emotion data. This makes it possible to provide analysis results according to the user's emotions and generate emotionally appropriate sentences.

[0051] The generation AI linkage unit can detect and correct typos and misuse. For example, when the generation AI detects typos and misuse, the generation AI linkage unit registers business terminology and abbreviations in a database and detects typos and misuse based on that. For example, it accurately recognizes terminology and abbreviations used in a specific industry. The generation AI linkage unit also uses natural language processing technology to detect typos and misuse. For example, it performs grammar analysis and spell checking. Furthermore, the generation AI linkage unit uses an automatic correction algorithm to correct typos and misuse. For example, it automatically corrects typos and misuse. This makes it possible to detect and correct typos and misuse, thereby generating accurate business documents.

[0052] The generation AI linkage unit can add an interactive function that provides feedback to the user on the results of detection of typos and misuses and allows the user to confirm corrections. The generation AI linkage unit, for example, provides feedback to the user on the results of detection of typos and misuses and allows the user to confirm corrections. For example, the detected typos and misuses can be highlighted so the user can confirm the corrections. The generation AI linkage unit also provides an interface for the user to confirm the corrections. For example, it provides a button for the user to check the detection results and approve the corrections. Furthermore, when the user approves the corrections, the generation AI analyzes the content and provides feedback again. For example, after the user approves the corrections, the generation AI generates the business text again. In this way, by adding an interactive function that allows the user to confirm the corrections, the accuracy of the text can be improved.

[0053] The generation AI linkage unit can use the emotion estimation function to suggest corrections to typos and misuses based on the user's emotions. For example, the generation AI linkage unit uses the emotion estimation function to build a system that suggests corrections to typos and misuses based on the user's emotions. For example, if the user has positive emotions, the generation AI linkage unit suggests corrections using cheerful expressions. The generation AI linkage unit also uses an emotion estimation algorithm to suggest corrections based on the user's emotions. For example, it analyzes the user's facial expressions and voice to estimate their emotions. Furthermore, the generation AI linkage unit references an emotion database to suggest corrections based on the user's emotions. For example, it estimates the user's emotions based on past emotion data. This makes it possible to generate emotionally appropriate sentences by suggesting corrections to typos and misuses based on the user's emotions.

[0054] The Generative AI Collaboration Unit can automatically translate business documents into other languages ​​and support international business communication. For example, the Generative AI Collaboration Unit builds a system that automatically translates business documents generated by the Generative AI into multiple languages, such as English, French, and Chinese. For example, meeting minutes can be shared in multiple languages. The Generative AI Collaboration Unit also uses machine translation algorithms to automatically translate business documents. For example, it uses neural machine translation (NMT) to perform highly accurate translations. Furthermore, the Generative AI Collaboration Unit sets standards for evaluating translation accuracy. For example, it evaluates translation accuracy using the BLEU score or TER score. This makes it possible to automatically translate business documents into other languages ​​and support international business communication.

[0055] The generation AI linkage unit can visualize business documents to enable users to intuitively understand them. For example, the generation AI linkage unit builds a system that visualizes business documents generated by the generation AI to enable users to intuitively understand them. For example, it displays the generated documents using graphs and charts. The generation AI linkage unit also uses data visualization technology to visualize business documents. For example, it creates graphs and charts using D3.js or Chart.js. Furthermore, the generation AI linkage unit provides a function that allows users to interactively manipulate visualized information to enable users to intuitively understand it. For example, a user can click on a graph or chart to display detailed information. In this way, business documents can be visualized to enable users to intuitively understand them.

[0056] The generation AI linkage unit uses the emotion estimation function to generate business documents based on the user's emotions, providing expressions that are easy to empathize with emotionally. The generation AI linkage unit, for example, uses the emotion estimation function to build a system that generates business documents based on the user's emotions. For example, if the user has positive emotions, it generates documents using cheerful expressions. The generation AI linkage unit also uses an emotion estimation algorithm to generate business documents based on the user's emotions. For example, it analyzes the user's facial expressions and voice to estimate their emotions. Furthermore, the generation AI linkage unit references an emotion database to generate business documents based on the user's emotions. For example, it estimates the user's emotions based on past emotion data. This makes it possible to generate business documents based on the user's emotions and provide expressions that are easy to empathize with emotionally.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The generation AI collaboration unit can estimate the user's emotions and provide appropriate feedback to the user based on the estimated emotions. For example, if the user is feeling stressed, the generation AI collaboration unit provides feedback using expressions that will help the user to relax. If the user is happy, the generation AI collaboration unit provides positive feedback that shares that joy. Furthermore, if the user is feeling anxious, the generation AI collaboration unit provides feedback that gives a sense of security and eases that anxiety. In this way, by providing feedback that corresponds to the user's emotions, it is possible to improve user satisfaction.

[0059] The generation AI linkage unit can estimate the user's emotions and adjust the tone of business text to suit the user based on the estimated emotions. For example, if the user is angry, the generation AI linkage unit generates text with a calm and composed tone. If the user is sad, the generation AI linkage unit generates text with a comforting and gentle tone. Furthermore, if the user is excited, the generation AI linkage unit generates text with an energetic tone that shares the user's excitement. This allows the quality of communication to be improved by generating business text with a tone that matches the user's emotions.

[0060] The generation AI collaboration unit can estimate the user's emotions and provide appropriate advice to the user based on the estimated emotions. For example, if the user is unsure, the generation AI collaboration unit will present specific options and provide advice to support the user's decision. If the user is confident, the generation AI collaboration unit will provide advice that will boost that confidence. Furthermore, if the user is tired, the generation AI collaboration unit will provide advice encouraging the user to take a rest. In this way, by providing advice that corresponds to the user's emotions, the system can support the user's actions.

[0061] The generation AI linkage unit can estimate the user's emotions and provide learning content appropriate for the user based on the estimated emotions. For example, if the user is interested, the generation AI linkage unit provides attractive learning content that will maintain that interest. If the user is tired, the generation AI linkage unit provides light learning content that will help the user relax. Furthermore, if the user is impatient, the generation AI linkage unit provides content that will advance the learning at a pace that will reduce the user's impatience. In this way, learning effectiveness can be improved by providing learning content that corresponds to the user's emotions.

[0062] The generation AI collaboration unit can estimate the user's emotions and provide appropriate reminders to the user based on the estimated emotions. For example, if the user is busy, the generation AI collaboration unit will prioritize reminders of important tasks. Also, if the user is relaxing, the generation AI collaboration unit will adjust the frequency of reminders so as not to disturb the user's relaxation. Furthermore, if the user is forgetful, the generation AI collaboration unit will repeatedly remind the user to help them remember important tasks. This makes task management more efficient by providing reminders that correspond to the user's emotions.

[0063] The Generative AI Collaboration Unit can evaluate the user's work performance and suggest areas for improvement. For example, it can evaluate the user's work speed and accuracy and provide specific advice for improving efficiency. It can also evaluate the user's communication skills and suggest training programs for improvement. It can also evaluate the user's stress level and provide resources for stress management. This allows it to make specific suggestions to improve the user's work performance.

[0064] The Generative AI Collaboration Unit can analyze the user's work history and provide support for creating future work plans. For example, it analyzes the success and failure factors of past projects and reflects these in future project plans. It can also analyze the user's skill set and propose training plans for skill improvement. It can also analyze the user's workload and provide advice on appropriate task allocation. This allows it to support the user in creating efficient work plans.

[0065] The Generative AI Collaboration Unit can monitor the user's work environment and provide advice to provide an optimal work environment. For example, it can monitor the lighting and temperature of the workspace and provide advice to maintain an optimal environment. It can also monitor the user's posture and movements and provide advice to maintain a healthy working posture. It can also monitor the user's working hours and provide advice to encourage them to take appropriate breaks. This makes it possible to provide specific advice to optimize the user's work environment.

[0066] The Generative AI Collaboration Unit can analyze the user's business data and propose improvements to business processes. For example, it can identify bottlenecks in the business flow and propose specific improvements to improve efficiency. It can also analyze the user's work patterns and propose optimal work schedules. Furthermore, it can identify parts of the business that can be automated based on the user's business data and make specific proposals for automation. This makes it possible to make specific proposals to improve the efficiency of business processes.

[0067] The Generative AI Collaboration Unit can visualize the user's work results, allowing them to understand them intuitively. For example, it can display project progress in graphs and charts to provide a visual understanding. It can also represent work results in infographics to highlight important points. It can also display the user's work performance on a dashboard, allowing them to monitor it in real time. This allows users to intuitively understand their work results, thereby improving work efficiency.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The dedicated programming language creation unit converts business communications into a dedicated programming language. For example, it includes syntax for various business documents such as meeting minutes, reports, and email templates. The dedicated programming language creation unit has grammar and vocabulary specialized for the business, and is designed to allow business content to be described concisely and clearly. Step 2: In the generation AI collaboration unit, the generation AI analyzes the content written in a dedicated programming language and generates business documents based on that content. For example, if an instruction to "create minutes of a meeting" is written in a programming language, the generation AI analyzes the instruction and generates specific minutes of the meeting. The generation AI also has the ability to automatically detect and correct typos and misuses when generating business documents. Furthermore, the generation AI uses natural, non-intuitive expressions when generating business documents. This enables the communication system according to the embodiment to carry out business communication accurately, quickly, and efficiently.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0083] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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]

[0137] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dedicated programming language creation unit that converts business communications into a dedicated programming language; a generation AI collaboration unit that analyzes the dedicated programming language and generates business documents; A system characterized by:

2. The dedicated programming language creation unit Create a dedicated programming language for each type of business, and provide grammar and vocabulary optimized for each business.

2. The system of claim 1.

3. The generation AI collaboration unit When analyzing the dedicated programming language, background information is automatically collected to understand the business context, improving the accuracy of the analysis.

2. The system of claim 1.

4. The generation AI collaboration unit Detect and correct typos and misuse 2. The system of claim 1.

5. The generation AI collaboration unit Providing analysis results according to the user's emotions and generating emotionally appropriate sentences 2. The system of claim 1.

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