System

Abstract

An object of a system according to an embodiment is to efficiently and automatically generate a handover document.SOLUTION: A system according to an embodiment includes a material reading unit, an information organization unit, and a document generation unit. The material reading unit reads an existing material. The information organization part organizes the material read by the material reading part. The document generation unit generates a handover document based on the information organized by the information organization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating handover documents is time-consuming and labor-intensive, making it difficult to do so efficiently.

[0005] The system according to the embodiment aims to efficiently and automatically generate handover documents. [Means for solving the problem]

[0006] The system according to the embodiment includes a material reading unit, an information organizing unit, and a document generating unit. The material reading unit reads existing materials. The information organizing unit organizes the materials read by the material reading unit. The document generating unit generates a handover document based on the information organized by the information organizing unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate handover documents efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The handover document automatic generation system according to an embodiment of the present invention uses a generation AI to automatically generate handover documents for new employees or employees taking on new positions. This system reads existing documents, organizes them using the generation AI, and compiles the necessary information into handover documents. This allows the handover document automatic generation system to efficiently and accurately create handover documents.

[0029] The handover document automatic generation system according to the embodiment includes a material reading unit, an information organizing unit, and a document generation unit. The material reading unit reads existing materials. For example, it can read past project reports, business manuals, email correspondence, etc. as input information. The material reading unit can also directly read digital materials. For example, it reads digital files such as PDFs and Word documents. The material reading unit can also obtain materials from a database via an API. For example, it can obtain necessary materials from a company's internal database. The information organizing unit organizes the read materials. For example, the generation AI analyzes the contents of the materials and extracts important information. The information organizing unit also determines the relevance and importance of the materials and categorizes the information. For example, it organizes project progress, important contacts, work procedures, etc. The information organizing unit also tags the information and stores it in a database. For example, it tags each piece of information to make it easier to search. The document generation unit generates handover documents based on the organized information. For example, the generation AI creates work procedure manuals for new employees and project summaries for employees taking on new positions. The document generation unit generates documents in an appropriate format and layout based on user instructions. For example, it creates documents according to a format specified by the user. Furthermore, the document generation unit can customize the generated documents. For example, it can emphasize specific information or add additional explanations. This allows the handover document automatic generation system according to the embodiment to efficiently and accurately create handover documents. For example, this allows new employees to start their work smoothly and employees taking on new positions to quickly take over their work.

[0030] The document reading unit can analyze metadata such as the date and time the document was created or the creator's position, and evaluate the reliability and importance of the information. For example, when the generation AI reads a document, the document reading unit analyzes the date and time the document was created and prioritizes extracting the most recent information. For example, it prioritizes analyzing the most recent project reports and business manuals. The document reading unit also evaluates the reliability of information based on the creator's position. For example, it evaluates documents created by senior management as highly reliable. Furthermore, the document reading unit evaluates the importance of information based on metadata. For example, it prioritizes documents related to important projects. This makes it possible to evaluate the reliability and importance of the information.

[0031] The document reading unit also analyzes the contents of the document as audio data, and can also import oral explanations and meeting records using voice recognition technology. For example, when the generation AI reads a document, the document reading unit also analyzes the audio data and imports oral explanations and meeting records. For example, it analyzes audio recordings of meetings and extracts important remarks. The document reading unit also converts audio data into text data using voice recognition technology. For example, it converts recordings of meetings into text and imports them as documents. Furthermore, the document reading unit evaluates the importance of information based on the audio data. For example, it prioritizes parts with a large number of important remarks. This makes it possible to analyze audio data and import oral explanations and meeting records.

[0032] The document reading unit can also analyze multimedia data such as images or videos and capture visual and auditory information. For example, when the generative AI reads documents, the document reading unit can also analyze multimedia data such as images and videos and capture visual and auditory information. For example, it can analyze graphs and videos showing project progress. The document reading unit can also analyze image data using image recognition technology. For example, it can convert the contents of images into text data and capture it as documents. Furthermore, the document reading unit can analyze video data and extract important scenes. For example, it can highlight important parts in a presentation video. This allows the multimedia data to be analyzed and visual and auditory information to be captured.

[0033] The document reading unit automatically translates documents written in different languages, and the generation AI can generate handover documents that support multiple languages. The document reading unit, for example, automatically translates documents written in different languages ​​using the generation AI to generate handover documents that support multiple languages. For example, it translates documents in English or Chinese into Japanese. The document reading unit also performs highly accurate translation using machine translation technology. For example, it achieves natural translation using neural network translation. Furthermore, the document reading unit generates handover documents based on the translated documents. For example, it creates PDFs or Word documents in multiple languages. This makes it possible to automatically translate documents in different languages ​​and generate handover documents that support multiple languages.

[0034] The information organization unit can refer to the latest trends or news in the related industry and reflect the latest information. For example, when the generation AI summarizes information, the information organization unit automatically collects the latest trends and news in the related industry and reflects them in the summary. For example, it incorporates the latest technological trends and market needs. The information organization unit also obtains the latest information using a news API. For example, it analyzes industry news feeds and extracts important information. Furthermore, the information organization unit updates the summary based on the latest information. For example, it adds information about new technologies and products. This allows the latest industry information to be reflected.

[0035] The information organization unit can visualize the interrelationships of information as graph data, allowing the user to intuitively understand. For example, when the generation AI organizes information, the information organization unit visualizes the interrelationships of information as graph data, allowing the user to intuitively understand. For example, the progress of a project is displayed in a graph. The information organization unit also visualizes the relevance of information using a network graph. For example, related information is represented by nodes and edges. Furthermore, the information organization unit evaluates the importance of information based on the graph data. For example, important nodes are highlighted. This makes it possible to visualize the interrelationships of information in a way that allows the user to intuitively understand them.

[0036] The information organization unit can convert the summarized information into different formats to make it easier to understand visually. For example, the information organization unit converts the information summarized by the generation AI into presentation materials to make it easier to understand visually. For example, it organizes the information in a slide format. The information organization unit also visualizes the information using infographics. For example, it represents the information using graphs and charts. Furthermore, the information organization unit converts the summarized information into a report format. For example, it creates a document including detailed explanations. This allows the summarized information to be converted into different formats to make it easier to understand visually.

[0037] The information organization unit can have the summarized information reviewed by experts in different industries and fields, and reorganize the information based on the feedback. For example, the information organization unit can have the information summarized by the generation AI reviewed by experts in different industries and fields, and reorganize the information based on the feedback. For example, it can ask technical experts and design experts to review the information. The information organization unit can also modify the information based on the feedback. For example, it can update the information to reflect the opinions of the experts. Furthermore, the information organization unit can reclassify the information based on the feedback. For example, it can review the categories of the information and make a more appropriate classification. This makes it possible to reorganize the information based on feedback from experts in different industries and fields.

[0038] The document generation unit can refer to past success stories and best practices and include practical advice. For example, when the generation AI generates handover documents, the document generation unit can refer to past success stories and include practical advice. For example, it can incorporate methods and strategies that have been successful in past projects. The document generation unit also provides advice based on industry best practices. For example, it can introduce industry-standard procedures and technologies. Furthermore, the document generation unit provides specific procedures based on success stories and best practices. For example, it can provide specific step-by-step guides. This makes it possible to refer to past success stories and best practices and include practical advice.

[0039] The document generation unit can reflect user feedback in real time and continuously optimize the document. For example, when the generation AI takes over and generates a document, the document generation unit collects user feedback in real time and continuously optimizes the document. For example, it modifies the document by reflecting the user's opinions. The document generation unit also updates the content of the document based on the real-time feedback. For example, it immediately corrects errors pointed out by the user. Furthermore, the document generation unit adjusts the layout of the document based on the feedback. For example, it changes the layout to suit the user's preferences. This allows the document to be continuously optimized by reflecting user feedback in real time.

[0040] The document generation unit can convert the generated handover document into a format that can be viewed on different devices. For example, the document generation unit converts the handover document generated by the generation AI into a format that can be viewed on a smartphone or tablet. For example, it adjusts the layout to be mobile-friendly. The document generation unit also converts the document format to be compatible with different devices. For example, it converts it into PDF or HTML format. Furthermore, the document generation unit provides the optimal display method depending on the device. For example, it displays detailed information on a desktop and concise information on a mobile device. This makes it possible to convert the document into a format that can be viewed on different devices.

[0041] The document generation unit can link the generated handover documents with different business processes and systems and automatically update the document contents. For example, the document generation unit links the handover documents generated by the generation AI with different business processes and systems and automatically updates the document contents. For example, it can link with a project management system to automatically reflect progress. The document generation unit also synchronizes data with other systems via API. For example, it can link with a CRM system to automatically update customer information. Furthermore, the document generation unit updates documents in response to changes in business processes. For example, it automatically modifies documents when new procedures are added. This makes it possible to link with different business processes and systems and automatically update the document contents.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The document reading unit can analyze metadata such as the date and time the document was created or the creator's position to evaluate the reliability and importance of the information. For example, when the generation AI reads a document, it analyzes the date and time the document was created and prioritizes extracting the most recent information. For example, it might prioritize analyzing the most recent project reports or business manuals. The document reading unit also evaluates the reliability of the information based on the creator's position. For example, it might rate documents created by senior management as highly reliable. The document reading unit also evaluates the importance of the information based on metadata. For example, it might prioritize documents related to important projects. This makes it possible to evaluate the reliability and importance of the information.

[0044] The document reading unit also analyzes the contents of the documents as audio data, and can also import oral explanations and meeting records using voice recognition technology. For example, when the generation AI reads documents, it also analyzes the audio data and imports oral explanations and meeting records. For example, it analyzes audio recordings of meetings and extracts important remarks. The document reading unit also converts audio data into text data using voice recognition technology. For example, it converts recordings of meetings into text and imports them as documents. Furthermore, the document reading unit evaluates the importance of information based on the audio data. For example, it prioritizes parts with a large number of important remarks. This makes it possible to analyze audio data and import oral explanations and meeting records.

[0045] The document reading unit can also analyze multimedia data such as images or videos and capture visual and auditory information. For example, when the generative AI reads documents, it also analyzes multimedia data such as images and videos and captures visual and auditory information. For example, it analyzes graphs and videos showing project progress. The document reading unit also analyzes image data using image recognition technology. For example, it converts the contents of images into text data and imports it as documents. Furthermore, the document reading unit analyzes video data and extracts important scenes. For example, it highlights important parts of a presentation video. This allows it to analyze multimedia data and capture visual and auditory information.

[0046] The document reading unit automatically translates documents written in different languages, and the generation AI can generate handover documents that support multiple languages. For example, the generation AI can automatically translate documents written in different languages ​​and generate handover documents that support multiple languages. For example, it can translate documents in English or Chinese into Japanese. The document reading unit also performs highly accurate translations using machine translation technology. For example, it can achieve natural translations using neural network translation. The document reading unit then generates handover documents based on the translated documents. For example, it can create PDFs or Word documents in multiple languages. This makes it possible to automatically translate documents in different languages ​​and generate handover documents that support multiple languages.

[0047] The information organization unit can refer to the latest trends or news in the related industry and reflect the latest information. For example, when the generation AI summarizes information, it automatically collects the latest trends and news in the related industry and reflects them in the summary. For example, it incorporates the latest technological trends and market needs. The information organization unit also obtains the latest information using a news API. For example, it analyzes industry news feeds and extracts important information. The information organization unit then updates the summary based on the latest information. For example, it adds information about new technologies and products. This allows the latest industry information to be reflected.

[0048] The information organization unit can visualize the interrelationships of information as graph data, allowing users to intuitively understand them. For example, when the generative AI organizes information, it visualizes the interrelationships of information as graph data, allowing users to intuitively understand them. For example, it displays the progress of a project in a graph. The information organization unit also visualizes the relevance of information using a network graph. For example, it represents related information with nodes and edges. Furthermore, the information organization unit evaluates the importance of information based on the graph data. For example, it highlights important nodes. This makes it possible to visualize the interrelationships of information in a way that allows users to intuitively understand them.

[0049] The information organizer can convert the summarized information into different formats to make it easier to understand visually. For example, the generative AI can convert the summarized information into presentation materials to make it easier to understand visually. For example, it can organize the information in a slide format. The information organizer can also visualize the information using infographics. For example, it can represent the information using graphs and charts. Furthermore, the information organizer can convert the summarized information into a report format. For example, it can create a document that includes detailed explanations. This allows the summarized information to be converted into different formats to make it easier to understand visually.

[0050] The information organization unit can have the summarized information reviewed by experts from different industries and fields, and reorganize the information based on the feedback. For example, the information summarized by the generative AI can be reviewed by experts from different industries and fields, and the information can be reorganized based on the feedback. For example, technical experts and design experts can be asked to review the information. The information organization unit can also revise the information based on the feedback. For example, it can update the information to reflect the opinions of the experts. Furthermore, the information organization unit can reclassify the information based on the feedback. For example, it can review the categories of the information and make it a more appropriate classification. This makes it possible to reorganize the information based on feedback from experts from different industries and fields.

[0051] The document generation unit can refer to past success stories and best practices and include practical advice. For example, when the generation AI generates handover documents, it can refer to past success stories and include practical advice. For example, it can incorporate methods and strategies that have been successful in past projects. The document generation unit also provides advice based on industry best practices. For example, it can introduce industry-standard procedures and technologies. Furthermore, the document generation unit provides specific procedures based on success stories and best practices. For example, it can provide specific step-by-step guides. This makes it possible to refer to past success stories and best practices and include practical advice.

[0052] The document generation unit can reflect user feedback in real time and continuously optimize the document. For example, when the generation AI takes over and generates a document, it collects user feedback in real time and continuously optimizes the document. For example, it modifies the document by reflecting the user's opinions. The document generation unit also updates the content of the document based on real-time feedback. For example, it immediately corrects errors pointed out by the user. Furthermore, the document generation unit adjusts the layout of the document based on the feedback. For example, it changes the layout to suit the user's preferences. This allows the document to be continuously optimized by reflecting user feedback in real time.

[0053] The document generation unit can convert the generated handover document into a format that can be viewed on different devices. For example, it converts the handover document generated by the generation AI into a format that can be viewed on a smartphone or tablet. For example, it adjusts the layout to be mobile-friendly. The document generation unit also converts the document format to be compatible with different devices. For example, it converts it into PDF or HTML format. Furthermore, the document generation unit provides the optimal display method depending on the device. For example, it displays detailed information on a desktop and concise information on a mobile device. This makes it possible to convert the document into a format that can be viewed on different devices.

[0054] The document generation unit can link the generated handover documents with different business processes and systems, automatically updating the document contents. For example, the handover documents generated by the generation AI can be linked with different business processes and systems, automatically updating the document contents. For example, it can link with a project management system to automatically reflect progress. The document generation unit also synchronizes data with other systems via API. For example, it can link with a CRM system to automatically update customer information. Furthermore, the document generation unit updates documents in response to changes in business processes. For example, it automatically modifies documents when new procedures are added. This makes it possible to link with different business processes and systems, automatically updating the document contents.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The document reader loads existing documents. For example, past project reports, business manuals, email correspondence, etc. can be loaded as input information. Digital documents can also be loaded directly. For example, digital files such as PDFs and Word documents can be loaded. Furthermore, documents can be retrieved from databases via API. For example, the required documents can be retrieved from a company's internal database. Step 2: The information organizer organizes the loaded materials. For example, the generation AI analyzes the contents of the materials and extracts important information. It also determines the relevance and importance of the materials and categorizes the information. For example, it organizes project progress, important contacts, and work procedures. It then tags the information and stores it in a database. For example, it tags each piece of information to make it easier to search. Step 3: The document generation unit generates handover documents based on the organized information. For example, the generation AI creates work procedures for new employees and project summaries for employees taking on new positions. It also generates documents in the appropriate format and layout based on user instructions. For example, it creates documents according to the format specified by the user. Furthermore, the generated documents can be customized, for example, by emphasizing specific information or adding additional explanations.

[0057] (Example 2) The handover document automatic generation system according to an embodiment of the present invention uses a generation AI to automatically generate handover documents for new employees or employees taking on new positions. This system reads existing documents, organizes them using the generation AI, and compiles the necessary information into handover documents. This allows the handover document automatic generation system to efficiently and accurately create handover documents.

[0058] The handover document automatic generation system according to the embodiment includes a material reading unit, an information organizing unit, and a document generation unit. The material reading unit reads existing materials. For example, it can read past project reports, business manuals, email correspondence, etc. as input information. The material reading unit can also directly read digital materials. For example, it reads digital files such as PDFs and Word documents. The material reading unit can also obtain materials from a database via an API. For example, it can obtain necessary materials from a company's internal database. The information organizing unit organizes the read materials. For example, the generation AI analyzes the contents of the materials and extracts important information. The information organizing unit also determines the relevance and importance of the materials and categorizes the information. For example, it organizes project progress, important contacts, work procedures, etc. The information organizing unit also tags the information and stores it in a database. For example, it tags each piece of information to make it easier to search. The document generation unit generates handover documents based on the organized information. For example, the generation AI creates work procedure manuals for new employees and project summaries for employees taking on new positions. The document generation unit generates documents in an appropriate format and layout based on user instructions. For example, it creates documents according to a format specified by the user. Furthermore, the document generation unit can customize the generated documents. For example, it can emphasize specific information or add additional explanations. This allows the handover document automatic generation system according to the embodiment to efficiently and accurately create handover documents. For example, this allows new employees to start their work smoothly and employees taking on new positions to quickly take over their work.

[0059] The document reading unit can use the emotion estimation function to analyze the emotions of the document creator and prioritize extracting emotionally important parts. For example, when the generation AI reads a document, the document reading unit uses the emotion estimation function to analyze the emotions of the document creator and prioritize extracting emotionally strong parts. For example, it extracts particularly emphasized parts in a project report or parts with many emotional expressions. The document reading unit also evaluates the importance of the document based on the emotion score. For example, it prioritizes parts with a high emotion score. Furthermore, the document reading unit uses the emotion estimation function to capture the emotional nuances of the document. For example, it analyzes changes in the emotions of the document creator and identifies emotionally important parts. This makes it possible to prioritize extracting emotionally important information.

[0060] The document reading unit can analyze metadata such as the date and time the document was created or the creator's position, and evaluate the reliability and importance of the information. For example, when the generation AI reads a document, the document reading unit analyzes the date and time the document was created and prioritizes extracting the most recent information. For example, it prioritizes analyzing the most recent project reports and business manuals. The document reading unit also evaluates the reliability of information based on the creator's position. For example, it evaluates documents created by senior management as highly reliable. Furthermore, the document reading unit evaluates the importance of information based on metadata. For example, it prioritizes documents related to important projects. This makes it possible to evaluate the reliability and importance of the information.

[0061] The document reading unit also analyzes the contents of the document as audio data, and can also import oral explanations and meeting records using voice recognition technology. For example, when the generation AI reads a document, the document reading unit also analyzes the audio data and imports oral explanations and meeting records. For example, it analyzes audio recordings of meetings and extracts important remarks. The document reading unit also converts audio data into text data using voice recognition technology. For example, it converts recordings of meetings into text and imports them as documents. Furthermore, the document reading unit evaluates the importance of information based on the audio data. For example, it prioritizes parts with a large number of important remarks. This makes it possible to analyze audio data and import oral explanations and meeting records.

[0062] The document reading unit can also analyze multimedia data such as images or videos and capture visual and auditory information. For example, when the generative AI reads documents, the document reading unit can also analyze multimedia data such as images and videos and capture visual and auditory information. For example, it can analyze graphs and videos showing project progress. The document reading unit can also analyze image data using image recognition technology. For example, it can convert the contents of images into text data and capture it as documents. Furthermore, the document reading unit can analyze video data and extract important scenes. For example, it can highlight important parts in a presentation video. This allows the multimedia data to be analyzed and visual and auditory information to be captured.

[0063] The document reading unit automatically translates documents written in different languages, and the generation AI can generate handover documents that support multiple languages. The document reading unit, for example, automatically translates documents written in different languages ​​using the generation AI to generate handover documents that support multiple languages. For example, it translates documents in English or Chinese into Japanese. The document reading unit also performs highly accurate translation using machine translation technology. For example, it achieves natural translation using neural network translation. Furthermore, the document reading unit generates handover documents based on the translated documents. For example, it creates PDFs or Word documents in multiple languages. This makes it possible to automatically translate documents in different languages ​​and generate handover documents that support multiple languages.

[0064] The document reading unit uses the emotion estimation function to monitor the user's emotions in real time when reading documents, and can prioritize extract information that interests the user. For example, when the generation AI reads documents, the document reading unit uses the emotion estimation function to monitor the user's emotions in real time and prioritize extract information that interests the user. For example, it highlights parts that the user showed interest in. The document reading unit also evaluates the importance of information based on the emotion score. For example, it prioritizes parts with high emotion scores. Furthermore, the document reading unit analyzes changes in the user's emotions and identifies information that interests the user. For example, it prioritizes topics that the user showed interest in. This makes it possible to monitor the user's emotions in real time and prioritize extract information that interests the user.

[0065] The information organizing unit can use the emotion estimation function to devise the arrangement and expression of information that elicits positive emotions. For example, when the generation AI organizes information, the information organizing unit uses the emotion estimation function to devise the arrangement of information that elicits positive emotions. For example, it emphasizes success stories and positive feedback. The information organizing unit also organizes information using positive expressions. For example, it includes positive words and encouraging messages. Furthermore, the information organizing unit optimizes the arrangement of information based on the emotion score. For example, it places parts with high emotion scores in prominent positions. This makes it possible to devise the arrangement and expression of information that elicits positive emotions.

[0066] The information organization unit can refer to the latest trends or news in the related industry and reflect the latest information. For example, when the generation AI summarizes information, the information organization unit automatically collects the latest trends and news in the related industry and reflects them in the summary. For example, it incorporates the latest technological trends and market needs. The information organization unit also obtains the latest information using a news API. For example, it analyzes industry news feeds and extracts important information. Furthermore, the information organization unit updates the summary based on the latest information. For example, it adds information about new technologies and products. This allows the latest industry information to be reflected.

[0067] The information organization unit can visualize the interrelationships of information as graph data, allowing the user to intuitively understand. For example, when the generation AI organizes information, the information organization unit visualizes the interrelationships of information as graph data, allowing the user to intuitively understand. For example, the progress of a project is displayed in a graph. The information organization unit also visualizes the relevance of information using a network graph. For example, related information is represented by nodes and edges. Furthermore, the information organization unit evaluates the importance of information based on the graph data. For example, important nodes are highlighted. This makes it possible to visualize the interrelationships of information in a way that allows the user to intuitively understand them.

[0068] The information organization unit can convert the summarized information into different formats to make it easier to understand visually. For example, the information organization unit converts the information summarized by the generation AI into presentation materials to make it easier to understand visually. For example, it organizes the information in a slide format. The information organization unit also visualizes the information using infographics. For example, it represents the information using graphs and charts. Furthermore, the information organization unit converts the summarized information into a report format. For example, it creates a document including detailed explanations. This allows the summarized information to be converted into different formats to make it easier to understand visually.

[0069] The information organization unit can have the summarized information reviewed by experts in different industries and fields, and reorganize the information based on the feedback. For example, the information organization unit can have the information summarized by the generation AI reviewed by experts in different industries and fields, and reorganize the information based on the feedback. For example, it can ask technical experts and design experts to review the information. The information organization unit can also modify the information based on the feedback. For example, it can update the information to reflect the opinions of the experts. Furthermore, the information organization unit can reclassify the information based on the feedback. For example, it can review the categories of the information and make a more appropriate classification. This makes it possible to reorganize the information based on feedback from experts in different industries and fields.

[0070] The information organization unit can collect users' emotional reactions to information summarized using the emotion estimation function, thereby improving the accuracy of the summary. For example, the information organization unit collects users' emotional reactions to information summarized by the generation AI, and improves the accuracy of the summary based on that data. For example, it prioritizes the adoption of summary sentences with a high number of positive reactions. The information organization unit also analyzes users' emotional scores using the emotion estimation function. For example, it emphasizes parts with high emotional scores. Furthermore, the information organization unit modifies the summary sentence based on user feedback. For example, it improves the summary sentence by reflecting the user's opinions. This allows the information organization unit to collect users' emotional reactions and improve the accuracy of the summary.

[0071] The document generation unit can use the emotion estimation function to adopt expressions and layouts that evoke the most positive emotions in the user. For example, when the generation AI generates a handover document, the document generation unit uses the emotion estimation function to adopt expressions that evoke the most positive emotions in the user. For example, it includes positive words and encouraging messages. The document generation unit also adopts a layout that elicits positive emotions. For example, it uses bright colors and visually appealing designs. Furthermore, the document generation unit optimizes the expressions and layouts based on the emotion scores. For example, it places parts with high emotion scores in prominent positions. This makes it possible to adopt expressions and layouts that evoke positive emotions in the user.

[0072] The document generation unit can refer to past success stories and best practices and include practical advice. For example, when the generation AI generates handover documents, the document generation unit can refer to past success stories and include practical advice. For example, it can incorporate methods and strategies that have been successful in past projects. The document generation unit also provides advice based on industry best practices. For example, it can introduce industry-standard procedures and technologies. Furthermore, the document generation unit provides specific procedures based on success stories and best practices. For example, it can provide specific step-by-step guides. This makes it possible to refer to past success stories and best practices and include practical advice.

[0073] The document generation unit can reflect user feedback in real time and continuously optimize the document. For example, when the generation AI takes over and generates a document, the document generation unit collects user feedback in real time and continuously optimizes the document. For example, it modifies the document by reflecting the user's opinions. The document generation unit also updates the content of the document based on the real-time feedback. For example, it immediately corrects errors pointed out by the user. Furthermore, the document generation unit adjusts the layout of the document based on the feedback. For example, it changes the layout to suit the user's preferences. This allows the document to be continuously optimized by reflecting user feedback in real time.

[0074] The document generation unit can convert the generated handover document into a format that can be viewed on different devices. For example, the document generation unit converts the handover document generated by the generation AI into a format that can be viewed on a smartphone or tablet. For example, it adjusts the layout to be mobile-friendly. The document generation unit also converts the document format to be compatible with different devices. For example, it converts it into PDF or HTML format. Furthermore, the document generation unit provides the optimal display method depending on the device. For example, it displays detailed information on a desktop and concise information on a mobile device. This makes it possible to convert the document into a format that can be viewed on different devices.

[0075] The document generation unit can link the generated handover documents with different business processes and systems and automatically update the document contents. For example, the document generation unit links the handover documents generated by the generation AI with different business processes and systems and automatically updates the document contents. For example, it can link with a project management system to automatically reflect progress. The document generation unit also synchronizes data with other systems via API. For example, it can link with a CRM system to automatically update customer information. Furthermore, the document generation unit updates documents in response to changes in business processes. For example, it automatically modifies documents when new procedures are added. This makes it possible to link with different business processes and systems and automatically update the document contents.

[0076] The document generation unit can monitor the user's emotional response to the handover document generated using the emotion estimation function and propose the optimal document format. The document generation unit can, for example, monitor the user's emotional response to the handover document generated by the generation AI and propose the optimal document format. For example, it can adjust the layout based on the user's emotion score. The document generation unit also analyzes changes in the user's emotions using the emotion estimation function. For example, it can highlight parts where the user had a positive reaction. Furthermore, the document generation unit can optimize the document format based on user feedback. For example, it can create a document in a format that matches the user's preferred format. This makes it possible to monitor the user's emotional response and propose the optimal document format.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The document reading unit can analyze metadata such as the date and time the document was created or the creator's position to evaluate the reliability and importance of the information. For example, when the generation AI reads a document, it analyzes the date and time the document was created and prioritizes extracting the most recent information. For example, it might prioritize analyzing the most recent project reports or business manuals. The document reading unit also evaluates the reliability of the information based on the creator's position. For example, it might rate documents created by senior management as highly reliable. The document reading unit also evaluates the importance of the information based on metadata. For example, it might prioritize documents related to important projects. This makes it possible to evaluate the reliability and importance of the information.

[0079] The document reading unit also analyzes the contents of the documents as audio data, and can also import oral explanations and meeting records using voice recognition technology. For example, when the generation AI reads documents, it also analyzes the audio data and imports oral explanations and meeting records. For example, it analyzes audio recordings of meetings and extracts important remarks. The document reading unit also converts audio data into text data using voice recognition technology. For example, it converts recordings of meetings into text and imports them as documents. Furthermore, the document reading unit evaluates the importance of information based on the audio data. For example, it prioritizes parts with a large number of important remarks. This makes it possible to analyze audio data and import oral explanations and meeting records.

[0080] The document reading unit can also analyze multimedia data such as images or videos and capture visual and auditory information. For example, when the generative AI reads documents, it also analyzes multimedia data such as images and videos and captures visual and auditory information. For example, it analyzes graphs and videos showing project progress. The document reading unit also analyzes image data using image recognition technology. For example, it converts the contents of images into text data and imports it as documents. Furthermore, the document reading unit analyzes video data and extracts important scenes. For example, it highlights important parts of a presentation video. This allows it to analyze multimedia data and capture visual and auditory information.

[0081] The document reading unit automatically translates documents written in different languages, and the generation AI can generate handover documents that support multiple languages. For example, the generation AI can automatically translate documents written in different languages ​​and generate handover documents that support multiple languages. For example, it can translate documents in English or Chinese into Japanese. The document reading unit also performs highly accurate translations using machine translation technology. For example, it can achieve natural translations using neural network translation. The document reading unit then generates handover documents based on the translated documents. For example, it can create PDFs or Word documents in multiple languages. This makes it possible to automatically translate documents in different languages ​​and generate handover documents that support multiple languages.

[0082] The document reading unit uses the emotion estimation function to monitor the user's emotions in real time when reading documents, and can prioritize extract information that interests the user. For example, when the generation AI reads documents, it uses the emotion estimation function to monitor the user's emotions in real time and prioritize extract information that interests the user. For example, it highlights parts that the user has shown interest in. The document reading unit also evaluates the importance of information based on the emotion score. For example, it prioritizes parts with high emotion scores. Furthermore, the document reading unit analyzes changes in the user's emotions and identifies information that interests the user. For example, it prioritizes topics that the user has shown interest in. This makes it possible to monitor the user's emotions in real time and prioritize extracting information that interests the user.

[0083] The information organization unit can use the emotion estimation function to devise an arrangement and expression of information that elicits positive emotions. For example, when the generation AI organizes information, it uses the emotion estimation function to devise an arrangement of information that elicits positive emotions. For example, it can emphasize success stories and positive feedback. The information organization unit also organizes information using positive expressions. For example, it can include positive words and encouraging messages. Furthermore, the information organization unit optimizes the arrangement of information based on the emotion score. For example, it can place parts with high emotion scores in prominent positions. This makes it possible to devise an arrangement and expression of information that elicits positive emotions.

[0084] The information organization unit can refer to the latest trends or news in the related industry and reflect the latest information. For example, when the generation AI summarizes information, it automatically collects the latest trends and news in the related industry and reflects them in the summary. For example, it incorporates the latest technological trends and market needs. The information organization unit also obtains the latest information using a news API. For example, it analyzes industry news feeds and extracts important information. The information organization unit then updates the summary based on the latest information. For example, it adds information about new technologies and products. This allows the latest industry information to be reflected.

[0085] The information organization unit can visualize the interrelationships of information as graph data, allowing users to intuitively understand them. For example, when the generative AI organizes information, it visualizes the interrelationships of information as graph data, allowing users to intuitively understand them. For example, it displays the progress of a project in a graph. The information organization unit also visualizes the relevance of information using a network graph. For example, it represents related information with nodes and edges. Furthermore, the information organization unit evaluates the importance of information based on the graph data. For example, it highlights important nodes. This makes it possible to visualize the interrelationships of information in a way that allows users to intuitively understand them.

[0086] The information organizer can convert the summarized information into different formats to make it easier to understand visually. For example, the generative AI can convert the summarized information into presentation materials to make it easier to understand visually. For example, it can organize the information in a slide format. The information organizer can also visualize the information using infographics. For example, it can represent the information using graphs and charts. Furthermore, the information organizer can convert the summarized information into a report format. For example, it can create a document that includes detailed explanations. This allows the summarized information to be converted into different formats to make it easier to understand visually.

[0087] The information organization unit can have the summarized information reviewed by experts from different industries and fields, and reorganize the information based on the feedback. For example, the information summarized by the generative AI can be reviewed by experts from different industries and fields, and the information can be reorganized based on the feedback. For example, technical experts and design experts can be asked to review the information. The information organization unit can also revise the information based on the feedback. For example, it can update the information to reflect the opinions of the experts. Furthermore, the information organization unit can reclassify the information based on the feedback. For example, it can review the categories of the information and make it a more appropriate classification. This makes it possible to reorganize the information based on feedback from experts from different industries and fields.

[0088] The information organizer uses the emotion estimation function to collect users' emotional reactions to summarized information, thereby improving the accuracy of the summary. For example, the generation AI collects users' emotional reactions to summarized information and uses that data to improve the accuracy of the summary. For example, it prioritizes the adoption of summary sentences with a high number of positive reactions. The information organizer also uses the emotion estimation function to analyze users' emotional scores. For example, it emphasizes parts with high emotional scores. Furthermore, the information organizer modifies the summary sentence based on user feedback. For example, it improves the summary sentence by reflecting the user's opinions. This allows the collection of users' emotional reactions and the improvement of the summary accuracy.

[0089] The document generation unit can use the emotion estimation function to adopt expressions and layouts that evoke the most positive emotions in the user. For example, when the generation AI generates a handover document, it uses the emotion estimation function to adopt expressions that evoke the most positive emotions in the user. For example, it includes positive words and encouraging messages. The document generation unit also adopts a layout that elicits positive emotions. For example, it uses bright colors and visually appealing designs. Furthermore, the document generation unit optimizes the expressions and layout based on the emotion score. For example, it places parts with high emotion scores in prominent positions. This makes it possible to adopt expressions and layouts that evoke positive emotions in the user.

[0090] The document generation unit can refer to past success stories and best practices and include practical advice. For example, when the generation AI generates handover documents, it can refer to past success stories and include practical advice. For example, it can incorporate methods and strategies that have been successful in past projects. The document generation unit also provides advice based on industry best practices. For example, it can introduce industry-standard procedures and technologies. Furthermore, the document generation unit provides specific procedures based on success stories and best practices. For example, it can provide specific step-by-step guides. This makes it possible to refer to past success stories and best practices and include practical advice.

[0091] The document generation unit can reflect user feedback in real time and continuously optimize the document. For example, when the generation AI takes over and generates a document, it collects user feedback in real time and continuously optimizes the document. For example, it modifies the document by reflecting the user's opinions. The document generation unit also updates the content of the document based on real-time feedback. For example, it immediately corrects errors pointed out by the user. Furthermore, the document generation unit adjusts the layout of the document based on the feedback. For example, it changes the layout to suit the user's preferences. This allows the document to be continuously optimized by reflecting user feedback in real time.

[0092] The document generation unit can convert the generated handover document into a format that can be viewed on different devices. For example, it converts the handover document generated by the generation AI into a format that can be viewed on a smartphone or tablet. For example, it adjusts the layout to be mobile-friendly. The document generation unit also converts the document format to be compatible with different devices. For example, it converts it into PDF or HTML format. Furthermore, the document generation unit provides the optimal display method depending on the device. For example, it displays detailed information on a desktop and concise information on a mobile device. This makes it possible to convert the document into a format that can be viewed on different devices.

[0093] The document generation unit can link the generated handover documents with different business processes and systems, automatically updating the document contents. For example, the handover documents generated by the generation AI can be linked with different business processes and systems, automatically updating the document contents. For example, it can link with a project management system to automatically reflect progress. The document generation unit also synchronizes data with other systems via API. For example, it can link with a CRM system to automatically update customer information. Furthermore, the document generation unit updates documents in response to changes in business processes. For example, it automatically modifies documents when new procedures are added. This makes it possible to link with different business processes and systems, automatically updating the document contents.

[0094] The document generation unit can monitor the user's emotional response to the handover document generated using the emotion estimation function and propose the optimal document format. For example, it can monitor the user's emotional response to the handover document generated by the generation AI and propose the optimal document format. For example, it can adjust the layout based on the user's emotion score. The document generation unit also uses the emotion estimation function to analyze changes in the user's emotions. For example, it can highlight parts where the user had a positive reaction. Furthermore, the document generation unit can optimize the document format based on user feedback. For example, it can create a document in a format that matches the user's preferred format. This makes it possible to monitor the user's emotional response and propose the optimal document format.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The document reader loads existing documents. For example, past project reports, business manuals, email correspondence, etc. can be loaded as input information. Digital documents can also be loaded directly. For example, digital files such as PDFs and Word documents can be loaded. Furthermore, documents can be retrieved from databases via API. For example, the required documents can be retrieved from a company's internal database. Step 2: The information organizer organizes the loaded materials. For example, the generation AI analyzes the contents of the materials and extracts important information. It also determines the relevance and importance of the materials and categorizes the information. For example, it organizes project progress, important contacts, and work procedures. It then tags the information and stores it in a database. For example, it tags each piece of information to make it easier to search. Step 3: The document generation unit generates handover documents based on the organized information. For example, the generation AI creates work procedures for new employees and project summaries for employees taking on new positions. It also generates documents in the appropriate format and layout based on user instructions. For example, it creates documents according to the format specified by the user. Furthermore, the generated documents can be customized, for example, by emphasizing specific information or adding additional explanations.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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 material reading unit for reading existing materials; an information organizing unit that organizes the materials read by the material reading unit; a document generation unit that generates a handover document based on the information organized by the information organization unit. A system characterized by:

2. The material reading unit Analyze the feelings of the document creator and prioritize extracting emotionally important parts 2. The system of claim 1.

3. The material reading unit It also analyzes multimedia data such as images and videos, capturing visual and auditory information.

2. The system of claim 1.

4. The information organizing unit Use creative ways to arrange and express information in a way that elicits positive emotions 2. The system of claim 1.

5. The information organizing unit Visualize the interrelationships of information as graph data, allowing users to intuitively understand them 2. The system of claim 1.

6. The document generation unit Use expressions and layouts that evoke the most positive feelings in users.

2. The system of claim 1.

7. The document generation unit Continuously optimize the document based on real-time user feedback 2. The system of claim 1.

8. The document generation unit Monitor the user's emotional response to the generated handover document and suggest the optimal document format.

2. The system of claim 1.

Images