System

Abstract

An object of the system according to the embodiment is to efficiently collect, structure, and evolve intellectual assets in a company and integrate related information.SOLUTION: A system includes a collection part, a structurization part, an evolution part, and an integration part. The collection part collects intellectual assets in a company. The structurization part structurizes the intellectual property collected by the collection part. The evolution part evolves the intellectual property structured by the structuring part. The integration part integrates the related information based on the intellectual property evolved by the evolution part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately collect, structure, and evolve internal intellectual assets efficiently and integrate related information, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently collect, structure, and evolve intellectual assets within a company and integrate related information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a structuring unit, an evolution unit, and an integration unit. The collection unit collects intellectual assets within a company. The structuring unit structures the intellectual assets collected by the collection unit. The evolution unit evolves the intellectual assets structured by the structuring unit. The integration unit integrates related information based on the intellectual assets evolved by the evolution unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, structure, and evolve intellectual assets within a company and integrate related information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Knowledge Sphere System according to an embodiment of the present invention is a system that autonomously collects, structures, and evolves various intellectual assets within a company, thereby efficiently managing the company's intellectual assets and enabling users to quickly obtain the information they need.

[0029] The knowledge sphere system according to the embodiment includes a collection unit, a structuring unit, an evolution unit, and an integration unit. The collection unit collects intellectual assets within a company. For example, it automatically scans documents such as internal manuals, product catalogs, and meeting minutes and imports them as digital data. The collection unit also uses a generation AI to analyze the contents of the documents and extract important information. For example, the generation AI extracts information based on prompts containing the document content. The structuring unit structures the intellectual assets collected by the collection unit. For example, it extracts procedures and rules from internal manuals and organizes them hierarchically. The structuring unit also extracts product features and specifications from product catalogs and organizes them by category. The structuring unit also extracts meeting key points and decisions from meeting minutes and organizes them chronologically. The evolution unit evolves the intellectual assets structured by the structuring unit. For example, when new information is added, the generation AI compares it with existing knowledge to eliminate duplication and inconsistency. The evolution unit also automatically links related information to build a knowledge network. The Integration Department integrates related information based on the intellectual assets evolved by the Evolution Department. For example, it links product catalog information related to procedures in an internal manual, or information about other meetings related to decisions in meeting minutes. This allows the Knowledge Sphere System to efficiently collect, structure, and evolve internal intellectual assets and integrate related information. For example, users can obtain the information they need from a single platform.

[0030] The collection unit analyzes not only the content of a document, but also the intentions and background information of the document author, allowing for deeper knowledge to be extracted. For example, when the generation AI analyzes the content of a document, the collection unit takes into account the intentions and background information of the document author. For example, it infers the author's intentions from the wording and expressions in the document and extracts important information based on that intention. The collection unit also analyzes the document's metadata and the author's history information, taking background information into account. For example, it evaluates the importance of a document based on the date and time the document was created and the author's past achievements. In this way, by taking into account the intentions and background information of the document author, deeper knowledge can be extracted.

[0031] The collection unit also takes into account the document's update history and changes, allowing it to prioritize extracting the latest information. For example, the collection unit has the generation AI analyze the document's update history and prioritize extracting the latest changes. For example, it references the document's version control system and extracts information based on the contents of the latest version. The collection unit also analyzes the changes to the document and extracts important changes. For example, it analyzes the document's change history and identifies important changes. Furthermore, the collection unit takes into account the frequency of document updates and prioritizes analyzing documents that are updated frequently. This allows it to prioritize extracting the latest information by taking into account the document's update history and changes.

[0032] The collection unit can also collect intellectual assets from audio data or video data and integrate it with text data. The collection unit, for example, uses generative AI to extract important information from audio data and integrate it with text data. For example, it analyzes recordings of meetings and converts them into text as minutes. The collection unit also extracts important information from video data and integrates it with text data. For example, it analyzes videos of presentations and converts the main points into text. Furthermore, the collection unit builds a system that analyzes audio data and video data and integrates it with text data. This makes it possible to collect intellectual assets from audio data and video data and integrate them with text data.

[0033] The collection department can centrally collect intellectual assets from different departments and projects and promote knowledge sharing between departments. The collection department, for example, centrally collects intellectual assets from different departments and promotes knowledge sharing between departments. For example, it integrates the manuals and reports of each department into a single database. The collection department can also centrally collect intellectual assets from different projects and promote knowledge sharing between projects. For example, it can integrate the deliverables and reports of each project into a single database. The collection department can also centrally collect intellectual assets from different departments and projects and build a system that promotes knowledge sharing between departments and projects. This allows the collection of intellectual assets from different departments and projects to be centrally collected and promotes knowledge sharing between departments.

[0034] The structuring unit uses a generation AI to automatically generate a hierarchical structure of knowledge and visualize it so that the user can intuitively understand it. The structuring unit, for example, uses a generation AI to automatically generate a hierarchical structure of knowledge. For example, the procedures in an internal manual are organized hierarchically and displayed visually. The structuring unit also uses a generation AI to hierarchically organize the features and specifications of a product catalog and display them visually. Furthermore, the structuring unit uses a generation AI to build a system that hierarchically organizes the main points and decisions in meeting minutes and displays them visually. In this way, the hierarchical structure of knowledge is automatically generated and visualized, allowing the user to intuitively understand it.

[0035] The structuring unit also references external data when the generative AI structures knowledge, allowing it to build a more comprehensive knowledge system. For example, the structuring unit references related external data when the generative AI structures knowledge. For example, it structures knowledge based on industry standards and legal regulations. In addition, the structuring unit references external databases when the generative AI structures knowledge to complement the knowledge. For example, it structures knowledge based on related literature and databases. Furthermore, the structuring unit builds a system that references external data when the generative AI structures knowledge and builds a more comprehensive knowledge system. In this way, by referencing external data, a more comprehensive knowledge system can be built.

[0036] The structuring unit uses generative AI to adapt the structuring of knowledge to different languages ​​and cultural spheres, thereby realizing knowledge sharing from a global perspective. The structuring unit, for example, uses generative AI to adapt the structuring of knowledge to different languages. For example, it translates an internal manual into multiple languages ​​and structures the knowledge in each language. The structuring unit also uses generative AI to adapt the structuring of knowledge to different cultural spheres. For example, it structures knowledge taking cultural background into consideration. Furthermore, the structuring unit uses generative AI to adapt the structuring of knowledge to different languages ​​and cultural spheres, building a system that realizes knowledge sharing from a global perspective. In this way, by adapting to different languages ​​and cultural spheres, knowledge sharing from a global perspective can be realized.

[0037] The structuring unit customizes the structuring of knowledge according to the user's position and field of expertise, thereby meeting individual needs. The structuring unit customizes the structuring of knowledge according to the user's position, for example. For example, it builds separate knowledge systems for managers and field staff. The structuring unit also customizes the structuring of knowledge according to the user's field of expertise. For example, it builds separate knowledge systems for technical experts and sales experts. The structuring unit also customizes the structuring of knowledge according to the user's position and field of expertise, thereby building a system that meets individual needs. This allows the structuring of knowledge to be customized according to the user's position and field of expertise, thereby meeting individual needs.

[0038] The evolution department can use generative AI to track the evolutionary process of knowledge and visualize the changes in knowledge over time. The evolution department, for example, uses generative AI to track the evolutionary process of knowledge. For example, it can analyze the update history of an internal manual and display the changes over time. The evolution department also uses generative AI to visualize the changes in knowledge over time. For example, it can display the stages of knowledge evolution on a timeline. Furthermore, the evolution department uses generative AI to build a system that tracks the evolutionary process of knowledge and visualizes the changes in knowledge over time. This allows the evolutionary process of knowledge to be tracked and visualized over time, allowing for an intuitive understanding of the changes in knowledge.

[0039] The evolution unit can analyze the relevance with past knowledge when the generation AI adds new information and dynamically update the knowledge network. For example, the evolution unit analyzes the relevance with past knowledge when the generation AI adds new information. For example, when new product information is added, it analyzes the relevance with existing product information and updates the knowledge network. The evolution unit also dynamically updates the knowledge network when the generation AI adds new information. For example, it automatically links related information and builds a knowledge network. Furthermore, the evolution unit builds a system that analyzes the relevance with past knowledge when the generation AI adds new information and dynamically updates the knowledge network. This makes it possible to analyze the relevance with past knowledge when new information is added and dynamically update the knowledge network.

[0040] The Evolution Department can use generative AI to integrate knowledge from different fields and industries to promote crossover innovation. For example, the Evolution Department uses generative AI to integrate knowledge from different fields. For example, it can integrate knowledge from the medical and technical fields to create new innovations. The Evolution Department can also use generative AI to integrate knowledge from different industries. For example, it can integrate knowledge from the manufacturing and service industries to build new business models. Furthermore, the Evolution Department can use generative AI to integrate knowledge from different fields and industries to build a system that promotes crossover innovation. This allows it to integrate knowledge from different fields and industries and promote crossover innovation.

[0041] The evolution unit reflects the evolution of knowledge in real time based on user feedback, thereby realizing user-centered knowledge evolution. The evolution unit, for example, reflects the evolution of knowledge in real time based on user feedback. For example, it collects user opinions and requests and reflects them in the knowledge system. The evolution unit also builds a system that reflects user feedback in real time. For example, it implements a feedback form and a real-time update function. Furthermore, the evolution unit reflects the evolution of knowledge in real time based on user feedback, thereby realizing user-centered knowledge evolution. This makes it possible to reflect the evolution of knowledge in real time based on user feedback and realize user-centered knowledge evolution.

[0042] The integration unit can use the generation AI to evaluate the reliability and source of the information when integrating related information, and prioritize the integration of highly reliable information. The integration unit, for example, uses the generation AI to evaluate the reliability of the related information. For example, it evaluates information based on the reliability of the information's source or author, and prioritizes the integration of highly reliable information. The integration unit also uses the generation AI to develop an algorithm for evaluating the reliability of information. For example, it evaluates the reliability of the information using a reliability evaluation algorithm. Furthermore, the integration unit uses the generation AI to build a system that evaluates the reliability and source of the information when integrating related information, and prioritizes the integration of highly reliable information. This makes it possible to evaluate the reliability and source of the information, and prioritize the integration of highly reliable information.

[0043] The integration unit can analyze the interdependence of information when the generation AI integrates related information and maintain the consistency of the information. For example, the integration unit analyzes the interdependence of information when the generation AI integrates related information. For example, it analyzes the relevance of information and integrates consistent information. The integration unit also develops an algorithm that allows the generation AI to analyze the interdependence of information. For example, it analyzes the interdependence of information using a dependency graph or a consistency check algorithm. Furthermore, the integration unit analyzes the interdependence of information when the generation AI integrates related information and builds a system that maintains the consistency of information. This makes it possible to analyze the interdependence of information and integrate consistent information.

[0044] The integration unit can use the generation AI to integrate different data formats and provide multimodal information. The integration unit, for example, uses the generation AI to integrate different data formats. For example, it centrally manages text data, image data, and audio data to provide multimodal information. The integration unit also uses the generation AI to develop algorithms for integrating different data formats. For example, it develops a method for integrating text, image, and audio. Furthermore, the integration unit uses the generation AI to build a system that integrates different data formats and provides multimodal information. This makes it possible to integrate different data formats and provide multimodal information.

[0045] The integration unit can incorporate the integration of related information into a user's business process to improve business efficiency. The integration unit, for example, incorporates the integration of related information into a user's business process. For example, it automatically links related information to a business flow to improve business efficiency. The integration unit also builds a system that incorporates the integration of related information into a business process. For example, it uses business flow design and process automation tools. The integration unit also builds a system that incorporates the integration of related information into a user's business process to improve business efficiency. This makes it possible to incorporate the integration of related information into a business process and improve business efficiency.

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

[0047] The Knowledge Sphere system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes what information the user frequently references and identifies important information. The behavior analysis unit also analyzes what search keywords the user uses and provides relevant information. Furthermore, the behavior analysis unit can analyze the user's behavior patterns and provide information based on predicted needs. This allows for more appropriate information to be provided by taking the user's behavior history into consideration.

[0048] The knowledge sphere system can further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit, for example, analyzes what projects the user has participated in in the past and evaluates the skill level. The skill evaluation unit can also analyze what kind of training the user has received and evaluate the skill level. Furthermore, the skill evaluation unit can provide appropriate information according to the user's skill level. This allows more appropriate information to be provided by taking the user's skill level into consideration.

[0049] The knowledge sphere system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects, for example, how users evaluate the information provided. The feedback collection unit also collects what improvements users suggest. Furthermore, the feedback collection unit can improve the system based on the collected feedback. This allows the quality of the system to be improved by taking user feedback into consideration.

[0050] The Knowledge Sphere system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes what information the user frequently references and identifies important information. The behavior analysis unit also analyzes what search keywords the user uses and provides relevant information. Furthermore, the behavior analysis unit can analyze the user's behavior patterns and provide information based on predicted needs. This allows for more appropriate information to be provided by taking the user's behavior history into consideration.

[0051] The knowledge sphere system can further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit, for example, analyzes what projects the user has participated in in the past and evaluates the skill level. The skill evaluation unit can also analyze what kind of training the user has received and evaluate the skill level. Furthermore, the skill evaluation unit can provide appropriate information according to the user's skill level. This allows more appropriate information to be provided by taking the user's skill level into consideration.

[0052] The knowledge sphere system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects, for example, how users evaluate the information provided. The feedback collection unit also collects what improvements users suggest. Furthermore, the feedback collection unit can improve the system based on the collected feedback. This allows the quality of the system to be improved by taking user feedback into consideration.

[0053] The Knowledge Sphere system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes what information the user frequently references and identifies important information. The behavior analysis unit also analyzes what search keywords the user uses and provides relevant information. Furthermore, the behavior analysis unit can analyze the user's behavior patterns and provide information based on predicted needs. This allows for more appropriate information to be provided by taking the user's behavior history into consideration.

[0054] The knowledge sphere system can further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit, for example, analyzes what projects the user has participated in in the past and evaluates the skill level. The skill evaluation unit can also analyze what kind of training the user has received and evaluate the skill level. Furthermore, the skill evaluation unit can provide appropriate information according to the user's skill level. This allows more appropriate information to be provided by taking the user's skill level into consideration.

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

[0056] Step 1: The collection department collects intellectual assets within the company. For example, documents such as internal manuals, product catalogs, and meeting minutes are automatically scanned and captured as digital data. The collection department then uses generation AI to analyze the content of the documents and extract important information. For example, the generation AI extracts information based on prompts containing the content of the documents. Step 2: The Structuring Department structures the intellectual assets collected by the Collection Department. For example, they extract procedures and rules from internal manuals and organize them hierarchically. They also extract product features and specifications from product catalogs and organize them by category. They also extract key points and decisions from meeting minutes and organize them chronologically. Step 3: The evolution unit evolves the intellectual assets structured by the structuring unit. For example, when new information is added, the generative AI compares it with existing knowledge and eliminates duplication and contradictions. The evolution unit also automatically links related information and builds a knowledge network. Step 4: The Integration Department integrates related information based on the intellectual assets evolved by the Evolution Department. For example, it links product catalog information related to procedures in an internal manual, or information about other meetings related to decisions in meeting minutes. This enables the Knowledge Sphere System to efficiently collect, structure, and evolve internal intellectual assets and integrate related information.

[0057] (Example 2) The Knowledge Sphere System according to an embodiment of the present invention is a system that autonomously collects, structures, and evolves various intellectual assets within a company, thereby efficiently managing the company's intellectual assets and enabling users to quickly obtain the information they need.

[0058] The knowledge sphere system according to the embodiment includes a collection unit, a structuring unit, an evolution unit, and an integration unit. The collection unit collects intellectual assets within a company. For example, it automatically scans documents such as internal manuals, product catalogs, and meeting minutes and imports them as digital data. The collection unit also uses a generation AI to analyze the contents of the documents and extract important information. For example, the generation AI extracts information based on prompts containing the document content. The structuring unit structures the intellectual assets collected by the collection unit. For example, it extracts procedures and rules from internal manuals and organizes them hierarchically. The structuring unit also extracts product features and specifications from product catalogs and organizes them by category. The structuring unit also extracts meeting key points and decisions from meeting minutes and organizes them chronologically. The evolution unit evolves the intellectual assets structured by the structuring unit. For example, when new information is added, the generation AI compares it with existing knowledge to eliminate duplication and inconsistency. The evolution unit also automatically links related information to build a knowledge network. The Integration Department integrates related information based on the intellectual assets evolved by the Evolution Department. For example, it links product catalog information related to procedures in an internal manual, or information about other meetings related to decisions in meeting minutes. This allows the Knowledge Sphere System to efficiently collect, structure, and evolve internal intellectual assets and integrate related information. For example, users can obtain the information they need from a single platform.

[0059] The collection unit analyzes not only the content of a document, but also the intentions and background information of the document author, allowing for deeper knowledge to be extracted. For example, when the generation AI analyzes the content of a document, the collection unit takes into account the intentions and background information of the document author. For example, it infers the author's intentions from the wording and expressions in the document and extracts important information based on that intention. The collection unit also analyzes the document's metadata and the author's history information, taking background information into account. For example, it evaluates the importance of a document based on the date and time the document was created and the author's past achievements. In this way, by taking into account the intentions and background information of the document author, deeper knowledge can be extracted.

[0060] The collection unit also takes into account the document's update history and changes, allowing it to prioritize extracting the latest information. For example, the collection unit has the generation AI analyze the document's update history and prioritize extracting the latest changes. For example, it references the document's version control system and extracts information based on the contents of the latest version. The collection unit also analyzes the changes to the document and extracts important changes. For example, it analyzes the document's change history and identifies important changes. Furthermore, the collection unit takes into account the frequency of document updates and prioritizes analyzing documents that are updated frequently. This allows it to prioritize extracting the latest information by taking into account the document's update history and changes.

[0061] The collection unit can use the emotion estimation function to analyze the emotion of the document creator and preferentially extract emotionally important information. The collection unit, for example, uses the emotion estimation function to analyze the emotion of the document creator and extract emotionally important information. For example, the collection unit analyzes emotional expressions in the document and preferentially extracts parts where emotions are strongly expressed. The collection unit also analyzes the facial expressions and voice of the document creator to estimate the emotion. For example, the collection unit calculates an emotion score from the facial expressions and voice of the document creator and extracts emotionally important information. Furthermore, the collection unit analyzes the emotion data of the document creator and identifies emotionally important information. In this way, emotionally important information can be preferentially extracted by taking the emotion of the document creator into consideration.

[0062] The collection unit can also collect intellectual assets from audio data or video data and integrate it with text data. The collection unit, for example, uses generative AI to extract important information from audio data and integrate it with text data. For example, it analyzes recordings of meetings and converts them into text as minutes. The collection unit also extracts important information from video data and integrates it with text data. For example, it analyzes videos of presentations and converts the main points into text. Furthermore, the collection unit builds a system that analyzes audio data and video data and integrates it with text data. This makes it possible to collect intellectual assets from audio data and video data and integrate them with text data.

[0063] The collection department can centrally collect intellectual assets from different departments and projects and promote knowledge sharing between departments. The collection department, for example, centrally collects intellectual assets from different departments and promotes knowledge sharing between departments. For example, it integrates the manuals and reports of each department into a single database. The collection department can also centrally collect intellectual assets from different projects and promote knowledge sharing between projects. For example, it can integrate the deliverables and reports of each project into a single database. The collection department can also centrally collect intellectual assets from different departments and projects and build a system that promotes knowledge sharing between departments and projects. This allows the collection of intellectual assets from different departments and projects to be centrally collected and promotes knowledge sharing between departments.

[0064] The collection unit can use the emotion estimation function to analyze the emotions of users when collecting intellectual assets and propose a collection method that elicits positive emotions. The collection unit, for example, uses the emotion estimation function to analyze the emotions of users when collecting intellectual assets. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The collection unit also analyzes the user's emotion data and proposes a collection method that elicits positive emotions. For example, it improves the user interface design and feedback function to elicit positive emotions. Furthermore, the collection unit builds a system that takes the user's emotions into consideration and proposes a collection method that elicits positive emotions. This makes it possible to propose a collection method that takes the user's emotions into consideration and elicits positive emotions.

[0065] The structuring unit uses a generation AI to automatically generate a hierarchical structure of knowledge and visualize it so that the user can intuitively understand it. The structuring unit, for example, uses a generation AI to automatically generate a hierarchical structure of knowledge. For example, the procedures in an internal manual are organized hierarchically and displayed visually. The structuring unit also uses a generation AI to hierarchically organize the features and specifications of a product catalog and display them visually. Furthermore, the structuring unit uses a generation AI to build a system that hierarchically organizes the main points and decisions in meeting minutes and displays them visually. In this way, the hierarchical structure of knowledge is automatically generated and visualized, allowing the user to intuitively understand it.

[0066] The structuring unit also references external data when the generative AI structures knowledge, allowing it to build a more comprehensive knowledge system. For example, the structuring unit references related external data when the generative AI structures knowledge. For example, it structures knowledge based on industry standards and legal regulations. In addition, the structuring unit references external databases when the generative AI structures knowledge to complement the knowledge. For example, it structures knowledge based on related literature and databases. Furthermore, the structuring unit builds a system that references external data when the generative AI structures knowledge and builds a more comprehensive knowledge system. In this way, by referencing external data, a more comprehensive knowledge system can be built.

[0067] The structuring unit uses the emotion estimation function to analyze the emotions of the user when using knowledge and can structure the knowledge in a format that is emotionally easy to understand. The structuring unit, for example, uses the emotion estimation function to analyze the emotions of the user when using knowledge. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The structuring unit also analyzes the user's emotion data and structures the knowledge in a format that is emotionally easy to understand. For example, it uses clever color usage and layout to display the knowledge in a format that is emotionally easy to understand. Furthermore, the structuring unit builds a system that takes the user's emotions into consideration and structures knowledge in a format that is emotionally easy to understand. This makes it possible to take the user's emotions into consideration and structure knowledge in a format that is emotionally easy to understand.

[0068] The structuring unit uses generative AI to adapt the structuring of knowledge to different languages ​​and cultural spheres, thereby realizing knowledge sharing from a global perspective. The structuring unit, for example, uses generative AI to adapt the structuring of knowledge to different languages. For example, it translates an internal manual into multiple languages ​​and structures the knowledge in each language. The structuring unit also uses generative AI to adapt the structuring of knowledge to different cultural spheres. For example, it structures knowledge taking cultural background into consideration. Furthermore, the structuring unit uses generative AI to adapt the structuring of knowledge to different languages ​​and cultural spheres, building a system that realizes knowledge sharing from a global perspective. In this way, by adapting to different languages ​​and cultural spheres, knowledge sharing from a global perspective can be realized.

[0069] The structuring unit customizes the structuring of knowledge according to the user's position and field of expertise, thereby meeting individual needs. The structuring unit customizes the structuring of knowledge according to the user's position, for example. For example, it builds separate knowledge systems for managers and field staff. The structuring unit also customizes the structuring of knowledge according to the user's field of expertise. For example, it builds separate knowledge systems for technical experts and sales experts. The structuring unit also customizes the structuring of knowledge according to the user's position and field of expertise, thereby building a system that meets individual needs. This allows the structuring of knowledge to be customized according to the user's position and field of expertise, thereby meeting individual needs.

[0070] The structuring unit uses the emotion estimation function to collect users' emotional reactions to knowledge structuring, and can continuously improve the structuring method. The structuring unit, for example, uses the emotion estimation function to collect users' emotional reactions to knowledge structuring. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The structuring unit also analyzes the user's emotional data and continuously improves the structuring method. For example, it improves the knowledge structuring method based on user feedback. Furthermore, the structuring unit uses the emotion estimation function to collect users' emotional reactions to knowledge structuring, and builds a system that continuously improves the structuring method. This allows users' emotional reactions to be collected and the structuring method to be continuously improved.

[0071] The evolution department can use generative AI to track the evolutionary process of knowledge and visualize the changes in knowledge over time. The evolution department, for example, uses generative AI to track the evolutionary process of knowledge. For example, it can analyze the update history of an internal manual and display the changes over time. The evolution department also uses generative AI to visualize the changes in knowledge over time. For example, it can display the stages of knowledge evolution on a timeline. Furthermore, the evolution department uses generative AI to build a system that tracks the evolutionary process of knowledge and visualizes the changes in knowledge over time. This allows the evolutionary process of knowledge to be tracked and visualized over time, allowing for an intuitive understanding of the changes in knowledge.

[0072] The evolution unit can analyze the relevance with past knowledge when the generation AI adds new information and dynamically update the knowledge network. For example, the evolution unit analyzes the relevance with past knowledge when the generation AI adds new information. For example, when new product information is added, it analyzes the relevance with existing product information and updates the knowledge network. The evolution unit also dynamically updates the knowledge network when the generation AI adds new information. For example, it automatically links related information and builds a knowledge network. Furthermore, the evolution unit builds a system that analyzes the relevance with past knowledge when the generation AI adds new information and dynamically updates the knowledge network. This makes it possible to analyze the relevance with past knowledge when new information is added and dynamically update the knowledge network.

[0073] The evolution unit uses the emotion estimation function to analyze the emotions the user has regarding the evolution of knowledge, and can evolve the knowledge in an emotionally acceptable manner. The evolution unit, for example, uses the emotion estimation function to analyze the emotions the user has regarding the evolution of knowledge. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The evolution unit also analyzes the user's emotion data and evolves the knowledge in an emotionally acceptable manner. For example, it improves the user interface design and feedback function to display the knowledge in an emotionally acceptable manner. Furthermore, the evolution unit uses the emotion estimation function to analyze the emotions the user has regarding the evolution of knowledge, and builds a system that evolves the knowledge in an emotionally acceptable manner. This makes it possible to take the user's emotions into consideration and evolve the knowledge in an emotionally acceptable manner.

[0074] The Evolution Department can use generative AI to integrate knowledge from different fields and industries to promote crossover innovation. For example, the Evolution Department uses generative AI to integrate knowledge from different fields. For example, it can integrate knowledge from the medical and technical fields to create new innovations. The Evolution Department can also use generative AI to integrate knowledge from different industries. For example, it can integrate knowledge from the manufacturing and service industries to build new business models. Furthermore, the Evolution Department can use generative AI to integrate knowledge from different fields and industries to build a system that promotes crossover innovation. This allows it to integrate knowledge from different fields and industries and promote crossover innovation.

[0075] The evolution unit reflects the evolution of knowledge in real time based on user feedback, thereby realizing user-centered knowledge evolution. The evolution unit, for example, reflects the evolution of knowledge in real time based on user feedback. For example, it collects user opinions and requests and reflects them in the knowledge system. The evolution unit also builds a system that reflects user feedback in real time. For example, it implements a feedback form and a real-time update function. Furthermore, the evolution unit reflects the evolution of knowledge in real time based on user feedback, thereby realizing user-centered knowledge evolution. This makes it possible to reflect the evolution of knowledge in real time based on user feedback and realize user-centered knowledge evolution.

[0076] The evolution unit can use the emotion estimation function to monitor the user's emotional response to the evolution of knowledge and adjust the direction of evolution. The evolution unit, for example, uses the emotion estimation function to monitor the user's emotional response to the evolution of knowledge. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The evolution unit also analyzes the user's emotional data and adjusts the direction of evolution. For example, it adjusts the direction of knowledge evolution based on the user's emotional response. Furthermore, the evolution unit uses the emotion estimation function to build a system that monitors the user's emotional response to the evolution of knowledge and adjusts the direction of evolution. In this way, by monitoring the user's emotional response and adjusting the direction of evolution, it is possible to achieve knowledge evolution that is emotionally acceptable.

[0077] The integration unit can use the generation AI to evaluate the reliability and source of the information when integrating related information, and prioritize the integration of highly reliable information. The integration unit, for example, uses the generation AI to evaluate the reliability of the related information. For example, it evaluates information based on the reliability of the information's source or author, and prioritizes the integration of highly reliable information. The integration unit also uses the generation AI to develop an algorithm for evaluating the reliability of information. For example, it evaluates the reliability of the information using a reliability evaluation algorithm. Furthermore, the integration unit uses the generation AI to build a system that evaluates the reliability and source of the information when integrating related information, and prioritizes the integration of highly reliable information. This makes it possible to evaluate the reliability and source of the information, and prioritize the integration of highly reliable information.

[0078] The integration unit can analyze the interdependence of information when the generation AI integrates related information and maintain the consistency of the information. For example, the integration unit analyzes the interdependence of information when the generation AI integrates related information. For example, it analyzes the relevance of information and integrates consistent information. The integration unit also develops an algorithm that allows the generation AI to analyze the interdependence of information. For example, it analyzes the interdependence of information using a dependency graph or a consistency check algorithm. Furthermore, the integration unit analyzes the interdependence of information when the generation AI integrates related information and builds a system that maintains the consistency of information. This makes it possible to analyze the interdependence of information and integrate consistent information.

[0079] The integration unit uses the emotion estimation function to analyze the emotions of the user when using related information, and can integrate information in a manner that is emotionally convincing. The integration unit, for example, uses the emotion estimation function to analyze the emotions of the user when using related information. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The integration unit also analyzes the user's emotion data and integrates information in a manner that is emotionally convincing. For example, it improves the user interface design and feedback function to display information in a manner that is emotionally convincing. Furthermore, the integration unit uses the emotion estimation function to analyze the emotions of the user when using related information, and builds a system that integrates information in a manner that is emotionally convincing. This makes it possible to integrate information in a manner that is emotionally convincing, taking into account the user's emotions.

[0080] The integration unit can use the generation AI to integrate different data formats and provide multimodal information. The integration unit, for example, uses the generation AI to integrate different data formats. For example, it centrally manages text data, image data, and audio data to provide multimodal information. The integration unit also uses the generation AI to develop algorithms for integrating different data formats. For example, it develops a method for integrating text, image, and audio. Furthermore, the integration unit uses the generation AI to build a system that integrates different data formats and provides multimodal information. This makes it possible to integrate different data formats and provide multimodal information.

[0081] The integration unit can incorporate the integration of related information into a user's business process to improve business efficiency. The integration unit, for example, incorporates the integration of related information into a user's business process. For example, it automatically links related information to a business flow to improve business efficiency. The integration unit also builds a system that incorporates the integration of related information into a business process. For example, it uses business flow design and process automation tools. The integration unit also builds a system that incorporates the integration of related information into a user's business process to improve business efficiency. This makes it possible to incorporate the integration of related information into a business process and improve business efficiency.

[0082] The integration unit uses the emotion estimation function to collect the user's emotional reactions to the integration of related information, and can continuously improve the integration method. The integration unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the integration of related information. For example, the integration unit analyzes the user's facial expressions and voice and calculates an emotion score. The integration unit also analyzes the user's emotional data and continuously improves the integration method. For example, the integration unit improves the integration method based on user feedback. Furthermore, the integration unit uses the emotion estimation function to collect the user's emotional reactions to the integration of related information, and builds a system that continuously improves the integration method. This allows the user's emotional reactions to be collected and the integration method to be continuously improved.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The Knowledge Sphere system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes what information the user frequently references and identifies important information. The behavior analysis unit also analyzes what search keywords the user uses and provides relevant information. Furthermore, the behavior analysis unit can analyze the user's behavior patterns and provide information based on predicted needs. This allows for more appropriate information to be provided by taking the user's behavior history into consideration.

[0085] The knowledge sphere system can further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit, for example, analyzes what projects the user has participated in in the past and evaluates the skill level. The skill evaluation unit can also analyze what kind of training the user has received and evaluate the skill level. Furthermore, the skill evaluation unit can provide appropriate information according to the user's skill level. This allows more appropriate information to be provided by taking the user's skill level into consideration.

[0086] The knowledge sphere system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects, for example, how users evaluate the information provided. The feedback collection unit also collects what improvements users suggest. Furthermore, the feedback collection unit can improve the system based on the collected feedback. This allows the quality of the system to be improved by taking user feedback into consideration.

[0087] The knowledge sphere system can further include a display adjustment unit that estimates the user's emotions and adjusts the information display method based on the estimated user emotions. For example, the display adjustment unit displays information in a concise manner when the user is feeling stressed. Alternatively, the display adjustment unit provides detailed information when the user is excited. Furthermore, the display adjustment unit can change the display format of information according to the user's emotions. This allows the information display method to be optimized by taking the user's emotions into consideration.

[0088] The Knowledge Sphere system can further include an importance evaluation unit that estimates the user's emotions and evaluates the importance of information based on the estimated user emotions. The importance evaluation unit, for example, prioritizes displaying information to which the user has a strong emotional reaction. The importance evaluation unit also postpones information to which the user is emotionally indifferent. Furthermore, the importance evaluation unit can dynamically change the importance of information according to the user's emotions. This allows the importance of information to be appropriately evaluated by taking the user's emotions into consideration.

[0089] The Knowledge Sphere System can further include a timing adjustment unit that estimates the user's emotions and adjusts the timing of providing information based on the estimated user emotions. For example, the timing adjustment unit provides important information when the user is relaxed. The timing adjustment unit also refrains from providing information when the user is busy. Furthermore, the timing adjustment unit can dynamically change the timing of providing information according to the user's emotions. This allows the timing of providing information to be optimized by taking the user's emotions into consideration.

[0090] The knowledge sphere system can further include a filtering unit that estimates the user's emotions and filters information based on the estimated user emotions. For example, the filtering unit reduces the amount of information when the user is emotionally tired. Alternatively, the filtering unit provides more detailed information when the user is emotionally excited. Furthermore, the filtering unit can dynamically change the information filtering criteria according to the user's emotions. This allows information filtering to be optimized by taking the user's emotions into consideration.

[0091] The knowledge sphere system can further include a priority determination unit that estimates the user's emotions and determines the priority of information based on the estimated user emotions. The priority determination unit, for example, preferentially displays information that the user feels is emotionally important. The priority determination unit also postpones information that the user is emotionally indifferent to. Furthermore, the priority determination unit can dynamically change the priority of information according to the user's emotions. This allows the priority of information to be appropriately determined by taking the user's emotions into consideration.

[0092] The knowledge sphere system can further include a display format adjustment unit that estimates the user's emotions and adjusts the display format of information based on the estimated user emotions. For example, the display format adjustment unit displays information in a concise manner when the user is emotionally tired. Alternatively, the display format adjustment unit provides detailed information when the user is emotionally excited. Furthermore, the display format adjustment unit can dynamically change the display format of information according to the user's emotions. This allows the display format of information to be optimized by taking the user's emotions into consideration.

[0093] The Knowledge Sphere system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes what information the user frequently references and identifies important information. The behavior analysis unit also analyzes what search keywords the user uses and provides relevant information. Furthermore, the behavior analysis unit can analyze the user's behavior patterns and provide information based on predicted needs. This allows for more appropriate information to be provided by taking the user's behavior history into consideration.

[0094] The knowledge sphere system can further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit, for example, analyzes what projects the user has participated in in the past and evaluates the skill level. The skill evaluation unit can also analyze what kind of training the user has received and evaluate the skill level. Furthermore, the skill evaluation unit can provide appropriate information according to the user's skill level. This allows more appropriate information to be provided by taking the user's skill level into consideration.

[0095] The knowledge sphere system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects, for example, how users evaluate the information provided. The feedback collection unit also collects what improvements users suggest. Furthermore, the feedback collection unit can improve the system based on the collected feedback. This allows the quality of the system to be improved by taking user feedback into consideration.

[0096] The Knowledge Sphere system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes what information the user frequently references and identifies important information. The behavior analysis unit also analyzes what search keywords the user uses and provides relevant information. Furthermore, the behavior analysis unit can analyze the user's behavior patterns and provide information based on predicted needs. This allows for more appropriate information to be provided by taking the user's behavior history into consideration.

[0097] The knowledge sphere system can further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit, for example, analyzes what projects the user has participated in in the past and evaluates the skill level. The skill evaluation unit can also analyze what kind of training the user has received and evaluate the skill level. Furthermore, the skill evaluation unit can provide appropriate information according to the user's skill level. This allows more appropriate information to be provided by taking the user's skill level into consideration.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The collection department collects intellectual assets within the company. For example, documents such as internal manuals, product catalogs, and meeting minutes are automatically scanned and captured as digital data. The collection department then uses generation AI to analyze the content of the documents and extract important information. For example, the generation AI extracts information based on prompts containing the content of the documents. Step 2: The Structuring Department structures the intellectual assets collected by the Collection Department. For example, they extract procedures and rules from internal manuals and organize them hierarchically. They also extract product features and specifications from product catalogs and organize them by category. They also extract key points and decisions from meeting minutes and organize them chronologically. Step 3: The evolution unit evolves the intellectual assets structured by the structuring unit. For example, when new information is added, the generative AI compares it with existing knowledge and eliminates duplication and contradictions. The evolution unit also automatically links related information and builds a knowledge network. Step 4: The Integration Department integrates related information based on the intellectual assets evolved by the Evolution Department. For example, it links product catalog information related to procedures in an internal manual, or information about other meetings related to decisions in meeting minutes. This enables the Knowledge Sphere System to efficiently collect, structure, and evolve internal intellectual assets and integrate related information.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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 collection department that collects intellectual assets within the company; a structuring unit that structures the intellectual assets collected by the collecting unit; an evolution unit that evolves the intellectual assets structured by the structuring unit; an integration unit that integrates related information based on the intellectual assets evolved by the evolution unit; A system characterized by:

2. The collecting unit Collect intellectual property from audio or video data and integrate it with text data 2. The system of claim 1.

3. The structured portion is Using generative AI, we automatically generate a hierarchical structure of knowledge and visualize it so that users can understand it intuitively.

2. The system of claim 1.

4. The evolution unit Using generative AI to track the evolution of knowledge and visualize the changes in that knowledge over time.

2. The system of claim 1.

5. The integration unit When integrating the related information, a generative AI is used to evaluate the reliability and source of the information, and prioritize the integration of the highly reliable information.

2. The system of claim 1.

6. The collecting unit Using emotion estimation functionality, the emotions of the document author are analyzed and emotionally important information is preferentially extracted.

2. The system of claim 1.

7. The structured portion is Using emotion estimation function, analyze the emotions when users use knowledge and structure the knowledge in an emotionally understandable format.

2. The system of claim 1.

8. The evolution unit Using emotion estimation function, the emotion that the user feels about the evolution of knowledge is analyzed, and the knowledge is evolved in an emotionally acceptable way.

2. The system of claim 1.

Images