system

Abstract

The system according to the embodiment aims to enable new employees and people changing jobs to quickly understand company terminology and quickly become accustomed to their work. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a display unit. The collection unit collects materials from an internal folder or cloud storage. The analysis unit analyzes the materials collected by the collection unit and extracts internal company terms. The generation unit generates definitions and usage examples of the terms extracted by the analysis unit. The display unit displays the internal company glossary generated by the generation unit.

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] With conventional technology, it can take time for new employees or people transferring from other companies to understand company terminology, which can lead to reduced work efficiency.

[0005] The system according to the embodiment aims to enable new employees and people changing jobs to quickly understand company terminology and quickly become accustomed to their work. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a display unit. The collection unit collects materials from an internal folder or cloud storage. The analysis unit analyzes the materials collected by the collection unit and extracts internal company terms. The generation unit generates definitions and usage examples of the terms extracted by the analysis unit. The display unit displays the internal company glossary generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment enables new employees and people changing jobs to quickly understand company terminology and quickly become accustomed to their work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically creates an internal glossary by linking internal folders and cloud storage with a generation AI. The generation AI analyzes documents stored in internal folders and cloud storage, extracts internal terminology, and automatically generates definitions and usage examples. For example, if the term "Project X" appears frequently in documents, the generation AI displays the definition and related information in a consolidated view. The generated internal glossary is then organized in a Wikipedia-like structure, making it easily accessible to internal members. This allows new graduates and new employees to easily understand internal terminology and quickly become accustomed to their work. Furthermore, the system also features a function that allows internal documents to be viewed in a consolidated view. For example, documents related to a specific project can be displayed in one place, promoting information sharing. This facilitates internal communication and improves work efficiency. This allows new graduates and new employees to quickly become accustomed to their work without stumbling over internal terminology, improving overall performance. Furthermore, by making internal documents available for consolidated viewing, information sharing is promoted and work efficiency is improved.

[0029] An information processing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a display unit. The collection unit collects materials from an internal folder or cloud storage. For example, the collection unit collects document files from an internal folder. The collection unit can also collect image files from cloud storage. The collection unit can also collect audio files. For example, the collection unit can collect PDF files from an internal folder. The analysis unit analyzes the materials collected by the collection unit to extract internal company terms. For example, the analysis unit analyzes document files using natural language processing technology to extract internal company terms. The analysis unit can also analyze image files using image recognition technology to extract internal company terms. The analysis unit can also analyze audio files using speech recognition technology to extract internal company terms. For example, the analysis unit can extract frequently occurring terms from document files using natural language processing technology. The generation unit generates definitions and usage examples of the terms extracted by the analysis unit. The generation unit generates term definitions using, for example, a generation AI. The generation unit can also generate usage examples of terms using a generation AI. Furthermore, the generation unit can also generate related information for terms using a generation AI. For example, the generation unit can automatically generate definitions of terms using a generation AI. The display unit displays the in-house glossary generated by the generation unit. The display unit displays the glossary in a hierarchical structure, for example, like Wikipedia. The display unit can also display materials related to a specific project all at once. Furthermore, the display unit can display the glossary in a searchable format. For example, the display unit displays the glossary in a hierarchical structure, like Wikipedia, to allow users to easily access it. As a result, the information processing system according to the embodiment can automatically generate and display an in-house glossary, allowing new graduates and employees who have transferred to the company to quickly become familiar with their work.

[0030] The collection unit can collect materials from in-house folders and cloud storage. The collection unit, for example, collects document files from in-house folders. The collection unit can also collect image files from cloud storage, for example. The collection unit can also collect audio files, for example. This allows materials to be collected efficiently from in-house folders and cloud storage. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input document files collected from in-house folders into the generation AI, and the generation AI can analyze the contents of the document files.

[0031] The analysis unit can analyze the collected materials and extract in-house terms. The analysis unit can, for example, use natural language processing technology to analyze document files and extract in-house terms. The analysis unit can also, for example, use image recognition technology to analyze image files and extract in-house terms. The analysis unit can also, for example, use voice recognition technology to analyze audio files and extract in-house terms. This allows for efficient extraction of in-house terms from collected materials. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input document files collected by the collection unit into a generation AI, which can analyze the contents of the document files and extract in-house terms.

[0032] The generation unit can generate definitions and usage examples of the extracted terms. The generation unit generates definitions of terms using, for example, a generation AI. The generation unit can also generate usage examples of terms using, for example, a generation AI. The generation unit can also generate related information for terms using, for example, a generation AI. This makes it possible to automatically generate definitions and usage examples of the extracted terms. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input terms extracted by the analysis unit into a generation AI, which then generates definitions and usage examples of the terms.

[0033] The display unit can display the generated internal glossary in a hierarchical structure. The display unit, for example, displays the glossary in a hierarchical structure like Wikipedia. The display unit can also, for example, display materials related to a specific project all at once. The display unit can also, for example, display the glossary in a searchable format. This allows the generated internal glossary to be displayed in a highly visible format. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the glossary generated by the generation unit into the generation AI, and the generation AI can display the glossary in a hierarchical structure.

[0034] The display unit can display materials related to a specific project all at once. The display unit, for example, displays materials related to a specific project all at once. The display unit can, for example, search for related materials based on the project name and display them all at once. The display unit can also search for related materials based on the project period and display them all at once. This allows materials related to a specific project to be displayed efficiently. Some or all of the above-mentioned processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can input a project name into the generation AI, which then searches for related materials and displays them all at once.

[0035] The information processing system includes a collection unit that analyzes a user's past document browsing history and selects an optimal collection method when collecting documents. The collection unit, for example, prioritizes collection of document formats (PDF, Word, etc.) that the user has frequently viewed in the past. The collection unit can, for example, prioritize collection of related documents based on the categories of documents (projects, meeting materials, etc.) that the user has previously viewed. The collection unit can, for example, prioritize collection of documents containing specific keywords from the user's past browsing history. This enables efficient document collection by selecting an optimal collection method based on the user's past document browsing history. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past document browsing history into the generation AI, which can select the optimal collection method.

[0036] The information processing system includes a collection unit that, when collecting materials, filters the materials based on the user's current project or area of ​​interest. The collection unit, for example, prioritizes collection of materials related to a project the user is currently working on. The collection unit can, for example, prioritize collection of materials related to areas in which the user is interested (technology, marketing, etc.). The collection unit can, for example, filter and collect materials related to topics in which the user has previously shown interest. This makes it possible to collect highly relevant materials by filtering the materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's current project or area of ​​interest into the generation AI, and the generation AI can filter and collect relevant materials.

[0037] The information processing system includes a collection unit that, when collecting materials, selects an optimal collection means according to a user's input method. For example, if the user uses voice input, the collection unit collects materials using voice recognition technology. For example, if the user uses text input, the collection unit can collect materials using text analysis technology. For example, if the user uses image input, the collection unit can collect materials using image recognition technology. This enables efficient material collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI, which can select the optimal collection means.

[0038] The information processing system includes a collection unit that, when collecting materials, prioritizes collecting highly relevant materials by taking into account the user's geographical location information. For example, when the user is in a specific office, the collection unit prioritizes collecting materials related to that office. For example, when the user is on a business trip, the collection unit can prioritize collecting materials related to the business trip destination. For example, when the user is working remotely, the collection unit can prioritize collecting materials that can be accessed from home. This allows highly relevant materials to be collected efficiently by collecting materials by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant materials.

[0039] The information processing system includes a collection unit that analyzes a user's social media activities and collects related materials when collecting materials. The collection unit, for example, collects materials related to links shared by the user on social media. The collection unit can, for example, analyze the content of the user's social media posts and collect related materials. The collection unit can, for example, collect related materials by referring to the activities of the user's friends on social media. This enables efficient collection of materials by analyzing the user's social media activities and collecting related materials. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media activities into the generation AI, which then collects related materials.

[0040] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting materials. The collection unit adjusts the type of materials to be collected based on, for example, feedback provided by the user in the past. The collection unit can, for example, preferentially collect materials of a specific type based on the user's feedback. The collection unit can, for example, improve the collection method by referring to the user's feedback and collect materials more efficiently. This enables efficient material collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can customize the collection method.

[0041] The information processing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the material during analysis. For example, the analysis unit allows the generation AI to perform a detailed analysis of highly important materials. For example, the analysis unit allows the generation AI to perform a simplified analysis of less important materials. For example, the analysis unit allows the generation AI to determine the priority of the analysis based on the importance of the material. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the material. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit may input the importance of the material to the generation AI, and the generation AI may adjust the level of detail of the analysis.

[0042] The information processing system includes an analysis unit that applies different analysis algorithms depending on the category of the document during analysis. For example, in the analysis unit, the generation AI applies a technical analysis algorithm to technical documents. For example, in the analysis unit, the generation AI can apply an analysis algorithm specialized for marketing to marketing documents. For example, in the analysis unit, the generation AI can apply a minutes analysis algorithm to meeting documents. This enables efficient analysis by applying an appropriate analysis algorithm depending on the category of the document. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the category of the document to the generation AI, which can then apply an appropriate analysis algorithm.

[0043] The information processing system includes an analysis unit that, during analysis, improves the accuracy of the analysis by referring to the user's past analysis results. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit can extract specific patterns from the user's past analysis results, and the generation AI can reflect these in the analysis. For example, the analysis unit can cause the generation AI to optimize the analysis algorithm by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0044] The information processing system includes an analysis unit that, during analysis, determines the priority of analysis based on the submission date of the documents. The analysis unit, for example, prioritizes analysis of documents with an upcoming submission deadline. The analysis unit, for example, can postpone analysis of documents with a distant submission deadline. The analysis unit, for example, allows the generation AI to adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the submission date of the documents to the generation AI, and the generation AI can determine the priority of analysis.

[0045] The information processing system includes an analysis unit that adjusts the order of analysis based on the relevance of materials during analysis. The analysis unit, for example, prioritizes analysis of highly relevant materials. The analysis unit can, for example, postpone analysis of less relevant materials. The analysis unit can, for example, have a generation AI determine the order of analysis based on the relevance of materials. This enables efficient analysis by adjusting the order of analysis based on the relevance of materials. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of materials to the generation AI, and the generation AI can determine the order of analysis.

[0046] The information processing system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit allows the generation AI to provide analysis results that use a lot of technical terms. For example, if the user does not have technical expertise, the analysis unit allows the generation AI to provide analysis results that avoid technical terms. For example, the analysis unit allows the generation AI to adjust the way the analysis results are expressed according to the user's level of expertise. This allows the generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the user's level of expertise into the generation AI, and the generation AI may adjust the use of technical terms in the analysis.

[0047] The information processing system includes a generation unit that adjusts the level of detail of the generated terms based on the importance of the terms during generation. For example, the generation unit allows the generation AI to provide detailed definitions and usage examples for terms with high importance. For example, the generation unit allows the generation AI to provide simplified definitions for terms with low importance. For example, the generation unit allows the generation AI to determine the priority of generation according to the importance of the terms. This enables efficient term generation by adjusting the level of detail of generation according to the importance of the terms. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input the importance of terms to the generation AI, and the generation AI may adjust the level of detail of generation.

[0048] The information processing system includes a generation unit that applies different generation algorithms depending on the category of a term during generation. For example, the generation unit may have a generation AI that applies a technical generation algorithm to technical terms. For example, the generation unit may have a generation AI that applies a generation algorithm specialized for marketing to marketing terms. For example, the generation unit may have a generation AI that applies a minutes generation algorithm to meeting terms. This enables efficient term generation by applying an appropriate generation algorithm depending on the category of the term. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input the category of a term to the generation AI, which may then apply an appropriate generation algorithm.

[0049] The information processing system includes a generation unit that, during generation, improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit allows the generation AI to improve the accuracy of generation based on definitions of terms previously generated by the user. For example, the generation unit can extract specific patterns from the user's past generation results, and the generation AI can reflect these in the generation. For example, the generation unit can allow the generation AI to optimize the generation algorithm by referring to the user's past generation results. This allows the accuracy of generation to be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past generation results into the generation AI, and the generation AI can improve the accuracy of generation.

[0050] The information processing system includes a generation unit that, at the time of generation, determines a generation priority based on the submission time of the terms. The generation unit, for example, prioritizes the generation of terms with an upcoming submission deadline. The generation unit, for example, can postpone the generation of terms with a more distant submission deadline. The generation unit, for example, can allow the generation AI to adjust the generation schedule based on the submission time. This enables efficient term generation by determining the generation priority based on the submission time of the terms. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the submission time of the terms into the generation AI, and the generation AI can determine the generation priority.

[0051] The information processing system includes a generation unit that adjusts the order of generation based on the relevance of terms during generation. The generation unit, for example, prioritizes the generation of highly relevant terms. The generation unit can, for example, postpone the generation of less relevant terms. The generation unit can, for example, have a generation AI determine the order of generation based on the relevance of terms. This enables efficient term generation by adjusting the order of generation based on the relevance of terms. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the relevance of terms to the generation AI, and the generation AI can determine the order of generation.

[0052] The information processing system includes a generation unit that adjusts the use of technical terms in the generated terms according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit can have the generation AI provide a definition of the term that uses a lot of technical terms. For example, if the user does not have technical expertise, the generation unit can have the generation AI provide a definition of the term that avoids technical terms. For example, the generation unit can have the generation AI adjust the way in which the definition of the term is expressed according to the user's level of expertise. This allows the generation AI to adjust the use of technical terms in the terms according to the user's level of expertise, thereby providing a definition of the term that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the terms.

[0053] The information processing system includes a display unit that, when displaying information, selects an optimal display method by referring to a user's past operation history. The display unit, for example, preferentially provides a display method that the user has used favorably in the past. The display unit, for example, can preferentially provide a specific display format based on the user's past operation history. The display unit, for example, can have a generation AI suggest an optimal display method based on the user's past operation history. This enables efficient information display by selecting an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the display unit can input the user's past operation history into the generation AI, which can select an optimal display method.

[0054] The information processing system includes a display unit that customizes display content according to a user's current task when displaying the information. The display unit, for example, prioritizes displaying information related to a project the user is currently working on. The display unit can, for example, display related materials according to the user's current task. The display unit can, for example, have a generation AI customize the display content based on the user's current task. This enables efficient information display by customizing the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's current task into the generation AI, which can then customize the display content.

[0055] The information processing system includes a display unit that, at the time of display, reflects user feedback to improve the display method. The display unit, for example, has a generation AI that improves the display method based on feedback provided by the user. The display unit, for example, can preferentially provide a specific display format based on the user feedback. The display unit, for example, can have the generation AI optimize the display method by referring to the user feedback. This enables efficient information display by improving the display method by reflecting user feedback. Some or all of the above-mentioned processing in the display unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the display unit can input user feedback to the generation AI, and the generation AI can improve the display method.

[0056] The information processing system includes a display unit that selects an optimal display method in consideration of a user's device information when displaying information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. For example, if the user is using a desktop, the display unit can display detailed information. This enables efficient information display by selecting an optimal display method in consideration of the user's device information. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI, which can select the optimal display method.

[0057] The information processing system includes a display unit that, when displayed, makes the displayed content multilingual according to a user's language setting. The display unit, for example, automatically translates the displayed content based on the language setting of the user's device. The display unit can provide a language switching function, for example, when a user uses multiple languages. For example, when a user selects a specific language, the display unit can provide the displayed content in that language. This enables efficient information display by making the displayed content multilingual according to the user's language setting. Some or all of the above-described processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's language setting into the generation AI, which can then make the displayed content multilingual.

[0058] The information processing system includes a display unit that, when displaying information, prioritizes highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific office, the display unit prioritizes displaying information related to the office. For example, when the user is on a business trip, the display unit can prioritize displaying information related to the business trip destination. For example, when the user is working remotely, the display unit can prioritize displaying information accessible from home. This enables efficient information display by prioritizing highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's geographical location information into the generation AI, which can then prioritize displaying highly relevant information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The information processing system includes a collection unit that analyzes a user's past document browsing history and selects an optimal collection method when collecting documents. The collection unit, for example, prioritizes collection of document formats (PDF, Word, etc.) that the user has frequently viewed in the past. The collection unit can, for example, prioritize collection of related documents based on the categories of documents (projects, meeting materials, etc.) that the user has previously viewed. The collection unit can, for example, prioritize collection of documents containing specific keywords from the user's past browsing history. This enables efficient document collection by selecting an optimal collection method based on the user's past document browsing history. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past document browsing history into the generation AI, which can select the optimal collection method.

[0061] The information processing system includes a collection unit that, when collecting materials, filters the materials based on the user's current project or area of ​​interest. The collection unit, for example, prioritizes collection of materials related to a project the user is currently working on. The collection unit can, for example, prioritize collection of materials related to areas in which the user is interested (technology, marketing, etc.). The collection unit can, for example, filter and collect materials related to topics in which the user has previously shown interest. This makes it possible to collect highly relevant materials by filtering the materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's current project or area of ​​interest into the generation AI, and the generation AI can filter and collect relevant materials.

[0062] The information processing system includes a collection unit that, when collecting materials, selects an optimal collection means according to a user's input method. For example, if the user uses voice input, the collection unit collects materials using voice recognition technology. For example, if the user uses text input, the collection unit can collect materials using text analysis technology. For example, if the user uses image input, the collection unit can collect materials using image recognition technology. This enables efficient material collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI, which can select the optimal collection means.

[0063] The information processing system includes a collection unit that, when collecting materials, prioritizes collecting highly relevant materials by taking into account the user's geographical location information. For example, when the user is in a specific office, the collection unit prioritizes collecting materials related to that office. For example, when the user is on a business trip, the collection unit can prioritize collecting materials related to the business trip destination. For example, when the user is working remotely, the collection unit can prioritize collecting materials that can be accessed from home. This allows highly relevant materials to be collected efficiently by collecting materials by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant materials.

[0064] The information processing system includes a collection unit that analyzes a user's social media activities and collects related materials when collecting materials. The collection unit, for example, collects materials related to links shared by the user on social media. The collection unit can, for example, analyze the content of the user's social media posts and collect related materials. The collection unit can, for example, collect related materials by referring to the activities of the user's friends on social media. This enables efficient collection of materials by analyzing the user's social media activities and collecting related materials. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media activities into the generation AI, which then collects related materials.

[0065] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting materials. The collection unit adjusts the type of materials to be collected based on, for example, feedback provided by the user in the past. The collection unit can, for example, preferentially collect materials of a specific type based on the user's feedback. The collection unit can, for example, improve the collection method by referring to the user's feedback and collect materials more efficiently. This enables efficient material collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can customize the collection method.

[0066] The information processing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the material during analysis. For example, the analysis unit allows the generation AI to perform a detailed analysis of highly important materials. For example, the analysis unit allows the generation AI to perform a simplified analysis of less important materials. For example, the analysis unit allows the generation AI to determine the priority of the analysis based on the importance of the material. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the material. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit may input the importance of the material to the generation AI, and the generation AI may adjust the level of detail of the analysis.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection department collects materials from an internal folder or cloud storage. For example, the collection department can collect document files and PDF files from an internal folder and image files and audio files from cloud storage. Step 2: The analysis department analyzes the materials collected by the collection department and extracts in-house terms. For example, the analysis department extracts in-house terms by analyzing document files using natural language processing technology, image files using image recognition technology, and audio files using voice recognition technology. Step 3: The generator generates definitions and usage examples of the terms extracted by the analyzer. For example, the generator uses a generation AI to automatically generate definitions, usage examples, and related information for terms. Step 4: The display unit displays the internal glossary generated by the generation unit. For example, the display unit can display the glossary in a hierarchical structure like Wikipedia, display materials related to a specific project in one place, and display the glossary in a searchable format.

[0069] (Example 2) A system according to an embodiment of the present invention automatically creates an internal glossary by linking internal folders and cloud storage with a generation AI. The generation AI analyzes documents stored in internal folders and cloud storage, extracts internal terminology, and automatically generates definitions and usage examples. For example, if the term "Project X" appears frequently in documents, the generation AI displays the definition and related information in a consolidated view. The generated internal glossary is then organized in a Wikipedia-like structure, making it easily accessible to internal members. This allows new graduates and new employees to easily understand internal terminology and quickly become accustomed to their work. Furthermore, the system also features a function that allows internal documents to be viewed in a consolidated view. For example, documents related to a specific project can be displayed in one place, promoting information sharing. This facilitates internal communication and improves work efficiency. This allows new graduates and new employees to quickly become accustomed to their work without stumbling over internal terminology, improving overall performance. Furthermore, by making internal documents available for consolidated viewing, information sharing is promoted and work efficiency is improved.

[0070] An information processing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a display unit. The collection unit collects materials from an internal folder or cloud storage. For example, the collection unit collects document files from an internal folder. The collection unit can also collect image files from cloud storage. The collection unit can also collect audio files. For example, the collection unit can collect PDF files from an internal folder. The analysis unit analyzes the materials collected by the collection unit to extract internal company terms. For example, the analysis unit analyzes document files using natural language processing technology to extract internal company terms. The analysis unit can also analyze image files using image recognition technology to extract internal company terms. The analysis unit can also analyze audio files using speech recognition technology to extract internal company terms. For example, the analysis unit can extract frequently occurring terms from document files using natural language processing technology. The generation unit generates definitions and usage examples of the terms extracted by the analysis unit. The generation unit generates term definitions using, for example, a generation AI. The generation unit can also generate usage examples of terms using a generation AI. Furthermore, the generation unit can also generate related information for terms using a generation AI. For example, the generation unit can automatically generate definitions of terms using a generation AI. The display unit displays the in-house glossary generated by the generation unit. The display unit displays the glossary in a hierarchical structure, for example, like Wikipedia. The display unit can also display materials related to a specific project all at once. Furthermore, the display unit can display the glossary in a searchable format. For example, the display unit displays the glossary in a hierarchical structure, like Wikipedia, to allow users to easily access it. As a result, the information processing system according to the embodiment can automatically generate and display an in-house glossary, allowing new graduates and employees who have transferred to the company to quickly become familiar with their work.

[0071] The collection unit can collect materials from in-house folders and cloud storage. The collection unit, for example, collects document files from in-house folders. The collection unit can also collect image files from cloud storage, for example. The collection unit can also collect audio files, for example. This allows materials to be collected efficiently from in-house folders and cloud storage. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input document files collected from in-house folders into the generation AI, and the generation AI can analyze the contents of the document files.

[0072] The analysis unit can analyze the collected materials and extract in-house terms. The analysis unit can, for example, use natural language processing technology to analyze document files and extract in-house terms. The analysis unit can also, for example, use image recognition technology to analyze image files and extract in-house terms. The analysis unit can also, for example, use voice recognition technology to analyze audio files and extract in-house terms. This allows for efficient extraction of in-house terms from collected materials. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input document files collected by the collection unit into a generation AI, which can analyze the contents of the document files and extract in-house terms.

[0073] The generation unit can generate definitions and usage examples of the extracted terms. The generation unit generates definitions of terms using, for example, a generation AI. The generation unit can also generate usage examples of terms using, for example, a generation AI. The generation unit can also generate related information for terms using, for example, a generation AI. This makes it possible to automatically generate definitions and usage examples of the extracted terms. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input terms extracted by the analysis unit into a generation AI, which then generates definitions and usage examples of the terms.

[0074] The display unit can display the generated internal glossary in a hierarchical structure. The display unit, for example, displays the glossary in a hierarchical structure like Wikipedia. The display unit can also, for example, display materials related to a specific project all at once. The display unit can also, for example, display the glossary in a searchable format. This allows the generated internal glossary to be displayed in a highly visible format. Some or all of the above-mentioned processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the glossary generated by the generation unit into the generation AI, and the generation AI can display the glossary in a hierarchical structure.

[0075] The display unit can display materials related to a specific project all at once. The display unit, for example, displays materials related to a specific project all at once. The display unit can, for example, search for related materials based on the project name and display them all at once. The display unit can also search for related materials based on the project period and display them all at once. This allows materials related to a specific project to be displayed efficiently. Some or all of the above-mentioned processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can input a project name into the generation AI, which then searches for related materials and displays them all at once.

[0076] The information processing system includes a collection unit that estimates a user's emotions and adjusts the timing of collecting materials based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit causes the generation AI to temporarily delay collecting materials and resume collection when the user is relaxed. For example, when the user is concentrating, the collection unit causes the generation AI to quickly collect materials, thereby improving the user's work efficiency. For example, when the user is tired, the collection unit causes the generation AI to refrain from collecting materials and resume collection after the user has rested. This adjusts the timing of collecting materials according to the user's emotions, thereby reducing the user's stress. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI. For example, the collection unit may input the user's emotion data into the generation AI, which may estimate the emotion and adjust the collection timing.

[0077] The information processing system includes a collection unit that analyzes a user's past document browsing history and selects an optimal collection method when collecting documents. The collection unit, for example, prioritizes collection of document formats (PDF, Word, etc.) that the user has frequently viewed in the past. The collection unit can, for example, prioritize collection of related documents based on the categories of documents (projects, meeting materials, etc.) that the user has previously viewed. The collection unit can, for example, prioritize collection of documents containing specific keywords from the user's past browsing history. This enables efficient document collection by selecting an optimal collection method based on the user's past document browsing history. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past document browsing history into the generation AI, which can select the optimal collection method.

[0078] The information processing system includes a collection unit that, when collecting materials, filters the materials based on the user's current project or area of ​​interest. The collection unit, for example, prioritizes collection of materials related to a project the user is currently working on. The collection unit can, for example, prioritize collection of materials related to areas in which the user is interested (technology, marketing, etc.). The collection unit can, for example, filter and collect materials related to topics in which the user has previously shown interest. This makes it possible to collect highly relevant materials by filtering the materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's current project or area of ​​interest into the generation AI, and the generation AI can filter and collect relevant materials.

[0079] The information processing system includes a collection unit that, when collecting materials, selects an optimal collection means according to a user's input method. For example, if the user uses voice input, the collection unit collects materials using voice recognition technology. For example, if the user uses text input, the collection unit can collect materials using text analysis technology. For example, if the user uses image input, the collection unit can collect materials using image recognition technology. This enables efficient material collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI, which can select the optimal collection means.

[0080] The information processing system includes a collection unit that estimates a user's emotions and prioritizes materials to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit allows the generation AI to postpone less important materials and prioritize collecting more important materials. For example, when the user is relaxed, the collection unit allows the generation AI to prioritize collecting detailed materials. For example, when the user is in a hurry, the collection unit allows the generation AI to prioritize collecting materials that can be collected quickly. This enables efficient material collection by prioritizing materials according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI, which then estimates the emotion and prioritizes the materials to be collected.

[0081] The information processing system includes a collection unit that, when collecting materials, prioritizes collecting highly relevant materials by taking into account the user's geographical location information. For example, when the user is in a specific office, the collection unit prioritizes collecting materials related to that office. For example, when the user is on a business trip, the collection unit can prioritize collecting materials related to the business trip destination. For example, when the user is working remotely, the collection unit can prioritize collecting materials that can be accessed from home. This allows highly relevant materials to be collected efficiently by collecting materials by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant materials.

[0082] The information processing system includes a collection unit that analyzes a user's social media activities and collects related materials when collecting materials. The collection unit, for example, collects materials related to links shared by the user on social media. The collection unit can, for example, analyze the content of the user's social media posts and collect related materials. The collection unit can, for example, collect related materials by referring to the activities of the user's friends on social media. This enables efficient collection of materials by analyzing the user's social media activities and collecting related materials. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media activities into the generation AI, which then collects related materials.

[0083] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting materials. The collection unit adjusts the type of materials to be collected based on, for example, feedback provided by the user in the past. The collection unit can, for example, preferentially collect materials of a specific type based on the user's feedback. The collection unit can, for example, improve the collection method by referring to the user's feedback and collect materials more efficiently. This enables efficient material collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can customize the collection method.

[0084] The information processing system includes an analysis unit that estimates a user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, when the user is nervous, the analysis unit allows the generation AI to provide a simple, highly visible analysis result. For example, when the user is relaxed, the analysis unit allows the generation AI to provide a detailed analysis result. For example, when the user is in a hurry, the analysis unit allows the generation AI to provide a key point analysis result. This allows the analysis result to be easily understood by adjusting the presentation method of the analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the presentation method of the analysis.

[0085] The information processing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the material during analysis. For example, the analysis unit allows the generation AI to perform a detailed analysis of highly important materials. For example, the analysis unit allows the generation AI to perform a simplified analysis of less important materials. For example, the analysis unit allows the generation AI to determine the priority of the analysis based on the importance of the material. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the material. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit may input the importance of the material to the generation AI, and the generation AI may adjust the level of detail of the analysis.

[0086] The information processing system includes an analysis unit that applies different analysis algorithms depending on the category of the document during analysis. For example, in the analysis unit, the generation AI applies a technical analysis algorithm to technical documents. For example, in the analysis unit, the generation AI can apply an analysis algorithm specialized for marketing to marketing documents. For example, in the analysis unit, the generation AI can apply a minutes analysis algorithm to meeting documents. This enables efficient analysis by applying an appropriate analysis algorithm depending on the category of the document. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the category of the document to the generation AI, which can then apply an appropriate analysis algorithm.

[0087] The information processing system includes an analysis unit that, during analysis, improves the accuracy of the analysis by referring to the user's past analysis results. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit can extract specific patterns from the user's past analysis results, and the generation AI can reflect these in the analysis. For example, the analysis unit can cause the generation AI to optimize the analysis algorithm by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0088] The information processing system includes an analysis unit that estimates a user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit allows the generation AI to provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit allows the generation AI to provide a detailed analysis result. For example, if the user is excited, the analysis unit allows the generation AI to provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the length of the analysis.

[0089] The information processing system includes an analysis unit that, during analysis, determines the priority of analysis based on the submission date of the documents. The analysis unit, for example, prioritizes analysis of documents with an upcoming submission deadline. The analysis unit, for example, can postpone analysis of documents with a distant submission deadline. The analysis unit, for example, allows the generation AI to adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the submission date of the documents to the generation AI, and the generation AI can determine the priority of analysis.

[0090] The information processing system includes an analysis unit that adjusts the order of analysis based on the relevance of materials during analysis. The analysis unit, for example, prioritizes analysis of highly relevant materials. The analysis unit can, for example, postpone analysis of less relevant materials. The analysis unit can, for example, have a generation AI determine the order of analysis based on the relevance of materials. This enables efficient analysis by adjusting the order of analysis based on the relevance of materials. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of materials to the generation AI, and the generation AI can determine the order of analysis.

[0091] The information processing system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit allows the generation AI to provide analysis results that use a lot of technical terms. For example, if the user does not have technical expertise, the analysis unit allows the generation AI to provide analysis results that avoid technical terms. For example, the analysis unit allows the generation AI to adjust the way the analysis results are expressed according to the user's level of expertise. This allows the generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the user's level of expertise into the generation AI, and the generation AI may adjust the use of technical terms in the analysis.

[0092] The information processing system includes a generation unit that estimates a user's emotions and adjusts the expression of generated terms based on the estimated user emotions. For example, when the user is nervous, the generation AI provides a simple, highly visible definition of the term. For example, when the user is relaxed, the generation AI can provide a detailed definition of the term. For example, when the user is in a hurry, the generation AI can provide a definition of the term that focuses on the main points. By adjusting the expression of the term according to the user's emotions, it is possible to provide a definition of the term that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI, which then estimates the emotion and adjusts the expression of the term.

[0093] The information processing system includes a generation unit that adjusts the level of detail of the generated terms based on the importance of the terms during generation. For example, the generation unit allows the generation AI to provide detailed definitions and usage examples for terms with high importance. For example, the generation unit allows the generation AI to provide simplified definitions for terms with low importance. For example, the generation unit allows the generation AI to determine the priority of generation according to the importance of the terms. This enables efficient term generation by adjusting the level of detail of generation according to the importance of the terms. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input the importance of terms to the generation AI, and the generation AI may adjust the level of detail of generation.

[0094] The information processing system includes a generation unit that applies different generation algorithms depending on the category of a term during generation. For example, the generation unit may have a generation AI that applies a technical generation algorithm to technical terms. For example, the generation unit may have a generation AI that applies a generation algorithm specialized for marketing to marketing terms. For example, the generation unit may have a generation AI that applies a minutes generation algorithm to meeting terms. This enables efficient term generation by applying an appropriate generation algorithm depending on the category of the term. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input the category of a term to the generation AI, which may then apply an appropriate generation algorithm.

[0095] The information processing system includes a generation unit that, during generation, improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit allows the generation AI to improve the accuracy of generation based on definitions of terms previously generated by the user. For example, the generation unit can extract specific patterns from the user's past generation results, and the generation AI can reflect these in the generation. For example, the generation unit can allow the generation AI to optimize the generation algorithm by referring to the user's past generation results. This allows the accuracy of generation to be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past generation results into the generation AI, and the generation AI can improve the accuracy of generation.

[0096] The information processing system includes a generation unit that estimates a user's emotion and adjusts the length of generated terms based on the estimated user emotion. For example, when the user is in a hurry, the generation AI provides a short, concise definition of the term. For example, when the user is relaxed, the generation AI can provide a detailed definition of the term. For example, when the user is excited, the generation AI can provide a visually stimulating definition of the term. By adjusting the length of the term according to the user's emotion, it is possible to provide a definition of the term that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit may input user emotion data into the generation AI, which then estimates the emotion and adjusts the length of the term.

[0097] The information processing system includes a generation unit that, at the time of generation, determines a generation priority based on the submission time of the terms. The generation unit, for example, prioritizes the generation of terms with an upcoming submission deadline. The generation unit, for example, can postpone the generation of terms with a more distant submission deadline. The generation unit, for example, can allow the generation AI to adjust the generation schedule based on the submission time. This enables efficient term generation by determining the generation priority based on the submission time of the terms. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the submission time of the terms into the generation AI, and the generation AI can determine the generation priority.

[0098] The information processing system includes a generation unit that adjusts the order of generation based on the relevance of terms during generation. The generation unit, for example, prioritizes the generation of highly relevant terms. The generation unit can, for example, postpone the generation of less relevant terms. The generation unit can, for example, have a generation AI determine the order of generation based on the relevance of terms. This enables efficient term generation by adjusting the order of generation based on the relevance of terms. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the relevance of terms to the generation AI, and the generation AI can determine the order of generation.

[0099] The information processing system includes a generation unit that adjusts the use of technical terms in the generated terms according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit can have the generation AI provide a definition of the term that uses a lot of technical terms. For example, if the user does not have technical expertise, the generation unit can have the generation AI provide a definition of the term that avoids technical terms. For example, the generation unit can have the generation AI adjust the way in which the definition of the term is expressed according to the user's level of expertise. This allows the generation AI to adjust the use of technical terms in the terms according to the user's level of expertise, thereby providing a definition of the term that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the terms.

[0100] The information processing system includes a display unit that estimates a user's emotion and adjusts a display method based on the estimated user emotion. For example, when the user is nervous, the generation AI of the display unit provides a simple, highly visible display method. For example, when the user is relaxed, the generation AI of the display unit can provide a display method that includes detailed information. For example, when the user is in a hurry, the generation AI of the display unit can provide a display method that focuses on the main points. This allows the display method to be adjusted according to the user's emotion, resulting in a highly visible display for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using, for example, the generation AI. For example, the display unit may input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the display method.

[0101] The information processing system includes a display unit that, when displaying information, selects an optimal display method by referring to a user's past operation history. The display unit, for example, preferentially provides a display method that the user has used favorably in the past. The display unit, for example, can preferentially provide a specific display format based on the user's past operation history. The display unit, for example, can have a generation AI suggest an optimal display method based on the user's past operation history. This enables efficient information display by selecting an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the display unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the display unit can input the user's past operation history into the generation AI, which can select an optimal display method.

[0102] The information processing system includes a display unit that customizes display content according to a user's current task when displaying the information. The display unit, for example, prioritizes displaying information related to a project the user is currently working on. The display unit can, for example, display related materials according to the user's current task. The display unit can, for example, have a generation AI customize the display content based on the user's current task. This enables efficient information display by customizing the display content according to the user's current task. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit can input the user's current task into the generation AI, which can then customize the display content.

[0103] The information processing system includes a display unit that, at the time of display, reflects user feedback to improve the display method. The display unit, for example, has a generation AI that improves the display method based on feedback provided by the user. The display unit, for example, can preferentially provide a specific display format based on the user feedback. The display unit, for example, can have the generation AI optimize the display method by referring to the user feedback. This enables efficient information display by improving the display method by reflecting user feedback. Some or all of the above-mentioned processing in the display unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the display unit can input user feedback to the generation AI, and the generation AI can improve the display method.

[0104] The information processing system includes a display unit that estimates a user's emotions and determines display priorities based on the estimated user emotions. For example, when the user is stressed, the generation AI postpones less important information and prioritizes displaying more important information. For example, when the user is relaxed, the generation AI can prioritize displaying detailed information. For example, when the user is in a hurry, the display unit can prioritize displaying information that the generation AI can display quickly. This enables efficient information display by determining display priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, the generation AI. For example, the display unit may input user emotion data to the generation AI, which then estimates the emotion and determines the display priorities.

[0105] The information processing system includes a display unit that selects an optimal display method in consideration of a user's device information when displaying information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. For example, if the user is using a desktop, the display unit can display detailed information. This enables efficient information display by selecting an optimal display method in consideration of the user's device information. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI, which can select the optimal display method.

[0106] The information processing system includes a display unit that, when displayed, makes the displayed content multilingual according to a user's language setting. The display unit, for example, automatically translates the displayed content based on the language setting of the user's device. The display unit can provide a language switching function, for example, when a user uses multiple languages. For example, when a user selects a specific language, the display unit can provide the displayed content in that language. This enables efficient information display by making the displayed content multilingual according to the user's language setting. Some or all of the above-described processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's language setting into the generation AI, which can then make the displayed content multilingual.

[0107] The information processing system includes a display unit that, when displaying information, prioritizes highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific office, the display unit prioritizes displaying information related to the office. For example, when the user is on a business trip, the display unit can prioritize displaying information related to the business trip destination. For example, when the user is working remotely, the display unit can prioritize displaying information accessible from home. This enables efficient information display by prioritizing highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's geographical location information into the generation AI, which can then prioritize displaying highly relevant information. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect materials from an internal company folder or cloud storage using the control unit 46A of the smart device 14. The analysis unit can analyze the materials collected by the specific processing unit 290 of the data processing device 12 and extract internal company terms. The generation unit can generate definitions and usage examples of the terms extracted by the specific processing unit 290 of the data processing device 12. The display unit can display the internal company glossary generated by the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect materials from an internal company folder or cloud storage by the control unit 46A of the smart glasses 214. The analysis unit can analyze the materials collected by the specific processing unit 290 of the data processing device 12 and extract internal company terms. The generation unit can generate definitions and usage examples of the terms extracted by the specific processing unit 290 of the data processing device 12. The display unit can display the internal company glossary generated by the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect materials from in-house folders or cloud storage using the control unit 46A of the headset type terminal 314. The analysis unit can analyze the materials collected by the specific processing unit 290 of the data processing device 12 and extract in-house terms. The generation unit can generate definitions and usage examples of the terms extracted by the specific processing unit 290 of the data processing device 12. The display unit can display the generated in-house glossary on the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect materials from in-house folders or cloud storage by the control unit 46A of the robot 414. The analysis unit can analyze the materials collected by the specific processing unit 290 of the data processing device 12 and extract in-house terms. The generation unit can generate definitions and usage examples of the terms extracted by the specific processing unit 290 of the data processing device 12. The display unit can display the in-house glossary generated by the speaker 240 of the robot 414.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The information processing system includes a collection unit that estimates a user's emotions and adjusts the timing of collecting materials based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit causes the generation AI to temporarily delay collecting materials and resume collection when the user is relaxed. For example, when the user is concentrating, the collection unit causes the generation AI to quickly collect materials, thereby improving the user's work efficiency. For example, when the user is tired, the collection unit causes the generation AI to refrain from collecting materials and resume collection after the user has rested. This adjusts the timing of collecting materials according to the user's emotions, thereby reducing the user's stress. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI. For example, the collection unit may input the user's emotion data into the generation AI, which may estimate the emotion and adjust the collection timing.

[0110] The information processing system includes a collection unit that analyzes a user's past document browsing history and selects an optimal collection method when collecting documents. The collection unit, for example, prioritizes collection of document formats (PDF, Word, etc.) that the user has frequently viewed in the past. The collection unit can, for example, prioritize collection of related documents based on the categories of documents (projects, meeting materials, etc.) that the user has previously viewed. The collection unit can, for example, prioritize collection of documents containing specific keywords from the user's past browsing history. This enables efficient document collection by selecting an optimal collection method based on the user's past document browsing history. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past document browsing history into the generation AI, which can select the optimal collection method.

[0111] The information processing system includes a collection unit that, when collecting materials, filters the materials based on the user's current project or area of ​​interest. The collection unit, for example, prioritizes collection of materials related to a project the user is currently working on. The collection unit can, for example, prioritize collection of materials related to areas in which the user is interested (technology, marketing, etc.). The collection unit can, for example, filter and collect materials related to topics in which the user has previously shown interest. This makes it possible to collect highly relevant materials by filtering the materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's current project or area of ​​interest into the generation AI, and the generation AI can filter and collect relevant materials.

[0112] The information processing system includes a collection unit that, when collecting materials, selects an optimal collection means according to a user's input method. For example, if the user uses voice input, the collection unit collects materials using voice recognition technology. For example, if the user uses text input, the collection unit can collect materials using text analysis technology. For example, if the user uses image input, the collection unit can collect materials using image recognition technology. This enables efficient material collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input method into the generation AI, which can select the optimal collection means.

[0113] The information processing system includes a collection unit that estimates a user's emotions and prioritizes materials to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit allows the generation AI to postpone less important materials and prioritize collecting more important materials. For example, when the user is relaxed, the collection unit allows the generation AI to prioritize collecting detailed materials. For example, when the user is in a hurry, the collection unit allows the generation AI to prioritize collecting materials that can be collected quickly. This enables efficient material collection by prioritizing materials according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI, which then estimates the emotion and prioritizes the materials to be collected.

[0114] The information processing system includes a collection unit that, when collecting materials, prioritizes collecting highly relevant materials by taking into account the user's geographical location information. For example, when the user is in a specific office, the collection unit prioritizes collecting materials related to that office. For example, when the user is on a business trip, the collection unit can prioritize collecting materials related to the business trip destination. For example, when the user is working remotely, the collection unit can prioritize collecting materials that can be accessed from home. This allows highly relevant materials to be collected efficiently by collecting materials by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which can then prioritize collecting highly relevant materials.

[0115] The information processing system includes a collection unit that analyzes a user's social media activities and collects related materials when collecting materials. The collection unit, for example, collects materials related to links shared by the user on social media. The collection unit can, for example, analyze the content of the user's social media posts and collect related materials. The collection unit can, for example, collect related materials by referring to the activities of the user's friends on social media. This enables efficient collection of materials by analyzing the user's social media activities and collecting related materials. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media activities into the generation AI, which then collects related materials.

[0116] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting materials. The collection unit adjusts the type of materials to be collected based on, for example, feedback provided by the user in the past. The collection unit can, for example, preferentially collect materials of a specific type based on the user's feedback. The collection unit can, for example, improve the collection method by referring to the user's feedback and collect materials more efficiently. This enables efficient material collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can customize the collection method.

[0117] The information processing system includes an analysis unit that estimates a user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, when the user is nervous, the analysis unit allows the generation AI to provide a simple, highly visible analysis result. For example, when the user is relaxed, the analysis unit allows the generation AI to provide a detailed analysis result. For example, when the user is in a hurry, the analysis unit allows the generation AI to provide a key point analysis result. This allows the analysis result to be easily understood by adjusting the presentation method of the analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the presentation method of the analysis.

[0118] The information processing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the material during analysis. For example, the analysis unit allows the generation AI to perform a detailed analysis of highly important materials. For example, the analysis unit allows the generation AI to perform a simplified analysis of less important materials. For example, the analysis unit allows the generation AI to determine the priority of the analysis based on the importance of the material. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the material. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit may input the importance of the material to the generation AI, and the generation AI may adjust the level of detail of the analysis.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection department collects materials from an internal folder or cloud storage. For example, the collection department can collect document files and PDF files from an internal folder and image files and audio files from cloud storage. Step 2: The analysis department analyzes the materials collected by the collection department and extracts in-house terms. For example, the analysis department extracts in-house terms by analyzing document files using natural language processing technology, image files using image recognition technology, and audio files using voice recognition technology. Step 3: The generator generates definitions and usage examples of the terms extracted by the analyzer. For example, the generator uses a generation AI to automatically generate definitions, usage examples, and related information for terms. Step 4: The display unit displays the internal glossary generated by the generation unit. For example, the display unit can display the glossary in a hierarchical structure like Wikipedia, display materials related to a specific project in one place, and display the glossary in a searchable format.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 materials from in-house folders or cloud storage; an analysis unit that analyzes the materials collected by the collection unit and extracts in-house terms; a generation unit that generates definitions and usage examples of the terms extracted by the analysis unit; a display unit that displays the in-house glossary generated by the generation unit. A system characterized by:

2. The collecting unit Collect documents from internal folders and cloud storage 2. The system of claim 1.

3. The analysis unit Analyze the collected data and extract in-house terms 2. The system of claim 1.

4. The generation unit Generate definitions and usage examples for extracted terms 2. The system of claim 1.

5. The display unit Display the generated company glossary in a hierarchical structure 2. The system of claim 1.

6. The display unit View all materials related to a specific project 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit When collecting materials, analyze the user's past browsing history and select the optimal collection method.

2. The system of claim 1.

9. The collecting unit As you collect materials, filter them based on your current projects and areas of interest 2. The system of claim 1.

Images