system

Abstract

The system according to the embodiment aims to efficiently analyze uploaded materials and convert them into a reusable form. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a storage unit, and a conversation unit. The reception unit uploads materials. The analysis unit analyzes the materials uploaded by the reception unit and converts them into a reusable format. The storage unit stores information analyzed by the analysis unit. The conversation unit conducts a conversation based on the information stored in the storage 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] Conventional technologies do not adequately analyze uploaded materials efficiently and convert them into a reusable form, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently analyze uploaded materials and convert them into a reusable form. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a storage unit, and a conversation unit. The reception unit uploads materials. The analysis unit analyzes the materials uploaded by the reception unit and converts them into a reusable format. The storage unit stores information analyzed by the analysis unit. The conversation unit conducts a conversation based on the information stored in the storage unit. [Effects of the Invention]

[0007] Embodiments of the system can efficiently analyze uploaded materials and convert them into a reusable form. [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 knowledge management system according to an embodiment of the present invention automatically organizes documents by simply uploading them and converts them into a reusable format using a generation AI. In this knowledge management system, users simply upload documents, and the generation AI automatically analyzes them, converts them into a reusable format, and stores them in a database. Various formats of documents, such as presentation materials and videos, can be uploaded. The generation AI extracts text information using natural language processing technology and converts speech to text using speech recognition technology. This allows it to extract text information from presentation slides and convert speech to text from videos. The analyzed information is stored in a database for centralized management of corporate knowledge, allowing for quick retrieval of necessary information. Furthermore, by using information retrieval-enhanced generation technology, the generation AI can engage in conversations based on stored knowledge. For example, when a user asks, "Tell me about the progress of this project," the generation AI retrieves relevant information from the database and generates an answer. It is also possible to retrieve specific documents. For example, in response to a request such as, "Show me last year's sales report," the generation AI searches the database for the relevant documents and displays them. This significantly reduces the burden of knowledge management for companies. This allows the knowledge management system to automate the organization and search of materials, making knowledge management in a company more efficient.

[0029] A knowledge management system according to an embodiment includes a reception unit, an analysis unit, a storage unit, and a conversation unit. The reception unit receives materials uploaded by a user. The materials include, but are not limited to, text, images, audio, and video. The analysis unit uses a generation AI to analyze the materials uploaded by the reception unit and convert them into a reusable format. The analysis unit extracts text information using, for example, natural language processing technology. The analysis unit can also convert audio into text using speech recognition technology. For example, the generation AI analyzes the materials and extracts text information using a text generation AI (e.g., LLM). The generation AI can also analyze audio and images and convert them into text using a multimodal generation AI. The storage unit stores the information analyzed by the analysis unit in a database. Examples of the database include, but are not limited to, cloud storage and local storage. The conversation unit uses information retrieval-enhanced generation technology to conduct a conversation based on the information stored in the storage unit. For example, the conversation unit searches a database for relevant information in response to a user's question and generates an answer. The conversation unit can also search for and display specific materials from a database. This allows the knowledge management system according to the embodiment to automatically upload, analyze, save, and converse on materials, thereby improving the efficiency of knowledge management.

[0030] The analysis unit can extract text information using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to divide a sentence into words and identify the part of speech of each word. The analysis unit can also use grammatical analysis to analyze the structure of the sentence and identify grammatical elements such as subjects and predicates. The analysis unit can also use semantic analysis to analyze the meaning of the sentence and extract important information. For example, the analysis unit uses morphological analysis to divide a sentence into words and identify the part of speech of each word. The analysis unit uses grammatical analysis to analyze the structure of the sentence and identify grammatical elements such as subjects and predicates. The semantic analysis can analyze the meaning of the sentence and extract important information. As a result, the use of natural language processing technology improves the accuracy of text information extraction.

[0031] The analysis unit can convert speech into text using speech recognition technology. Examples of speech recognition technology include, but are not limited to, deep learning-based speech recognition and acoustic models. The analysis unit can convert speech into text using, for example, deep learning-based speech recognition technology. The analysis unit can also analyze speech features using an acoustic model and convert the speech into text. For example, the analysis unit can convert speech into text using deep learning-based speech recognition technology. The analysis unit can analyze speech features using an acoustic model and convert the speech into text. This makes it possible to convert speech information into text using speech recognition technology.

[0032] The conversation unit can search for relevant information from a database in response to a user's question and generate an answer. Examples of relevant information include, but are not limited to, keyword search, context search, etc. The conversation unit can search for relevant information from a database using, for example, a keyword search. The conversation unit can also understand the context of the question and search for relevant information using a context search. For example, the conversation unit can search for relevant information from a database using a keyword search. The conversation unit can understand the context of the question and search for relevant information using a context search. This makes it possible to generate an appropriate answer to the user's question.

[0033] The conversation unit can search for specific materials from the database and display them. Examples of specific materials include, but are not limited to, title search, tag search, etc. The conversation unit can search for specific materials from the database using, for example, a title search. The conversation unit can also search for related materials using a tag search. For example, the conversation unit can search for specific materials from the database using a title search, and then search for related materials using a tag search. This allows specific materials to be quickly searched for and displayed.

[0034] The storage unit can store the analyzed information in a database. Examples of databases include, but are not limited to, a relational database and a NoSQL database. The storage unit can store the analyzed information using, for example, a relational database. The storage unit can also store the analyzed information using a NoSQL database. For example, the storage unit can store the analyzed information using a relational database. The storage unit can store the analyzed information using a NoSQL database. This allows for centralized management of the analyzed information.

[0035] The knowledge management system further includes a reception unit that analyzes the user's past upload history and selects the optimal upload method. The reception unit, for example, preferentially suggests upload methods (audio, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. The reception unit can also select the optimal upload method based on the format of materials previously uploaded by the user. For example, the reception unit analyzes the user's past upload history and selects the optimal upload method. This makes it possible to suggest the optimal upload method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0036] Furthermore, the knowledge management system includes a reception unit that filters materials based on the user's current project and areas of interest when uploading them. The reception unit, for example, prioritizes uploading materials related to the user's ongoing project. The reception unit can also filter and upload highly relevant materials based on the user's areas of interest. The reception unit can also automatically select and upload necessary materials according to the progress of the user's project. For example, the reception unit filters based on the user's current project and areas of interest. This makes it possible to upload materials according to the user's project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0037] Furthermore, the knowledge management system includes a reception unit that selects the optimal uploading means depending on the user's input method when uploading materials. For example, when a user uploads materials by voice, the reception unit converts the voice into text using voice recognition technology and uploads it. Furthermore, when a user uploads materials as images, the reception unit can also extract text information using image recognition technology and upload it. Furthermore, when a user uploads materials as text, the reception unit can also analyze the text information using natural language processing technology and upload it. For example, the reception unit selects the optimal uploading means depending on the user's input method. This makes it possible to provide the optimal uploading means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example. This may be done with or without the use of AI.

[0038] Furthermore, the knowledge management system includes a reception unit that, when uploading materials, prioritizes uploading highly relevant materials in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes uploading materials related to that area. Furthermore, when the user is on a business trip, the reception unit can also prioritize uploading materials related to the business trip destination. Furthermore, when the user is at home, the reception unit can also prioritize uploading materials related to the user's home. For example, the reception unit prioritizes uploading highly relevant materials in consideration of the user's geographical location information. This makes it possible to prioritize uploading highly relevant materials based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0039] Furthermore, the knowledge management system includes a reception unit that analyzes the user's social media activity and uploads related materials when uploading materials. The reception unit, for example, prioritizes uploading materials shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and upload related materials. The reception unit can also upload related materials by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and uploads related materials. This makes it possible to upload related materials based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI.

[0040] Furthermore, the knowledge management system includes a reception unit that customizes the upload method by reflecting the user's past feedback when uploading materials. The reception unit, for example, suggests an optimal upload method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific upload method based on the user's past feedback. Furthermore, the reception unit can also customize the upload procedure by reflecting the user's feedback. For example, the reception unit customizes the upload method by reflecting the user's past feedback. This makes it possible to customize the upload method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on documents with high importance. The analysis unit can also perform a brief analysis on documents with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the document. For example, the analysis unit evaluates the importance of the document and performs a detailed analysis on documents with high importance. The analysis unit performs a brief analysis on documents with low importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the document.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the material. For example, for presentation materials, the analysis unit applies an algorithm that extracts text information for each slide. Furthermore, for video materials, the analysis unit can also apply an algorithm that converts speech into text using speech recognition technology. Furthermore, for text materials, the analysis unit can apply an algorithm that analyzes text information using natural language processing technology. For example, for presentation materials, the analysis unit applies an algorithm that extracts text information for each slide. For video materials, the analysis unit applies an algorithm that converts speech into text using speech recognition technology. For text materials, the analysis unit applies an algorithm that analyzes text information using natural language processing technology. This enables optimal analysis depending on the category of the material.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also preferentially apply a specific analysis method based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit preferentially applies a specific analysis method based on the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis accuracy is improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the documents. For example, the analysis unit prioritizes analysis of the most recently submitted documents. The analysis unit can also postpone analysis of documents submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission date. For example, the analysis unit prioritizes analysis of the most recently submitted documents. The analysis unit postpones analysis of documents submitted earlier. The analysis priority is dynamically adjusted based on the submission date. This makes it possible to determine the priority of analysis based on the submission date of the documents.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the materials during analysis. For example, the analysis unit prioritizes analysis of highly relevant materials. The analysis unit can also postpone analysis of less relevant materials. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the materials. For example, the analysis unit prioritizes analysis of highly relevant materials. The analysis unit postpones analysis of less relevant materials. The order of analysis is dynamically adjusted based on the relevance of the materials. This makes it possible to adjust the order of analysis based on the relevance of the materials.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Provides analysis results that use analytical terminology. If the user does not have specialized knowledge, provides concise and easy-to-understand analysis results. Dynamically adjusts the use of analytical terminology according to the user's level of expertise. This allows the use of analytical terminology to be adjusted according to the user's level of expertise.

[0047] The storage unit can adjust the level of detail of storage based on the importance of the information when storing the information. For example, the storage unit stores information of high importance in detail. The storage unit can also store information of low importance in a concise manner. Furthermore, the storage unit can dynamically adjust the level of detail of storage according to the importance of the information. For example, the storage unit evaluates the importance of the information and stores information of high importance in detail. The storage unit stores information of low importance in a concise manner. In this way, the level of detail of storage can be adjusted according to the importance of the information.

[0048] The storage unit can apply different storage algorithms depending on the category of information when storing the information. For example, for presentation materials, the storage unit applies an algorithm that stores text information for each slide. For video materials, the storage unit can also apply an algorithm that uses voice recognition technology to convert voice into text and store the converted text. For text materials, the storage unit can also apply an algorithm that uses natural language processing technology to analyze and store the text information. For example, for presentation materials, the storage unit applies an algorithm that stores text information for each slide. For video materials, the storage unit applies an algorithm that uses voice recognition technology to convert voice into text and store the converted text. For text materials, the storage unit applies an algorithm that uses natural language processing technology to analyze and store the text information. This enables optimal storage depending on the category of information.

[0049] The storage unit can improve the accuracy of storage by referring to the user's past storage results when saving. The storage unit, for example, adjusts the storage algorithm based on the user's past storage results. The storage unit can also preferentially apply a specific storage method based on the user's past storage results. The storage unit can also improve the accuracy of storage by referring to the user's past storage results. For example, the storage unit adjusts the storage algorithm based on the user's past storage results. The storage unit preferentially applies a specific storage method based on the user's past storage results. The storage accuracy is improved by referring to the user's past storage results. In this way, the accuracy of storage is improved by referring to the user's past storage results.

[0050] The storage unit can determine the priority of storage based on the time of submission of information when storing the information. For example, the storage unit prioritizes saving the most recently submitted information. The storage unit can also postpone saving information that was submitted earlier. Furthermore, the storage unit can dynamically adjust the priority of storage based on the time of submission. For example, the storage unit prioritizes saving the most recently submitted information. The storage unit postpones saving information that was submitted earlier. The priority of storage is dynamically adjusted based on the time of submission. This makes it possible to determine the priority of storage based on the time of submission of information.

[0051] The storage unit can adjust the order of storage based on the relevance of the information when storing the information. For example, the storage unit prioritizes storing highly relevant information. The storage unit can also postpone storing less relevant information. Furthermore, the storage unit can dynamically adjust the order of storage based on the relevance of the information. For example, the storage unit prioritizes storing highly relevant information. The storage unit postpones storing less relevant information. The order of storage is dynamically adjusted based on the relevance of the information. In this way, the order of storage can be adjusted based on the relevance of the information.

[0052] The storage unit may adjust the use of technical terms for storage according to the user's level of expertise during storage. For example, if the user has technical expertise, the storage unit may provide a storage method that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the storage unit may provide a simple and easy-to-understand storage method. Furthermore, the storage unit may dynamically adjust the use of technical terms for storage according to the user's level of expertise. For example, if the user has technical expertise, the storage unit may provide a storage method that uses a lot of technical terms. If the user does not have technical expertise, the storage unit may provide a simple and easy-to-understand storage method. The use of technical terms for storage is dynamically adjusted according to the user's level of expertise. This allows the use of technical terms for storage to be adjusted according to the user's level of expertise.

[0053] During the conversation, the conversation unit can adjust the level of detail of the answer based on the importance of the question. For example, the conversation unit provides a detailed answer to a question of high importance. The conversation unit can also provide a concise answer to a question of low importance. Furthermore, the conversation unit can dynamically adjust the level of detail of the answer based on the importance of the question. For example, the conversation unit provides a detailed answer to a question of high importance. The conversation unit provides a concise answer to a question of low importance. The level of detail of the answer is dynamically adjusted based on the importance of the question. This makes it possible to adjust the level of detail of the answer based on the importance of the question.

[0054] During the conversation, the conversation unit can apply different answering algorithms depending on the category of the question. For example, the conversation unit applies a specialized answering algorithm to technical questions. The conversation unit can also apply a concise answering algorithm to general questions. Furthermore, the conversation unit can dynamically apply the optimal answering algorithm depending on the category of the question. For example, the conversation unit applies a specialized answering algorithm to technical questions. The conversation unit applies a concise answering algorithm to general questions. The optimal answering algorithm is dynamically applied depending on the category of the question. This makes it possible to provide the optimal answer depending on the category of the question.

[0055] The conversation unit can improve the accuracy of the answer by referring to the results of the user's past questions during the conversation. For example, the conversation unit adjusts the answer algorithm based on the results of the user's past questions. The conversation unit can also preferentially apply a specific answering method based on the results of the user's past questions. Furthermore, the conversation unit can improve the accuracy of the answer by referring to the results of the user's past questions. For example, the conversation unit can adjust the answer algorithm based on the results of the user's past questions. The answering algorithm is adjusted based on the results of the question asked. Specific answering methods are applied preferentially based on the user's past question results. The accuracy of answers is improved by referring to the user's past question results. In this way, the accuracy of answers is improved by referring to the user's past question results.

[0056] During a conversation, the conversation unit can determine the priority of answers based on the time when the question was submitted. For example, the conversation unit gives priority to answers to questions that were submitted most recently. The conversation unit can also postpone answers to questions that were submitted earlier. Furthermore, the conversation unit can dynamically adjust the priority of answers based on the time when they were submitted. For example, the conversation unit gives priority to answers to questions that were submitted most recently. Answers to questions that were submitted earlier are postponed. The priority of answers is dynamically adjusted based on the time when they were submitted. This makes it possible to determine the priority of answers based on the time when the question was submitted.

[0057] During a conversation, the conversation unit can adjust the order of answers based on the relevance of the questions. For example, the conversation unit gives priority to answers to highly relevant questions. The conversation unit can also postpone answers to less relevant questions. Furthermore, the conversation unit can dynamically adjust the order of answers based on the relevance of the questions. For example, the conversation unit gives priority to answers to highly relevant questions. The conversation unit postpones answers to less relevant questions. The order of answers is dynamically adjusted based on the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions.

[0058] During a conversation, the conversation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. For example, if the user has technical knowledge, the conversation unit provides an answer that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the conversation unit can also provide a concise and easy-to-understand answer. Furthermore, the conversation unit can dynamically adjust the use of technical terms in the answer depending on the user's level of expertise. For example, if the user has technical knowledge, the conversation unit provides an answer that uses a lot of technical terms. If the user does not have technical knowledge, the conversation unit provides a concise and easy-to-understand answer. The use of technical terms in the answer is dynamically adjusted depending on the user's level of expertise. This makes it possible to adjust the use of technical terms in the answer depending on the user's level of expertise.

[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 analysis unit can further improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis algorithm can be adjusted based on the user's past analysis results. Also, a specific analysis method can be preferentially applied based on the user's past analysis results. Furthermore, the analysis accuracy can be improved by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0061] The storage unit can adjust the level of detail of the storage based on the importance of the information when storing the analyzed information in the database. For example, information with high importance can be stored in detail. Information with low importance can also be stored in a simple manner. In this way, the level of detail of the storage can be adjusted according to the importance of the information.

[0062] The knowledge management system may also include a reception unit that analyzes the user's past upload history and selects the optimal upload method. For example, it may preferentially suggest upload methods (voice, text, etc.) that the user has frequently used in the past. It may also predict and suggest the upload method to be used during a specific time period based on the user's past upload history. This makes it possible to suggest the optimal upload method based on the user's past history.

[0063] The knowledge management system may further include a reception unit that filters materials based on the user's current project or area of ​​interest when uploading them. For example, materials related to the user's ongoing project may be uploaded with priority. It may also be possible to filter and upload highly relevant materials based on the user's area of ​​interest. This allows materials to be uploaded according to the user's project or area of ​​interest.

[0064] The knowledge management system can also include a reception unit that selects the optimal upload method depending on the user's input method when uploading materials. For example, if a user uploads materials by voice, the voice is converted to text using voice recognition technology and uploaded. Also, if a user uploads materials as images, the text information can be extracted using image recognition technology and uploaded. This makes it possible to provide the optimal upload method depending on the user's input method.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives materials uploaded by users, including but not limited to text, images, audio, and video. Step 2: The analysis unit uses generation AI to analyze the materials uploaded by the reception unit and convert them into a reusable format. The analysis unit can extract text information using natural language processing technology and convert speech to text using speech recognition technology. For example, it can use text generation AI (LLM) or multimodal generation AI to analyze the materials and extract text information. Step 3: The storage unit stores the information analyzed by the analysis unit in a database, which may include, but is not limited to, cloud storage or local storage. Step 4: The conversation unit uses information retrieval enhancement generation technology to hold a conversation based on the information stored in the storage unit. The conversation unit searches the database for relevant information in response to the user's question and generates an answer. It can also search the database for specific materials and display them.

[0067] (Example 2) A knowledge management system according to an embodiment of the present invention automatically organizes documents by simply uploading them and converts them into a reusable format using a generation AI. In this knowledge management system, users simply upload documents, and the generation AI automatically analyzes them, converts them into a reusable format, and stores them in a database. Various formats of documents, such as presentation materials and videos, can be uploaded. The generation AI extracts text information using natural language processing technology and converts speech to text using speech recognition technology. This allows it to extract text information from presentation slides and convert speech to text from videos. The analyzed information is stored in a database for centralized management of corporate knowledge, allowing for quick retrieval of necessary information. Furthermore, by using information retrieval-enhanced generation technology, the generation AI can engage in conversations based on stored knowledge. For example, when a user asks, "Tell me about the progress of this project," the generation AI retrieves relevant information from the database and generates an answer. It is also possible to retrieve specific documents. For example, in response to a request such as, "Show me last year's sales report," the generation AI searches the database for the relevant documents and displays them. This significantly reduces the burden of knowledge management for companies. This allows the knowledge management system to automate the organization and search of materials, making knowledge management in a company more efficient.

[0068] A knowledge management system according to an embodiment includes a reception unit, an analysis unit, a storage unit, and a conversation unit. The reception unit receives materials uploaded by a user. The materials include, but are not limited to, text, images, audio, and video. The analysis unit uses a generation AI to analyze the materials uploaded by the reception unit and convert them into a reusable format. The analysis unit extracts text information using, for example, natural language processing technology. The analysis unit can also convert audio into text using speech recognition technology. For example, the generation AI analyzes the materials and extracts text information using a text generation AI (e.g., LLM). The generation AI can also analyze audio and images and convert them into text using a multimodal generation AI. The storage unit stores the information analyzed by the analysis unit in a database. Examples of the database include, but are not limited to, cloud storage and local storage. The conversation unit uses information retrieval-enhanced generation technology to conduct a conversation based on the information stored in the storage unit. For example, the conversation unit searches a database for relevant information in response to a user's question and generates an answer. The conversation unit can also search for and display specific materials from a database. This allows the knowledge management system according to the embodiment to automatically upload, analyze, save, and converse on materials, thereby improving the efficiency of knowledge management.

[0069] The analysis unit can extract text information using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to divide a sentence into words and identify the part of speech of each word. The analysis unit can also use grammatical analysis to analyze the structure of the sentence and identify grammatical elements such as subjects and predicates. The analysis unit can also use semantic analysis to analyze the meaning of the sentence and extract important information. For example, the analysis unit uses morphological analysis to divide a sentence into words and identify the part of speech of each word. The analysis unit uses grammatical analysis to analyze the structure of the sentence and identify grammatical elements such as subjects and predicates. The semantic analysis can analyze the meaning of the sentence and extract important information. As a result, the use of natural language processing technology improves the accuracy of text information extraction.

[0070] The analysis unit can convert speech into text using speech recognition technology. Examples of speech recognition technology include, but are not limited to, deep learning-based speech recognition and acoustic models. The analysis unit can convert speech into text using, for example, deep learning-based speech recognition technology. The analysis unit can also analyze speech features using an acoustic model and convert the speech into text. For example, the analysis unit can convert speech into text using deep learning-based speech recognition technology. The analysis unit can analyze speech features using an acoustic model and convert the speech into text. This makes it possible to convert speech information into text using speech recognition technology.

[0071] The conversation unit can search for relevant information from a database in response to a user's question and generate an answer. Examples of relevant information include, but are not limited to, keyword search, context search, etc. The conversation unit can search for relevant information from a database using, for example, a keyword search. The conversation unit can also understand the context of the question and search for relevant information using a context search. For example, the conversation unit can search for relevant information from a database using a keyword search. The conversation unit can understand the context of the question and search for relevant information using a context search. This makes it possible to generate an appropriate answer to the user's question.

[0072] The conversation unit can search for specific materials from the database and display them. Examples of specific materials include, but are not limited to, title search, tag search, etc. The conversation unit can search for specific materials from the database using, for example, a title search. The conversation unit can also search for related materials using a tag search. For example, the conversation unit can search for specific materials from the database using a title search, and then search for related materials using a tag search. This allows specific materials to be quickly searched for and displayed.

[0073] The storage unit can store the analyzed information in a database. Examples of databases include, but are not limited to, a relational database and a NoSQL database. The storage unit can store the analyzed information using, for example, a relational database. The storage unit can also store the analyzed information using a NoSQL database. For example, the storage unit can store the analyzed information using a relational database. The storage unit can store the analyzed information using a NoSQL database. This allows for centralized management of the analyzed information.

[0074] The knowledge management system further includes a reception unit that estimates a user's emotions and adjusts the timing of uploading materials based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit simplifies the upload procedure and allows it to be completed quickly. Furthermore, if the user is relaxed, the reception unit can provide detailed upload options and suggest customizable upload methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable materials to be uploaded quickly. For example, the reception unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. It can also estimate emotions by analyzing the tone and speed of the user's voice using voice analysis technology. This allows materials to be uploaded at the optimal timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The knowledge management system further includes a reception unit that analyzes the user's past upload history and selects the optimal upload method. The reception unit, for example, preferentially suggests upload methods (audio, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. The reception unit can also select the optimal upload method based on the format of materials previously uploaded by the user. For example, the reception unit analyzes the user's past upload history and selects the optimal upload method. This makes it possible to suggest the optimal upload method based on the user's past history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0076] Furthermore, the knowledge management system includes a reception unit that filters materials based on the user's current project and areas of interest when uploading them. The reception unit, for example, prioritizes uploading materials related to the user's ongoing project. The reception unit can also filter and upload highly relevant materials based on the user's areas of interest. The reception unit can also automatically select and upload necessary materials according to the progress of the user's project. For example, the reception unit filters based on the user's current project and areas of interest. This makes it possible to upload materials according to the user's project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0077] Furthermore, the knowledge management system includes a reception unit that selects the optimal uploading means depending on the user's input method when uploading materials. For example, when a user uploads materials by voice, the reception unit converts the voice into text using voice recognition technology and uploads it. Furthermore, when a user uploads materials as images, the reception unit can also extract text information using image recognition technology and upload it. Furthermore, when a user uploads materials as text, the reception unit can also analyze the text information using natural language processing technology and upload it. For example, the reception unit selects the optimal uploading means depending on the user's input method. This makes it possible to provide the optimal uploading means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example. This may be done with or without the use of AI.

[0078] The knowledge management system further includes a reception unit that estimates a user's emotions and prioritizes the materials to be uploaded based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes uploading materials of high importance. Furthermore, if the user is relaxed, the reception unit can prioritize uploading detailed materials. Furthermore, if the user is in a hurry, the reception unit can prioritize uploading materials that can be uploaded quickly. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Emotions can also be estimated by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the prioritization of materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] Furthermore, the knowledge management system includes a reception unit that, when uploading materials, prioritizes uploading highly relevant materials in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes uploading materials related to that area. Furthermore, when the user is on a business trip, the reception unit can also prioritize uploading materials related to the business trip destination. Furthermore, when the user is at home, the reception unit can also prioritize uploading materials related to the user's home. For example, the reception unit prioritizes uploading highly relevant materials in consideration of the user's geographical location information. This makes it possible to prioritize uploading highly relevant materials based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0080] Furthermore, the knowledge management system includes a reception unit that analyzes the user's social media activity and uploads related materials when uploading materials. The reception unit, for example, prioritizes uploading materials shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and upload related materials. The reception unit can also upload related materials by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and uploads related materials. This makes it possible to upload related materials based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI.

[0081] Furthermore, the knowledge management system includes a reception unit that customizes the upload method by reflecting the user's past feedback when uploading materials. The reception unit, for example, suggests an optimal upload method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific upload method based on the user's past feedback. Furthermore, the reception unit can also customize the upload procedure by reflecting the user's feedback. For example, the reception unit customizes the upload method by reflecting the user's past feedback. This makes it possible to customize the upload method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The knowledge management system further includes an analysis unit that estimates a user's emotions and adjusts the presentation of the analysis based on the estimated user emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results with visually stimulating effects when the user is excited. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. It is also possible to estimate the emotion by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the presentation of the analysis to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on documents with high importance. The analysis unit can also perform a brief analysis on documents with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the document. For example, the analysis unit evaluates the importance of the document and performs a detailed analysis on documents with high importance. The analysis unit performs a brief analysis on documents with low importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the document.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the material. For example, for presentation materials, the analysis unit applies an algorithm that extracts text information for each slide. Furthermore, for video materials, the analysis unit can also apply an algorithm that converts speech into text using speech recognition technology. Furthermore, for text materials, the analysis unit can apply an algorithm that analyzes text information using natural language processing technology. For example, for presentation materials, the analysis unit applies an algorithm that extracts text information for each slide. For video materials, the analysis unit applies an algorithm that converts speech into text using speech recognition technology. For text materials, the analysis unit applies an algorithm that analyzes text information using natural language processing technology. This enables optimal analysis depending on the category of the material.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also preferentially apply a specific analysis method based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit preferentially applies a specific analysis method based on the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis accuracy is improved by referring to the user's past analysis results.

[0086] The knowledge management system further 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 provides a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. It is also possible to estimate the emotion by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the length of the analysis to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the documents. For example, the analysis unit prioritizes analysis of the most recently submitted documents. The analysis unit can also postpone analysis of documents submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission date. For example, the analysis unit prioritizes analysis of the most recently submitted documents. The analysis unit postpones analysis of documents submitted earlier. The analysis priority is dynamically adjusted based on the submission date. This makes it possible to determine the priority of analysis based on the submission date of the documents.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the materials during analysis. For example, the analysis unit prioritizes analysis of highly relevant materials. The analysis unit can also postpone analysis of less relevant materials. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the materials. For example, the analysis unit prioritizes analysis of highly relevant materials. The analysis unit postpones analysis of less relevant materials. The order of analysis is dynamically adjusted based on the relevance of the materials. This makes it possible to adjust the order of analysis based on the relevance of the materials.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Provides analysis results that use analytical terminology. If the user does not have specialized knowledge, provides concise and easy-to-understand analysis results. Dynamically adjusts the use of analytical terminology according to the user's level of expertise. This allows the use of analytical terminology to be adjusted according to the user's level of expertise.

[0090] The knowledge management system further includes a storage unit that estimates a user's emotions and prioritizes the information to be saved based on the estimated user emotions. For example, when a user is stressed, the storage unit prioritizes saving important information. Furthermore, when a user is relaxed, the storage unit can prioritize saving detailed information. Furthermore, when a user is in a hurry, the storage unit can prioritize saving information that can be saved quickly. For example, the storage unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Emotions can also be estimated by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the priority of information to be saved to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The storage unit can adjust the level of detail of storage based on the importance of the information when storing the information. For example, the storage unit stores information of high importance in detail. The storage unit can also store information of low importance in a concise manner. Furthermore, the storage unit can dynamically adjust the level of detail of storage according to the importance of the information. For example, the storage unit evaluates the importance of the information and stores information of high importance in detail. The storage unit stores information of low importance in a concise manner. In this way, the level of detail of storage can be adjusted according to the importance of the information.

[0092] The storage unit can apply different storage algorithms depending on the category of information when storing the information. For example, for presentation materials, the storage unit applies an algorithm that stores text information for each slide. For video materials, the storage unit can also apply an algorithm that uses voice recognition technology to convert voice into text and store the converted text. For text materials, the storage unit can also apply an algorithm that uses natural language processing technology to analyze and store the text information. For example, for presentation materials, the storage unit applies an algorithm that stores text information for each slide. For video materials, the storage unit applies an algorithm that uses voice recognition technology to convert voice into text and store the converted text. For text materials, the storage unit applies an algorithm that uses natural language processing technology to analyze and store the text information. This enables optimal storage depending on the category of information.

[0093] The storage unit can improve the accuracy of storage by referring to the user's past storage results when saving. The storage unit, for example, adjusts the storage algorithm based on the user's past storage results. The storage unit can also preferentially apply a specific storage method based on the user's past storage results. The storage unit can also improve the accuracy of storage by referring to the user's past storage results. For example, the storage unit adjusts the storage algorithm based on the user's past storage results. The storage unit preferentially applies a specific storage method based on the user's past storage results. The storage accuracy is improved by referring to the user's past storage results. In this way, the accuracy of storage is improved by referring to the user's past storage results.

[0094] The knowledge management system further includes a storage unit that estimates a user's emotions and adjusts the display method of the stored information based on the estimated user emotions. For example, when the user is nervous, the storage unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the storage unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the storage unit can also provide a display method that focuses on the main points. For example, the storage unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. It is also possible to estimate the emotion by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the display method of the stored information to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The storage unit can determine the priority of storage based on the time of submission of information when storing the information. For example, the storage unit prioritizes saving the most recently submitted information. The storage unit can also postpone saving information that was submitted earlier. Furthermore, the storage unit can dynamically adjust the priority of storage based on the time of submission. For example, the storage unit prioritizes saving the most recently submitted information. The storage unit postpones saving information that was submitted earlier. The priority of storage is dynamically adjusted based on the time of submission. This makes it possible to determine the priority of storage based on the time of submission of information.

[0096] The storage unit can adjust the order of storage based on the relevance of the information when storing the information. For example, the storage unit prioritizes storing highly relevant information. The storage unit can also postpone storing less relevant information. Furthermore, the storage unit can dynamically adjust the order of storage based on the relevance of the information. For example, the storage unit prioritizes storing highly relevant information. The storage unit postpones storing less relevant information. The order of storage is dynamically adjusted based on the relevance of the information. In this way, the order of storage can be adjusted based on the relevance of the information.

[0097] The storage unit may adjust the use of technical terms for storage according to the user's level of expertise during storage. For example, if the user has technical expertise, the storage unit may provide a storage method that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the storage unit may provide a simple and easy-to-understand storage method. Furthermore, the storage unit may dynamically adjust the use of technical terms for storage according to the user's level of expertise. For example, if the user has technical expertise, the storage unit may provide a storage method that uses a lot of technical terms. If the user does not have technical expertise, the storage unit may provide a simple and easy-to-understand storage method. The use of technical terms for storage is dynamically adjusted according to the user's level of expertise. This allows the use of technical terms for storage to be adjusted according to the user's level of expertise.

[0098] The knowledge management system further includes a conversation unit that estimates a user's emotions and adjusts the way the conversation is expressed based on the estimated user emotions. For example, if the user is nervous, the conversation unit may use a calm tone. Furthermore, if the user is relaxed, the conversation unit may use a friendly tone. Furthermore, if the user is in a hurry, the conversation unit may use a quick and concise conversation. For example, the conversation unit may capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. It may also be possible to estimate emotions by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the way the conversation is expressed to be adjusted based on 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.

[0099] During the conversation, the conversation unit can adjust the level of detail of the answer based on the importance of the question. For example, the conversation unit provides a detailed answer to a question of high importance. The conversation unit can also provide a concise answer to a question of low importance. Furthermore, the conversation unit can dynamically adjust the level of detail of the answer based on the importance of the question. For example, the conversation unit provides a detailed answer to a question of high importance. The conversation unit provides a concise answer to a question of low importance. The level of detail of the answer is dynamically adjusted based on the importance of the question. This makes it possible to adjust the level of detail of the answer based on the importance of the question.

[0100] During the conversation, the conversation unit can apply different answering algorithms depending on the category of the question. For example, the conversation unit applies a specialized answering algorithm to technical questions. The conversation unit can also apply a concise answering algorithm to general questions. Furthermore, the conversation unit can dynamically apply the optimal answering algorithm depending on the category of the question. For example, the conversation unit applies a specialized answering algorithm to technical questions. The conversation unit applies a concise answering algorithm to general questions. The optimal answering algorithm is dynamically applied depending on the category of the question. This makes it possible to provide the optimal answer depending on the category of the question.

[0101] The conversation unit can improve the accuracy of the answer by referring to the results of the user's past questions during the conversation. For example, the conversation unit adjusts the answer algorithm based on the results of the user's past questions. The conversation unit can also preferentially apply a specific answering method based on the results of the user's past questions. Furthermore, the conversation unit can improve the accuracy of the answer by referring to the results of the user's past questions. For example, the conversation unit can adjust the answer algorithm based on the results of the user's past questions. The answering algorithm is adjusted based on the results of the question asked. Specific answering methods are applied preferentially based on the user's past question results. The accuracy of answers is improved by referring to the user's past question results. In this way, the accuracy of answers is improved by referring to the user's past question results.

[0102] The knowledge management system further includes a conversation unit that estimates a user's emotions and adjusts the length of a conversation based on the estimated user emotions. For example, if the user is in a hurry, the conversation unit may provide a short, to-the-point conversation. Furthermore, if the user is relaxed, the conversation unit may provide a detailed conversation. Furthermore, if the user is excited, the conversation unit may provide a conversation with visually stimulating effects. For example, the conversation unit may capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. It may also be possible to estimate emotions by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the length of a conversation to be adjusted 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.

[0103] During a conversation, the conversation unit can determine the priority of answers based on the time when the question was submitted. For example, the conversation unit gives priority to answers to questions that were submitted most recently. The conversation unit can also postpone answers to questions that were submitted earlier. Furthermore, the conversation unit can dynamically adjust the priority of answers based on the time when they were submitted. For example, the conversation unit gives priority to answers to questions that were submitted most recently. Answers to questions that were submitted earlier are postponed. The priority of answers is dynamically adjusted based on the time when they were submitted. This makes it possible to determine the priority of answers based on the time when the question was submitted.

[0104] During a conversation, the conversation unit can adjust the order of answers based on the relevance of the questions. For example, the conversation unit gives priority to answers to highly relevant questions. The conversation unit can also postpone answers to less relevant questions. Furthermore, the conversation unit can dynamically adjust the order of answers based on the relevance of the questions. For example, the conversation unit gives priority to answers to highly relevant questions. The conversation unit postpones answers to less relevant questions. The order of answers is dynamically adjusted based on the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions.

[0105] During a conversation, the conversation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. For example, if the user has technical knowledge, the conversation unit provides an answer that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the conversation unit can also provide a concise and easy-to-understand answer. Furthermore, the conversation unit can dynamically adjust the use of technical terms in the answer depending on the user's level of expertise. For example, if the user has technical knowledge, the conversation unit provides an answer that uses a lot of technical terms. If the user does not have technical knowledge, the conversation unit provides a concise and easy-to-understand answer. The use of technical terms in the answer is dynamically adjusted depending on the user's level of expertise. This makes it possible to adjust the use of technical terms in the answer depending on the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and conversation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives materials uploaded by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the materials using a generation AI and converts them into a reusable form. The storage unit stores the analyzed information in the database 24 of the data processing device 12. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and conducts a conversation based on the stored information using information retrieval enhancement generation technology. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, storage unit, and conversation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives materials uploaded by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the materials using a generation AI and converts them into a reusable form. The storage unit stores the analyzed information in the database 24 of the data processing device 12. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and conducts a conversation based on the stored information using information retrieval-enhanced generation technology. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and conversation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives materials uploaded by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the materials using a generation AI and converts them into a reusable form. The storage unit stores the analyzed information in the database 24 of the data processing device 12. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and conducts a conversation based on the stored information using information retrieval-enhanced generation technology. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and conversation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives materials uploaded by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the materials using a generation AI and converts them into a reusable form. The storage unit stores the analyzed information in the database 24 of the data processing device 12. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and conducts a conversation based on the stored information using information retrieval-enhanced generation technology.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The knowledge management system may further include a reception unit that estimates the user's emotions and customizes the document upload method based on the estimated emotions. For example, if the user is feeling stressed, the upload procedure may be simplified and completed quickly. Alternatively, if the user is feeling relaxed, detailed upload options may be provided and a customizable upload method may be suggested. Furthermore, if the user is in a hurry, voice input may be prioritized to enable the document to be uploaded quickly. In this way, the optimal upload method may be provided according to the user's emotions.

[0108] The analysis unit can further improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis algorithm can be adjusted based on the user's past analysis results. Also, a specific analysis method can be preferentially applied based on the user's past analysis results. Furthermore, the analysis accuracy can be improved by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0109] The analysis unit can further estimate the user's emotions when converting speech to text using speech recognition technology and adjust the accuracy of the conversion based on the estimated emotions. For example, if the user is nervous, noise cancellation can be strengthened to improve the accuracy of speech recognition. Alternatively, if the user is relaxed, natural speech recognition can be prioritized. This makes it possible to adjust the accuracy of speech recognition according to the user's emotions.

[0110] The conversation unit further searches for relevant information from a database in response to a user's question, and when generating an answer, it can estimate the user's emotions and adjust the tone and level of detail of the answer based on the estimated emotions. For example, if the user is nervous, it can provide a concise answer in a calm tone. On the other hand, if the user is relaxed, it can provide an answer that includes detailed information. This makes it possible to provide the most appropriate answer depending on the user's emotions.

[0111] The conversation unit can also estimate the user's emotions when searching for and displaying specific materials from a database, and adjust the display method based on the estimated emotions. For example, if the user is in a hurry, it can display a concise display that focuses on the main points. On the other hand, if the user is relaxed, it can display a display that includes detailed information. This makes it possible to provide the optimal display method according to the user's emotions.

[0112] The storage unit can adjust the level of detail of the storage based on the importance of the information when storing the analyzed information in the database. For example, information with high importance can be stored in detail. Information with low importance can also be stored in a simple manner. In this way, the level of detail of the storage can be adjusted according to the importance of the information.

[0113] The knowledge management system may further include a storage unit that estimates the user's emotions and determines the priority of information to be saved based on the estimated emotions. For example, if the user is feeling stressed, it may prioritize saving information of high importance. Also, if the user is relaxed, it may prioritize saving detailed information. In this way, it is possible to determine the priority of information to be saved according to the user's emotions.

[0114] The knowledge management system may also include a reception unit that analyzes the user's past upload history and selects the optimal upload method. For example, it may preferentially suggest upload methods (voice, text, etc.) that the user has frequently used in the past. It may also predict and suggest the upload method to be used during a specific time period based on the user's past upload history. This makes it possible to suggest the optimal upload method based on the user's past history.

[0115] The knowledge management system may further include a reception unit that filters materials based on the user's current project or area of ​​interest when uploading them. For example, materials related to the user's ongoing project may be uploaded with priority. It may also be possible to filter and upload highly relevant materials based on the user's area of ​​interest. This allows materials to be uploaded according to the user's project or area of ​​interest.

[0116] The knowledge management system can also include a reception unit that selects the optimal upload method depending on the user's input method when uploading materials. For example, if a user uploads materials by voice, the voice is converted to text using voice recognition technology and uploaded. Also, if a user uploads materials as images, the text information can be extracted using image recognition technology and uploaded. This makes it possible to provide the optimal upload method depending on the user's input method.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives materials uploaded by users, including but not limited to text, images, audio, and video. Step 2: The analysis unit uses generation AI to analyze the materials uploaded by the reception unit and convert them into a reusable format. The analysis unit can extract text information using natural language processing technology and convert speech to text using speech recognition technology. For example, it can use text generation AI (LLM) or multimodal generation AI to analyze the materials and extract text information. Step 3: The storage unit stores the information analyzed by the analysis unit in a database, which may include, but is not limited to, cloud storage or local storage. Step 4: The conversation unit uses information retrieval enhancement generation technology to hold a conversation based on the information stored in the storage unit. The conversation unit searches the database for relevant information in response to the user's question and generates an answer. It can also search the database for specific materials and display them.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 reception desk where documents are uploaded, an analysis unit that analyzes the materials uploaded by the reception unit and converts them into a reusable format; a storage unit for storing the information analyzed by the analysis unit; a conversation unit that conducts a conversation based on the information stored in the storage unit; Equipped with A system characterized by:

2. The analysis unit Extract text information using natural language processing technology 2. The system of claim 1.

3. The analysis unit Converts speech to text using speech recognition technology 2. The system of claim 1.

4. The conversation unit is Searches for relevant information from a database in response to a user's question and generates an answer 2. The system of claim 1.

5. The conversation unit is Search and display specific materials from the database 2. The system of claim 1.

6. The storage unit Save the parsed information to a database 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the timing of uploading materials based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's upload history and select the optimal upload method 2. The system of claim 1.

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