System

Abstract

An object of a system according to an embodiment is to construct knowledge for quickly and accurately answering an in-house question.SOLUTION: A system includes a collection part, an analysis part, a generation part, an answer part, and a provision part. The collection part collects public information or result data of each department. The analysis unit analyzes the data collected by the collection unit. The generation unit constructs knowledge based on the data analyzed by the analysis unit. The answering unit answers the in-house question based on the knowledge constructed by the generating unit. The providing unit provides a processing result of each unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technologies, it is difficult to build knowledge to quickly and accurately answer internal questions, and there is room for improvement.

[0005] The system according to the embodiment aims to build knowledge to quickly and accurately answer questions within a company. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, an answering unit, and a providing unit. The collection unit collects public information or performance data from each department. The analysis unit analyzes the data collected by the collection unit. The generation unit constructs knowledge based on the data analyzed by the analysis unit. The answering unit answers internal questions based on the knowledge constructed by the generation unit. The providing unit provides the processing results of each unit. [Effects of the Invention]

[0007] The system according to the embodiment can build knowledge to quickly and accurately answer questions within a company. [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) An information sharing system according to an embodiment of the present invention collects public information and performance data from each department, and uses a generation AI to analyze the data to create a generation AI that is familiar with the company's internal circumstances. The information sharing system collects public information and performance data from each department, and the generation AI analyzes the data to build knowledge about the company's internal circumstances. For example, the information sharing system collects sales data from the sales department and project progress data from the development department. The information sharing system then inputs the collected data into the generation AI, which then analyzes the data. For example, the generation AI analyzes the sales data from the sales department to identify which products are selling the most. The generation AI also analyzes the project progress data from the development department to determine which projects are behind schedule. Based on the analysis results, the information sharing system then generates a generation AI that is familiar with the company's internal circumstances. For example, the generation AI can instantly answer questions such as, "What are sales this month?" or "What is the progress of the ongoing project?" This improves the efficiency of information sharing within the company and strengthens collaboration between departments. This allows the information sharing system to centrally manage internal information and prevent information leaks and duplication. For example, if the same information is managed by multiple departments, the generation AI can consolidate and centralize that information, preventing duplication. This makes the information sharing system more efficient within the company and strengthens collaboration between departments.

[0029] An information sharing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a response unit, and a provision unit. The collection unit collects public information or performance data from each department. Public information includes, but is not limited to, news articles, industry reports, and patent information. Performance data from each department includes, but is not limited to, sales data, project completion data, and customer satisfaction data. The collection unit collects information such as, but is not limited to, performance reports and project progress reports from each department, and internal newsletters. The analysis unit analyzes the data collected by the collection unit. The analysis may be performed using, but is not limited to, statistical analysis, machine learning algorithms, data mining, or other methods. For example, the analysis unit may analyze sales data from the sales department to identify which products are selling the most. The analysis unit may also analyze project progress data from the development department to identify which projects are behind schedule. The generation unit builds knowledge based on the data analyzed by the analysis unit. The knowledge may be built using, but is not limited to, knowledge base construction, rule-based systems, machine learning models, and other methods. The generation unit generates a generation AI that is familiar with the company's internal circumstances based on, for example, the analysis results. The generation AI is realized using technologies such as natural language generation, image generation, and voice generation. The answering unit answers internal questions based on the knowledge constructed by the generation unit. Methods for answering questions include, but are not limited to, an FAQ system, a chatbot, and an expert system. The answering unit can quickly and accurately answer questions such as, "What are sales this month?" and "What is the progress of the ongoing project?" The providing unit provides the processing results of each unit to each department within the company. Methods for providing the results include, but are not limited to, a dashboard, report generation, and a notification system. The providing unit displays the processing results of each unit on a dashboard, allowing each department within the company to check the information in real time. As a result, the information sharing system according to the embodiment improves the efficiency of internal information sharing and strengthens collaboration between departments.

[0030] The collection unit can collect performance data or project data for each department. Performance data includes, but is not limited to, sales data, profit data, and cost data. Project data includes, but is not limited to, project progress, resource usage, and deliverables. The collection unit, for example, collects performance reports for each department to acquire sales data and profit data. The collection unit can also collect project progress reports for each department to understand project progress and resource usage. Furthermore, the collection unit can collect deliverables for each department and evaluate project results. This allows for efficient collection of performance data and project data for each department. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input performance data for each department into AI, which then automatically collects the data.

[0031] The analysis unit can analyze the collected data and build knowledge about internal company circumstances. Internal company circumstances include, but are not limited to, organizational structure, business processes, and internal regulations. For example, the analysis unit can analyze collected sales data to identify which products are selling the most. The analysis unit can also analyze collected project progress data to determine which projects are behind schedule. Furthermore, the analysis unit can analyze collected customer satisfaction data to identify products and services that are highly rated by customers. This allows for efficient building of knowledge about internal company circumstances. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which then automatically analyzes the data.

[0032] The generation unit can generate a generation AI that is familiar with internal company circumstances based on the analysis results. The generation AI can be realized using technologies such as, but not limited to, natural language generation, image generation, and speech generation. The generation unit can generate a generation AI that has knowledge of internal company circumstances based on the analysis results. For example, the generation AI can analyze sales data from the sales department to identify which products are selling the most. The generation AI can also analyze project progress data from the development department to determine which projects are behind schedule. Furthermore, the generation AI can analyze customer satisfaction data to identify products and services that are highly rated by customers. This allows for efficient generation of a generation AI that is familiar with internal company circumstances. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the analysis results into the generation AI, which can then automatically build knowledge.

[0033] The answering unit can quickly and accurately answer internal questions based on the knowledge constructed by the generation AI. "Quick and accurate" is evaluated based on criteria such as, but not limited to, response time, accuracy of the answer, and user satisfaction. The answering unit can quickly and accurately answer questions such as, "What are sales this month?" and "What is the progress of the ongoing project?" The answering unit can automatically answer frequently asked questions using, for example, an FAQ system. The answering unit can also answer user questions in real time using a chatbot. Furthermore, the answering unit can accurately answer even specialized questions using an expert system. This allows for quick and accurate answers to internal questions. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input knowledge constructed by the generation AI into an AI, which can then automatically answer questions.

[0034] The providing unit can provide the processing results of each unit to each department within the company. Processing results include, but are not limited to, for example, analysis results, generated knowledge, and response content. For example, the providing unit can display the processing results of each unit on a dashboard, allowing each department within the company to check the information in real time. The providing unit can also output the processing results of each unit as a report using a report generation function. Furthermore, the providing unit can also notify each department within the company of the processing results of each unit using a notification system. This allows the processing results of each unit to be efficiently provided to each department within the company. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the processing results of each unit into AI, and the AI ​​can automatically provide the information.

[0035] The collection unit can analyze the data submission history of each department and select a collection method. The collection unit can, for example, analyze the data submission frequency of each department and set an optimal collection schedule. The collection unit can also analyze the data submission format of each department and select an optimal collection format. Furthermore, the collection unit can analyze the data submission timing of each department and set the optimal collection timing. This makes it possible to select an optimal collection method based on the data submission history of each department. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the data submission history of each department into AI, and the AI ​​can automatically select the optimal collection method.

[0036] When collecting data, the collection unit can filter the data based on each department's current projects or areas of interest. For example, the collection unit collects only data related to each department's current projects. The collection unit can also prioritize collecting relevant data based on each department's areas of interest. Furthermore, the collection unit can filter and collect necessary data according to the progress of each department's projects. This allows data to be filtered based on each department's current projects and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input each department's project data into AI, which can then automatically perform filtering.

[0037] When collecting data, the collection unit can select a collection means according to the input method of each department. For example, if each department uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if each department uses text input, the collection unit can also prioritize collecting text data. Furthermore, if each department uses image input, the collection unit can also prioritize collecting image data. This allows the optimal collection means to be selected according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the input data of each department into AI, and the AI ​​can automatically select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting data from related areas based on the location of each department. The collection unit can also prioritize collecting nearby data by taking into account the geographical location information of each department. Furthermore, the collection unit can prioritize collecting region-specific data based on the geographical location information of each department. This allows highly relevant data to be prioritized by taking into account the geographical location information of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of each department into AI, and the AI ​​can automatically prioritize collecting highly relevant data.

[0039] The collection unit can analyze the social media activities of each department during data collection and collect relevant data. For example, the collection unit analyzes the content of social media posts by each department and collects relevant data. The collection unit can also analyze the frequency of social media activities by each department and set the optimal collection timing. Furthermore, the collection unit can analyze the number of followers on social media by each department and prioritize collection of influential data. This makes it possible to analyze the social media activities of each department and collect relevant data. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media data of each department into AI, which then automatically collects relevant data.

[0040] When collecting data, the collection unit can customize the collection method by reflecting past feedback from each department. The collection unit can, for example, improve the collection method based on past feedback from each department. The collection unit can also customize the collection means by reflecting past feedback from each department. Furthermore, the collection unit can also adjust the collection timing by referring to past feedback from each department. This allows the collection method to be customized by reflecting past feedback from each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data from each department into AI, which can then automatically customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. Furthermore, the analysis unit can set an analysis priority according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a sales analysis algorithm to sales data. The analysis unit can also apply a project progress analysis algorithm to development data. Furthermore, the analysis unit can apply an employee performance analysis algorithm to human resources data. This allows different analysis algorithms to be applied depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, and the AI ​​can automatically apply the appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results from each department. The analysis unit, for example, improves the analysis algorithm based on past analysis results from each department. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results from each department. Furthermore, the analysis unit can analyze past analysis results from each department and select the optimal analysis method. This improves the accuracy of the analysis by referring to past analysis results from each department. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results from each department into AI, which can automatically improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can set an analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI, and the AI ​​can automatically determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can also set the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, and the AI ​​can automatically adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of each department. For example, the analysis unit can provide analysis results that use a lot of technical terminology to departments with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to departments with low levels of expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the level of expertise of each department. This allows the use of technical terminology in the analysis to be adjusted according to the level of expertise of each department. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the level of expertise of each department into AI, and the AI ​​can automatically adjust the use of technical terminology in the analysis.

[0047] The generation unit can adjust the level of detail of the generation based on the importance of the analysis result during generation. For example, the generation unit generates detailed knowledge for important analysis results. The generation unit can also generate simplified knowledge for analysis results with low importance. Furthermore, the generation unit can set a generation priority according to the importance of the analysis result. This allows the level of detail of the generation to be adjusted based on the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the analysis result to the generation AI, and the generation AI can automatically adjust the level of detail of the generation.

[0048] During generation, the generation unit can apply different generation algorithms depending on the category of the analysis results. For example, the generation unit applies a sales analysis algorithm to sales data. The generation unit can also apply a project progress analysis algorithm to development data. The generation unit can also apply an employee performance analysis algorithm to human resources data. This allows different generation algorithms to be applied depending on the category of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the analysis results into the generation AI, and the generation AI can automatically apply an appropriate generation algorithm.

[0049] During generation, the generation unit can improve the accuracy of generation by referring to the past generation results of each department. The generation unit, for example, improves the generation algorithm based on the past generation results of each department. The generation unit can also improve the accuracy of generation by referring to the past generation results of each department. Furthermore, the generation unit can analyze the past generation results of each department and select the optimal generation method. This improves the accuracy of generation by referring to the past generation results of each department. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the past generation results of each department into the generation AI, and the generation AI can automatically improve the accuracy of generation.

[0050] At the time of generation, the generation unit can determine the generation priority based on the submission time of the analysis results. For example, the generation unit generates the latest analysis results with priority. The generation unit can also postpone the generation of analysis results that were submitted earlier. Furthermore, the generation unit can set a generation schedule based on the submission time. This makes it possible to determine the generation priority based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of the analysis results into the generation AI, and the generation AI can automatically determine the generation priority.

[0051] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. For example, the generation unit prioritizes the generation of highly relevant analysis results. The generation unit can also postpone the generation of less relevant analysis results. Furthermore, the generation unit can set the order of generation based on the relevance of the analysis results. This makes it possible to adjust the order of generation based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the analysis results into the generation AI, and the generation AI can automatically adjust the order of generation.

[0052] During generation, the generation unit can adjust the use of technical terminology in the generated results according to the expertise level of each department. For example, the generation unit can provide knowledge that uses a lot of technical terminology to departments with high expertise. The generation unit can also provide concise and easy-to-understand knowledge to departments with low expertise. Furthermore, the generation unit can adjust the way the generated results are expressed according to the expertise level of each department. This makes it possible to adjust the use of technical terminology in the generated results according to the expertise level of each department. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the expertise level of each department into the generation AI, and the generation AI can automatically adjust the use of technical terminology.

[0053] When answering, the answering unit can adjust the level of detail of the answer based on the importance of the question. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a simplified answer to a question of low importance. Furthermore, the answering unit can set a priority order for the answer according to 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. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the importance of the question to AI, and the AI ​​can automatically adjust the level of detail of the answer.

[0054] When answering, the answering unit can apply different answering algorithms depending on the category of the question. For example, the answering unit can apply a sales analysis algorithm to a question about sales. The answering unit can also apply a project progress analysis algorithm to a question about development. Furthermore, the answering unit can apply an employee performance analysis algorithm to a question about human resources. This allows different answering algorithms to be applied depending on the category of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the question category into AI, and the AI ​​can automatically apply an appropriate answering algorithm.

[0055] When answering a question, the answering unit can improve the accuracy of the answer by referring to the past question results of each department. The answering unit, for example, improves the answering algorithm based on the past question results of each department. The answering unit can also improve the accuracy of the answer by referring to the past question results of each department. Furthermore, the answering unit can analyze the past question results of each department and select the optimal answering method. This improves the accuracy of the answer by referring to the past question results of each department. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the past question results of each department into AI, and the AI ​​can automatically improve the accuracy of the answer.

[0056] When answering, the answering unit can determine the priority of the answers based on the time of submission of the question. For example, the answering unit prioritizes answers to the most recent questions. The answering unit can also postpone answers to questions that were submitted earlier. Furthermore, the answering unit can set an answer schedule based on the time of submission. This makes it possible to determine the priority of the answers based on the time of submission of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the time of submission of the question into AI, and the AI ​​can automatically determine the priority of the answers.

[0057] The answering unit can adjust the order of answers based on the relevance of the questions when answering. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also postpone answers to less relevant questions. Furthermore, the answering unit can set the order of answers based on the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input the relevance of questions into AI, and the AI ​​can automatically adjust the order of answers.

[0058] When answering, the answering unit can adjust the use of technical terminology in the answer depending on the expertise level of each department. For example, the answering unit can provide answers that use a lot of technical terminology to departments with high expertise. The answering unit can also provide concise and easy-to-understand answers to departments with low expertise. Furthermore, the answering unit can adjust the way the answer result is expressed depending on the expertise level of each department. This allows the use of technical terminology in the answer to be adjusted depending on the expertise level of each department. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the expertise level of each department into AI, and the AI ​​can automatically adjust the use of technical terminology in the answer.

[0059] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides detailed information for important information. The providing unit can also provide simplified information for less important information. Furthermore, the providing unit can set a priority order for the information provided according to the importance of the information. This allows the level of detail of the information provided to be adjusted based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the information into AI, and the AI ​​can automatically adjust the level of detail of the information provided.

[0060] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies a sales analysis algorithm to sales information. The providing unit can also apply a project progress analysis algorithm to development information. Furthermore, the providing unit can apply an employee performance analysis algorithm to human resources information. This allows different providing algorithms to be applied depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information into AI, and the AI ​​can automatically apply an appropriate providing algorithm.

[0061] When providing data, the provision unit can improve the accuracy of the data provision by referring to the past provision results of each department. The provision unit, for example, improves the provision algorithm based on the past provision results of each department. The provision unit can also improve the accuracy of the data provision by referring to the past provision results of each department. Furthermore, the provision unit can analyze the past provision results of each department and select the optimal provision method. This improves the accuracy of the data provision by referring to the past provision results of each department. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the past provision results of each department into AI, and the AI ​​can automatically improve the accuracy of the data provision.

[0062] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides the latest information preferentially. The providing unit can also provide information that was submitted earlier later. Furthermore, the providing unit can set a provision schedule based on the time of submission. This makes it possible to determine the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of submission of information into AI, and the AI ​​can automatically determine the priority of provision.

[0063] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. Furthermore, the providing unit can set the order of provision based on the relevance of the information. This makes it possible to adjust the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the information into AI, and the AI ​​can automatically adjust the order of provision.

[0064] The providing unit can adjust the use of technical terminology in the provided information according to the expertise level of each department when providing the information. For example, the providing unit can provide information that uses a lot of technical terminology to departments with high expertise. The providing unit can also provide concise and easy-to-understand information to departments with low expertise. Furthermore, the providing unit can adjust the way the provided results are expressed according to the expertise level of each department. This allows the use of technical terminology in the provided information to be adjusted according to the expertise level of each department. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the expertise level of each department into AI, and the AI ​​can automatically adjust the use of technical terminology.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The information sharing system can further include a forecasting unit. The forecasting unit can predict future trends and risks based on the collected data and analysis results. For example, the forecasting unit can analyze sales data from the sales unit and forecast sales for the next quarter. The forecasting unit can also analyze project progress data from the development unit and predict when the project will be completed. Furthermore, the forecasting unit can analyze customer satisfaction data and predict future customer needs and market trends. This allows the information sharing system to grasp future trends and risks in advance and take appropriate measures.

[0067] When analyzing collected data, the analysis unit can evaluate the reliability of the data. For example, it can assign a reliability score based on the source of the data and the collection method. It can also check the consistency and integrity of the data and exclude data with low reliability. It can also adjust the weighting of the analysis results depending on the reliability of the data. This allows the analysis unit to provide accurate analysis results based on highly reliable data.

[0068] When providing the processing results of each section to each department in the company, the providing section can adjust the level of detail of the information provided based on the importance of the information. For example, important information can be provided in detail. Information of low importance can also be provided in simplified form. Furthermore, the priority of the information provided can be set according to the importance of the information. This allows the level of detail of the information provided to be adjusted based on the importance of the information.

[0069] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. For example, it can prioritize collecting data from related areas based on the location of each department. It can also prioritize collecting nearby data by taking into account the geographical location information of each department. It can also prioritize collecting region-specific data based on the geographical location information of each department. This allows it to prioritize collecting highly relevant data by taking into account the geographical location information of each department.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, the analysis priority can be set according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data.

[0071] The generation unit can determine the generation priority based on the submission time of the analysis results at the time of generation. For example, the latest analysis results can be generated preferentially. Analysis results that were submitted earlier can also be generated later. Furthermore, the generation schedule can be set based on the submission time. This allows the generation priority to be determined based on the submission time of the analysis results.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection department collects public information or performance data from each department. Public information includes news articles, industry reports, patent information, etc., while performance data from each department includes sales data, project completion data, customer satisfaction data, etc. The collection department collects information such as performance reports from each department, project progress reports, and internal newsletters. Step 2: The analysis department analyzes the data collected by the collection department. The analysis is performed using methods such as statistical analysis, machine learning algorithms, and data mining. For example, sales data from the sales department can be analyzed to identify which products are selling the best. Similarly, project progress data from the development department can be analyzed to identify which projects are behind schedule. Step 3: The generation unit builds knowledge based on the data analyzed by the analysis unit. Knowledge is built using methods such as knowledge base construction, rule-based systems, and machine learning models. For example, based on the analysis results, a generative AI that is familiar with the company's internal circumstances is generated. Generative AI is realized using technologies such as natural language generation, image generation, and voice generation. Step 4: The answering unit answers internal questions based on the knowledge built by the generating unit. Methods for answering questions include FAQ systems, chatbots, and expert systems. For example, questions such as "What are sales this month?" and "What is the progress of the current project?" can be answered quickly and accurately. Step 5: The provision department provides the processing results of each department to each department within the company. Methods of provision include dashboards, report generation, notification systems, etc. For example, the processing results of each department can be displayed on a dashboard so that each department within the company can check the information in real time.

[0074] (Example 2) An information sharing system according to an embodiment of the present invention collects public information and performance data from each department, and uses a generation AI to analyze the data to create a generation AI that is familiar with the company's internal circumstances. The information sharing system collects public information and performance data from each department, and the generation AI analyzes the data to build knowledge about the company's internal circumstances. For example, the information sharing system collects sales data from the sales department and project progress data from the development department. The information sharing system then inputs the collected data into the generation AI, which then analyzes the data. For example, the generation AI analyzes the sales data from the sales department to identify which products are selling the most. The generation AI also analyzes the project progress data from the development department to determine which projects are behind schedule. Based on the analysis results, the information sharing system then generates a generation AI that is familiar with the company's internal circumstances. For example, the generation AI can instantly answer questions such as, "What are sales this month?" or "What is the progress of the ongoing project?" This improves the efficiency of information sharing within the company and strengthens collaboration between departments. This allows the information sharing system to centrally manage internal information and prevent information leaks and duplication. For example, if the same information is managed by multiple departments, the generation AI can consolidate and centralize that information, preventing duplication. This makes the information sharing system more efficient within the company and strengthens collaboration between departments.

[0075] An information sharing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a response unit, and a provision unit. The collection unit collects public information or performance data from each department. Public information includes, but is not limited to, news articles, industry reports, and patent information. Performance data from each department includes, but is not limited to, sales data, project completion data, and customer satisfaction data. The collection unit collects information such as, but is not limited to, performance reports and project progress reports from each department, and internal newsletters. The analysis unit analyzes the data collected by the collection unit. The analysis may be performed using, but is not limited to, statistical analysis, machine learning algorithms, data mining, or other methods. For example, the analysis unit may analyze sales data from the sales department to identify which products are selling the most. The analysis unit may also analyze project progress data from the development department to identify which projects are behind schedule. The generation unit builds knowledge based on the data analyzed by the analysis unit. The knowledge may be built using, but is not limited to, knowledge base construction, rule-based systems, machine learning models, and other methods. The generation unit generates a generation AI that is familiar with the company's internal circumstances based on, for example, the analysis results. The generation AI is realized using technologies such as natural language generation, image generation, and voice generation. The answering unit answers internal questions based on the knowledge constructed by the generation unit. Methods for answering questions include, but are not limited to, an FAQ system, a chatbot, and an expert system. The answering unit can quickly and accurately answer questions such as, "What are sales this month?" and "What is the progress of the ongoing project?" The providing unit provides the processing results of each unit to each department within the company. Methods for providing the results include, but are not limited to, a dashboard, report generation, and a notification system. The providing unit displays the processing results of each unit on a dashboard, allowing each department within the company to check the information in real time. As a result, the information sharing system according to the embodiment improves the efficiency of internal information sharing and strengthens collaboration between departments.

[0076] The collection unit can collect performance data or project data for each department. Performance data includes, but is not limited to, sales data, profit data, and cost data. Project data includes, but is not limited to, project progress, resource usage, and deliverables. The collection unit, for example, collects performance reports for each department to acquire sales data and profit data. The collection unit can also collect project progress reports for each department to understand project progress and resource usage. Furthermore, the collection unit can collect deliverables for each department and evaluate project results. This allows for efficient collection of performance data and project data for each department. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input performance data for each department into AI, which then automatically collects the data.

[0077] The analysis unit can analyze the collected data and build knowledge about internal company circumstances. Internal company circumstances include, but are not limited to, organizational structure, business processes, and internal regulations. For example, the analysis unit can analyze collected sales data to identify which products are selling the most. The analysis unit can also analyze collected project progress data to determine which projects are behind schedule. Furthermore, the analysis unit can analyze collected customer satisfaction data to identify products and services that are highly rated by customers. This allows for efficient building of knowledge about internal company circumstances. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which then automatically analyzes the data.

[0078] The generation unit can generate a generation AI that is familiar with internal company circumstances based on the analysis results. The generation AI can be realized using technologies such as, but not limited to, natural language generation, image generation, and speech generation. The generation unit can generate a generation AI that has knowledge of internal company circumstances based on the analysis results. For example, the generation AI can analyze sales data from the sales department to identify which products are selling the most. The generation AI can also analyze project progress data from the development department to determine which projects are behind schedule. Furthermore, the generation AI can analyze customer satisfaction data to identify products and services that are highly rated by customers. This allows for efficient generation of a generation AI that is familiar with internal company circumstances. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the analysis results into the generation AI, which can then automatically build knowledge.

[0079] The answering unit can quickly and accurately answer internal questions based on the knowledge constructed by the generation AI. "Quick and accurate" is evaluated based on criteria such as, but not limited to, response time, accuracy of the answer, and user satisfaction. The answering unit can quickly and accurately answer questions such as, "What are sales this month?" and "What is the progress of the ongoing project?" The answering unit can automatically answer frequently asked questions using, for example, an FAQ system. The answering unit can also answer user questions in real time using a chatbot. Furthermore, the answering unit can accurately answer even specialized questions using an expert system. This allows for quick and accurate answers to internal questions. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input knowledge constructed by the generation AI into an AI, which can then automatically answer questions.

[0080] The providing unit can provide the processing results of each unit to each department within the company. Processing results include, but are not limited to, for example, analysis results, generated knowledge, and response content. For example, the providing unit can display the processing results of each unit on a dashboard, allowing each department within the company to check the information in real time. The providing unit can also output the processing results of each unit as a report using a report generation function. Furthermore, the providing unit can also notify each department within the company of the processing results of each unit using a notification system. This allows the processing results of each unit to be efficiently provided to each department within the company. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the processing results of each unit into AI, and the AI ​​can automatically provide the information.

[0081] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the timing of data collection.

[0082] The collection unit can analyze the data submission history of each department and select a collection method. The collection unit can, for example, analyze the data submission frequency of each department and set an optimal collection schedule. The collection unit can also analyze the data submission format of each department and select an optimal collection format. Furthermore, the collection unit can analyze the data submission timing of each department and set the optimal collection timing. This makes it possible to select an optimal collection method based on the data submission history of each department. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the data submission history of each department into AI, and the AI ​​can automatically select the optimal collection method.

[0083] When collecting data, the collection unit can filter the data based on each department's current projects or areas of interest. For example, the collection unit collects only data related to each department's current projects. The collection unit can also prioritize collecting relevant data based on each department's areas of interest. Furthermore, the collection unit can filter and collect necessary data according to the progress of each department's projects. This allows data to be filtered based on each department's current projects and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input each department's project data into AI, which can then automatically perform filtering.

[0084] When collecting data, the collection unit can select a collection means according to the input method of each department. For example, if each department uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if each department uses text input, the collection unit can also prioritize collecting text data. Furthermore, if each department uses image input, the collection unit can also prioritize collecting image data. This allows the optimal collection means to be selected according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the input data of each department into AI, and the AI ​​can automatically select the optimal collection means.

[0085] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This allows the priority of data to be collected to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and determine the priority of data to be collected.

[0086] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting data from related areas based on the location of each department. The collection unit can also prioritize collecting nearby data by taking into account the geographical location information of each department. Furthermore, the collection unit can prioritize collecting region-specific data based on the geographical location information of each department. This allows highly relevant data to be prioritized by taking into account the geographical location information of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of each department into AI, and the AI ​​can automatically prioritize collecting highly relevant data.

[0087] The collection unit can analyze the social media activities of each department during data collection and collect relevant data. For example, the collection unit analyzes the content of social media posts by each department and collects relevant data. The collection unit can also analyze the frequency of social media activities by each department and set the optimal collection timing. Furthermore, the collection unit can analyze the number of followers on social media by each department and prioritize collection of influential data. This makes it possible to analyze the social media activities of each department and collect relevant data. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media data of each department into AI, which then automatically collects relevant data.

[0088] When collecting data, the collection unit can customize the collection method by reflecting past feedback from each department. The collection unit can, for example, improve the collection method based on past feedback from each department. The collection unit can also customize the collection means by reflecting past feedback from each department. Furthermore, the collection unit can also adjust the collection timing by referring to past feedback from each department. This allows the collection method to be customized by reflecting past feedback from each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data from each department into AI, which can then automatically customize the collection method.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the presentation method of the analysis.

[0090] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. Furthermore, the analysis unit can set an analysis priority according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a sales analysis algorithm to sales data. The analysis unit can also apply a project progress analysis algorithm to development data. Furthermore, the analysis unit can apply an employee performance analysis algorithm to human resources data. This allows different analysis algorithms to be applied depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, and the AI ​​can automatically apply the appropriate analysis algorithm.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results from each department. The analysis unit, for example, improves the analysis algorithm based on past analysis results from each department. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results from each department. Furthermore, the analysis unit can analyze past analysis results from each department and select the optimal analysis method. This improves the accuracy of the analysis by referring to past analysis results from each department. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results from each department into AI, which can automatically improve the accuracy of the analysis.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the length of the analysis.

[0094] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can set an analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI, and the AI ​​can automatically determine the priority of analysis.

[0095] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can also set the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, and the AI ​​can automatically adjust the order of analysis.

[0096] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of each department. For example, the analysis unit can provide analysis results that use a lot of technical terminology to departments with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to departments with low levels of expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the level of expertise of each department. This allows the use of technical terminology in the analysis to be adjusted according to the level of expertise of each department. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the level of expertise of each department into AI, and the AI ​​can automatically adjust the use of technical terminology in the analysis.

[0097] The generation unit can estimate the user's emotions and adjust the representation method of the generated knowledge based on the estimated user emotions. For example, the generation unit can provide detailed knowledge when the user is relaxed. Furthermore, the generation unit can provide knowledge that focuses on the main points when the user is in a hurry. Furthermore, the generation unit can provide visually stimulating knowledge when the user is excited. This allows the representation method of the generated knowledge to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the representation method of the knowledge.

[0098] The generation unit can adjust the level of detail of the generation based on the importance of the analysis result during generation. For example, the generation unit generates detailed knowledge for important analysis results. The generation unit can also generate simplified knowledge for analysis results with low importance. Furthermore, the generation unit can set a generation priority according to the importance of the analysis result. This allows the level of detail of the generation to be adjusted based on the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the analysis result to the generation AI, and the generation AI can automatically adjust the level of detail of the generation.

[0099] During generation, the generation unit can apply different generation algorithms depending on the category of the analysis results. For example, the generation unit applies a sales analysis algorithm to sales data. The generation unit can also apply a project progress analysis algorithm to development data. The generation unit can also apply an employee performance analysis algorithm to human resources data. This allows different generation algorithms to be applied depending on the category of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the analysis results into the generation AI, and the generation AI can automatically apply an appropriate generation algorithm.

[0100] During generation, the generation unit can improve the accuracy of generation by referring to the past generation results of each department. The generation unit, for example, improves the generation algorithm based on the past generation results of each department. The generation unit can also improve the accuracy of generation by referring to the past generation results of each department. Furthermore, the generation unit can analyze the past generation results of each department and select the optimal generation method. This improves the accuracy of generation by referring to the past generation results of each department. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the past generation results of each department into the generation AI, and the generation AI can automatically improve the accuracy of generation.

[0101] The generation unit can estimate the user's emotions and adjust the length of the knowledge to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can provide short, concise knowledge. The generation unit can also provide detailed knowledge if the user is relaxed. Furthermore, the generation unit can provide visually stimulating knowledge if the user is excited. This allows the length of the knowledge to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then automatically estimate the emotion and adjust the length of the knowledge.

[0102] At the time of generation, the generation unit can determine the generation priority based on the submission time of the analysis results. For example, the generation unit generates the latest analysis results with priority. The generation unit can also postpone the generation of analysis results that were submitted earlier. Furthermore, the generation unit can set a generation schedule based on the submission time. This makes it possible to determine the generation priority based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of the analysis results into the generation AI, and the generation AI can automatically determine the generation priority.

[0103] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. For example, the generation unit prioritizes the generation of highly relevant analysis results. The generation unit can also postpone the generation of less relevant analysis results. Furthermore, the generation unit can set the order of generation based on the relevance of the analysis results. This makes it possible to adjust the order of generation based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the analysis results into the generation AI, and the generation AI can automatically adjust the order of generation.

[0104] During generation, the generation unit can adjust the use of technical terminology in the generated results according to the expertise level of each department. For example, the generation unit can provide knowledge that uses a lot of technical terminology to departments with high expertise. The generation unit can also provide concise and easy-to-understand knowledge to departments with low expertise. Furthermore, the generation unit can adjust the way the generated results are expressed according to the expertise level of each department. This makes it possible to adjust the use of technical terminology in the generated results according to the expertise level of each department. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the expertise level of each department into the generation AI, and the generation AI can automatically adjust the use of technical terminology.

[0105] The answering unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is nervous, the answering unit can provide a simple, highly visible answer. Furthermore, if the user is relaxed, the answering unit can provide a detailed answer. Furthermore, if the user is in a hurry, the answering unit can provide a quick answer. This allows the way the answer is expressed to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the answering unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the way the answer is expressed.

[0106] When answering, the answering unit can adjust the level of detail of the answer based on the importance of the question. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a simplified answer to a question of low importance. Furthermore, the answering unit can set a priority order for the answer according to 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. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the importance of the question to AI, and the AI ​​can automatically adjust the level of detail of the answer.

[0107] When answering, the answering unit can apply different answering algorithms depending on the category of the question. For example, the answering unit can apply a sales analysis algorithm to a question about sales. The answering unit can also apply a project progress analysis algorithm to a question about development. Furthermore, the answering unit can apply an employee performance analysis algorithm to a question about human resources. This allows different answering algorithms to be applied depending on the category of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the question category into AI, and the AI ​​can automatically apply an appropriate answering algorithm.

[0108] When answering a question, the answering unit can improve the accuracy of the answer by referring to the past question results of each department. The answering unit, for example, improves the answering algorithm based on the past question results of each department. The answering unit can also improve the accuracy of the answer by referring to the past question results of each department. Furthermore, the answering unit can analyze the past question results of each department and select the optimal answering method. This improves the accuracy of the answer by referring to the past question results of each department. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the past question results of each department into AI, and the AI ​​can automatically improve the accuracy of the answer.

[0109] The answering unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is in a hurry, the answering unit can provide a short and to-the-point answer. The answering unit can also provide a detailed answer if the user is relaxed. Furthermore, if the user is excited, the answering unit can also provide a visually stimulating answer. This allows the length of the answer to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the answering unit can input the user's emotion data into the generation AI, which can then automatically estimate the emotion and adjust the length of the answer.

[0110] When answering, the answering unit can determine the priority of the answers based on the time of submission of the question. For example, the answering unit prioritizes answers to the most recent questions. The answering unit can also postpone answers to questions that were submitted earlier. Furthermore, the answering unit can set an answer schedule based on the time of submission. This makes it possible to determine the priority of the answers based on the time of submission of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the time of submission of the question into AI, and the AI ​​can automatically determine the priority of the answers.

[0111] The answering unit can adjust the order of answers based on the relevance of the questions when answering. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also postpone answers to less relevant questions. Furthermore, the answering unit can set the order of answers based on the relevance of the questions. This makes it possible to adjust the order of answers based on the relevance of the questions. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input the relevance of questions into AI, and the AI ​​can automatically adjust the order of answers.

[0112] When answering, the answering unit can adjust the use of technical terminology in the answer depending on the expertise level of each department. For example, the answering unit can provide answers that use a lot of technical terminology to departments with high expertise. The answering unit can also provide concise and easy-to-understand answers to departments with low expertise. Furthermore, the answering unit can adjust the way the answer result is expressed depending on the expertise level of each department. This allows the use of technical terminology in the answer to be adjusted depending on the expertise level of each department. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the expertise level of each department into AI, and the AI ​​can automatically adjust the use of technical terminology in the answer.

[0113] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can also provide detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide information that focuses on the main points. This allows the presentation method of the information to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the presentation method of the information.

[0114] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides detailed information for important information. The providing unit can also provide simplified information for less important information. Furthermore, the providing unit can set a priority order for the information provided according to the importance of the information. This allows the level of detail of the information provided to be adjusted based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the information into AI, and the AI ​​can automatically adjust the level of detail of the information provided.

[0115] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies a sales analysis algorithm to sales information. The providing unit can also apply a project progress analysis algorithm to development information. Furthermore, the providing unit can apply an employee performance analysis algorithm to human resources information. This allows different providing algorithms to be applied depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information into AI, and the AI ​​can automatically apply an appropriate providing algorithm.

[0116] When providing data, the provision unit can improve the accuracy of the data provision by referring to the past provision results of each department. The provision unit, for example, improves the provision algorithm based on the past provision results of each department. The provision unit can also improve the accuracy of the data provision by referring to the past provision results of each department. Furthermore, the provision unit can analyze the past provision results of each department and select the optimal provision method. This improves the accuracy of the data provision by referring to the past provision results of each department. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the past provision results of each department into AI, and the AI ​​can automatically improve the accuracy of the data provision.

[0117] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. The providing unit can also provide detailed information if the user is relaxed. Furthermore, if the user is excited, the providing unit can provide visually stimulating information. This allows the length of the information to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the length of the information.

[0118] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides the latest information preferentially. The providing unit can also provide information that was submitted earlier later. Furthermore, the providing unit can set a provision schedule based on the time of submission. This makes it possible to determine the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of submission of information into AI, and the AI ​​can automatically determine the priority of provision.

[0119] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. Furthermore, the providing unit can set the order of provision based on the relevance of the information. This makes it possible to adjust the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the information into AI, and the AI ​​can automatically adjust the order of provision.

[0120] The providing unit can adjust the use of technical terminology in the provided information according to the expertise level of each department when providing the information. For example, the providing unit can provide information that uses a lot of technical terminology to departments with high expertise. The providing unit can also provide concise and easy-to-understand information to departments with low expertise. Furthermore, the providing unit can adjust the way the provided results are expressed according to the expertise level of each department. This allows the use of technical terminology in the provided information to be adjusted according to the expertise level of each department. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the expertise level of each department into AI, and the AI ​​can automatically adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, response unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects public information and performance data of each department using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs knowledge based on the analysis results. The response unit is realized, for example, by the control unit 46A of the smart device 14 and answers internal questions. The provision unit provides the processing results of each unit to each internal department using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, answering unit, and providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects public information and performance data of each department using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs knowledge based on the analysis results. The answering unit is realized, for example, by the control unit 46A of the smart glasses 214 and answers internal questions. The providing unit provides the processing results of each unit to each internal department using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, answering unit, and providing unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects public information and performance data of each department using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs knowledge based on the analysis results. The answering unit is realized, for example, by the control unit 46A of the headset terminal 314 and answers internal questions. The providing unit provides the processing results of each unit to each internal department using, for example, the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, answering unit, and providing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects public information and performance data of each department using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs knowledge based on the analysis results. The answering unit is realized, for example, by the control unit 46A of the robot 414 and answers internal questions. The providing unit provides the processing results of each unit to each internal department using, for example, the speaker 240 of the robot 414.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The information sharing system can further include a forecasting unit. The forecasting unit can predict future trends and risks based on the collected data and analysis results. For example, the forecasting unit can analyze sales data from the sales unit and forecast sales for the next quarter. The forecasting unit can also analyze project progress data from the development unit and predict when the project will be completed. Furthermore, the forecasting unit can analyze customer satisfaction data and predict future customer needs and market trends. This allows the information sharing system to grasp future trends and risks in advance and take appropriate measures.

[0123] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed information. Furthermore, if the user is in a hurry, only important data can be collected preferentially. This makes it possible to adjust the timing of data collection according to the user's emotions.

[0124] When analyzing collected data, the analysis unit can evaluate the reliability of the data. For example, it can assign a reliability score based on the source of the data and the collection method. It can also check the consistency and integrity of the data and exclude data with low reliability. It can also adjust the weighting of the analysis results depending on the reliability of the data. This allows the analysis unit to provide accurate analysis results based on highly reliable data.

[0125] When generating a generation AI that is familiar with the company's internal circumstances based on the analysis results, the generation unit can estimate the user's emotions and adjust the way in which the knowledge is expressed based on the estimated user emotions. For example, if the user is relaxed, detailed knowledge can be provided. If the user is in a hurry, knowledge that focuses on the main points can be provided. Furthermore, if the user is excited, visually stimulating knowledge can be provided. This makes it possible to adjust the way in which the knowledge is expressed according to the user's emotions.

[0126] The answering section can estimate the user's emotions based on the knowledge built by the generation AI when quickly and accurately answering internal questions, and can adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible answer. If the user is relaxed, it can also provide a detailed answer. Furthermore, if the user is in a hurry, it can provide an answer that focuses on the main points. This makes it possible to adjust the way the answer is expressed depending on the user's emotions.

[0127] When providing the processing results of each section to each department in the company, the providing section can adjust the level of detail of the information provided based on the importance of the information. For example, important information can be provided in detail. Information of low importance can also be provided in simplified form. Furthermore, the priority of the information provided can be set according to the importance of the information. This allows the level of detail of the information provided to be adjusted based on the importance of the information.

[0128] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. For example, it can prioritize collecting data from related areas based on the location of each department. It can also prioritize collecting nearby data by taking into account the geographical location information of each department. It can also prioritize collecting region-specific data based on the geographical location information of each department. This allows it to prioritize collecting highly relevant data by taking into account the geographical location information of each department.

[0129] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, the analysis priority can be set according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data.

[0130] The generation unit can determine the generation priority based on the submission time of the analysis results at the time of generation. For example, the latest analysis results can be generated preferentially. Analysis results that were submitted earlier can also be generated later. Furthermore, the generation schedule can be set based on the submission time. This allows the generation priority to be determined based on the submission time of the analysis results.

[0131] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user's emotions. For example, if the user is in a hurry, short, to-the-point information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is excited, visually stimulating information can be provided. In this way, the length of the information to be provided can be adjusted according to the user's emotions.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection department collects public information or performance data from each department. Public information includes news articles, industry reports, patent information, etc., while performance data from each department includes sales data, project completion data, customer satisfaction data, etc. The collection department collects information such as performance reports from each department, project progress reports, and internal newsletters. Step 2: The analysis department analyzes the data collected by the collection department. The analysis is performed using methods such as statistical analysis, machine learning algorithms, and data mining. For example, sales data from the sales department can be analyzed to identify which products are selling the best. Similarly, project progress data from the development department can be analyzed to identify which projects are behind schedule. Step 3: The generation unit builds knowledge based on the data analyzed by the analysis unit. Knowledge is built using methods such as knowledge base construction, rule-based systems, and machine learning models. For example, based on the analysis results, a generative AI that is familiar with the company's internal circumstances is generated. Generative AI is realized using technologies such as natural language generation, image generation, and voice generation. Step 4: The answering unit answers internal questions based on the knowledge built by the generating unit. Methods for answering questions include FAQ systems, chatbots, and expert systems. For example, questions such as "What are sales this month?" and "What is the progress of the current project?" can be answered quickly and accurately. Step 5: The provision department provides the processing results of each department to each department within the company. Methods of provision include dashboards, report generation, notification systems, etc. For example, the processing results of each department can be displayed on a dashboard so that each department within the company can check the information in real time.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 AI 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 AI 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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 AI 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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, in order to avoid confusion and to 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects public information or performance data of each department; an analysis unit that analyzes the data collected by the collection unit; a generation unit that constructs knowledge based on the data analyzed by the analysis unit; an answering unit that answers questions within the company based on the knowledge constructed by the generating unit; a providing unit that provides the processing results of each of the units; A system characterized by:

2. The collecting unit Collect performance or project data for each department 2. The system of claim 1.

3. The analysis unit Analyze the collected data and build knowledge about the internal context 2. The system of claim 1.

4. The generation unit Based on the analysis results, we generate AI that is familiar with the company's internal circumstances.

2. The system of claim 1.

5. The answering section Generative AI provides fast and accurate answers to internal questions based on knowledge it has built.

2. The system of claim 1.

6. The providing unit Provide the processing results of each department to each department within the company 2. The system of claim 1.

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

8. The collecting unit Analyze the data submission history of each department and select a collection method 2. The system of claim 1.

Images