system

Abstract

The system according to this embodiment aims to expedite access to internal company information and improve operational efficiency. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a summarization unit, a visualization unit, and a provision unit. The collection unit crawls the entire intranet to collect information. The analysis unit analyzes the information collected by the collection unit using a generation AI. The summarization unit summarizes the information analyzed by the analysis unit. The visualization unit visually analyzes the information summarized by the summarization unit. The provision unit provides links to the locations of the original data of the information analyzed by the visualization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to determine where the necessary information is within the company, and information search takes time and is inefficient.

[0005] The system according to the embodiment aims to speed up access to in-company information and improve work efficiency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a summarization unit, a visualization unit, and a provision unit. The collection unit crawls the entire intranet to collect information. The analysis unit analyzes the information collected by the collection unit using a generation AI. The summarization unit summarizes the information analyzed by the analysis unit. The visualization unit visually analyzes the information summarized by the summarization unit. The provision unit provides links to the locations of the original data of the information analyzed by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can expedite access to internal company information and improve operational efficiency. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An internal information discovery AI agent system according to an embodiment of the present invention is a system that efficiently searches, analyzes, summarizes, visualizes, and enables rapid access to internal company information. This internal information discovery AI agent system crawls the entire intranet and collects relevant information from various sources such as documents, databases, emails, and chat logs. Next, it uses generative AI to analyze the collected information in natural language and summarize it into a short report. Furthermore, it utilizes generative AI to perform a visual analysis of the information, highlighting important concepts and illustrating the correlations and relationships between each piece of information. Finally, it provides links to the location of the original data of the provided information, allowing users to check the details. For example, the internal information discovery AI agent system crawls the entire intranet and collects relevant information from various sources such as documents, databases, emails, and chat logs. In this process, the scope and target of the crawl are set to efficiently collect information. For example, it prioritizes collecting information related to a specific project or information containing specific keywords. Next, the internal information discovery AI agent system uses generative AI to analyze the collected information in natural language and summarize it into a short report. The generative AI understands the context of the collected information and extracts the key points. For example, by analyzing and summarizing documents containing important information, such as meeting minutes and project progress reports, the system enables quick information gathering. Furthermore, the internal information retrieval AI agent system utilizes generative AI to visually analyze information and highlight key concepts. The generative AI illustrates the correlations and relationships between information, providing it in a visually easy-to-understand format. For example, by illustrating project progress and the collaboration status between departments, it makes it easier to grasp the overall situation. Finally, the internal information retrieval AI agent system provides links to the source data of the information provided, allowing users to check details. By clicking the links, users can access the original sources and check detailed information. This enables quick access to necessary information and improves work efficiency. As a result, the internal information retrieval AI agent system speeds up access to internal information and improves work efficiency. It reduces wasted time on information searching, allowing users to focus on important tasks.Furthermore, it supports decision-making through consistent information provision. For example, by quickly understanding project progress and taking appropriate action, the project's success rate can be increased. As a result, the internal information retrieval AI agent system can expedite access to internal information and improve operational efficiency.

[0029] The internal information discovery AI agent system according to this embodiment comprises a collection unit, an analysis unit, a summarization unit, a visualization unit, and a provision unit. The collection unit crawls the entire intranet to collect information. The collection unit collects information from various sources, such as documents, databases, emails, and chat logs. The collection unit can, for example, prioritize the collection of information related to a specific project or information containing specific keywords. The collection unit can, for example, set the scope and target of the crawl to efficiently collect information. The analysis unit analyzes the collected information using generative AI. The analysis unit can, for example, understand the context of the collected information and extract key points. The analysis unit can, for example, analyze and summarize documents containing important information, such as meeting minutes or project progress reports. The analysis unit can, for example, use generative AI to extract important parts of the information and perform a summary. The summarization unit summarizes the information analyzed using generative AI. The summarization unit can, for example, extract the key points of the collected information and summarize them into a short report. The summarization unit can, for example, use generative AI to concisely summarize the key points of information. The summarization unit can, for example, use generative AI to extract important parts of information and summarize them. The visualization unit visually analyzes the information summarized using generative AI. The visualization unit can, for example, illustrate correlations and relationships between information and provide them in a visually easy-to-understand format. The visualization unit can, for example, illustrate project progress or the status of collaboration between departments. The visualization unit can, for example, use generative AI to extract important parts of information and visually analyze them. The provisioning unit provides links to the location of the original data of the information analyzed by the visualization unit. The provisioning unit can, for example, present links to the location of the original data of the provided information, allowing users to check details. The provisioning unit allows users to, for example, access the original information source and check detailed information by clicking on the links. The provisioning unit can, for example, use generative AI to provide links to the location of the original data of the information. As a result, the internal information discovery AI agent system according to this embodiment can efficiently search, analyze, summarize, visualize, and quickly access internal information.Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit crawls the entire intranet to collect information, it can use AI to set the scope and target of the crawl to efficiently collect information. Some or all of the processing described above in the analysis unit may be performed using generative AI, for example, or without generative AI. For example, when the analysis unit understands the context of the collected information and extracts the key points, it can use generative AI to extract the important parts of the information and summarize it. Some or all of the processing described above in the summarization unit may be performed using generative AI, for example, or without generative AI. For example, when the summarization unit extracts the key points of the collected information and summarizes it into a short report, it can use generative AI to extract the important parts of the information and summarize it. Some or all of the processing described above in the visualization unit may be performed using generative AI, for example, or without generative AI. For example, when the visualization unit illustrates the correlations and relationships of information and provides it in a visually easy-to-understand format, it can use generative AI to extract the important parts of the information and perform a visual analysis. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, when the provisioning unit presents the location of the original data of the provided information via a link so that details can be viewed, it may use AI to provide the location of the original data of the information via a link.

[0030] The data collection unit crawls the entire intranet to gather information. It collects information from diverse sources, such as documents, databases, emails, and chat logs. Specifically, it accesses various servers and databases within the intranet and automatically retrieves regularly updated information. By setting the scope and target of the crawl, the data collection unit can prioritize the collection of information related to specific projects or information containing specific keywords. For example, project names and related keywords can be pre-set, and relevant documents and emails can be collected preferentially based on these. Furthermore, the data collection unit can quickly obtain the latest information by adjusting the frequency and timing of crawls. For instance, information related to important projects can be crawled daily to collect the latest information, while other information can be crawled weekly, enabling efficient information gathering. In addition, the data collection unit centrally manages the collected information and stores it in a database. This allows the analysis and summarization units to quickly access the necessary information. The data collection unit can also use AI to dynamically set the scope and target of the crawl to efficiently collect information. For example, the AI ​​can analyze past collected data and automatically adjust the crawl scope based on specific keywords and patterns. This allows the data collection unit to efficiently gather the latest and most relevant information at all times.

[0031] The analysis unit analyzes collected information using generative AI. The analysis unit understands the context of the collected information and extracts key points. Specifically, it uses natural language processing techniques to analyze document content and extract important keywords and phrases. For example, it can analyze and summarize documents containing important information, such as meeting minutes or project progress reports. The generative AI analyzes context and meaning to understand the document content and extract important parts. For example, it can extract important decisions and action items from meeting minutes and important milestones and issues from project progress reports. The analysis unit can use generative AI to extract and summarize important parts of information. For example, the generative AI understands context and meaning to analyze document content and summarize important parts. This allows the analysis unit to quickly and accurately analyze collected information and extract important information. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of success for future projects. This allows the analysis unit to perform not only real-time information analysis but also long-term trend analysis and predictions.

[0032] The summarization unit uses generative AI to summarize the analyzed information. It extracts the key points of the collected information and summarizes them into a short report. Specifically, it uses generative AI to extract and summarize the important parts of the information. For example, it summarizes documents containing important information, such as meeting minutes or project progress reports, into short reports. The generative AI analyzes the context and meaning to understand the content of the document and extract the important parts. For example, it extracts important decisions and action items from meeting minutes, and important milestones and issues from project progress reports. The summarization unit can use generative AI to concisely summarize the key points of the information. For example, the generative AI analyzes the content of the document and understands the context and meaning to summarize the important parts. This allows the summarization unit to quickly and accurately summarize the collected information and extract the important information. Furthermore, the summarization unit can also utilize historical data and statistics to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of success for future projects. This allows the summarization unit to perform not only real-time information summarization but also long-term trend analysis and forecasting.

[0033] The visualization unit uses generative AI to visually analyze summarized information. The visualization unit illustrates the correlations and relationships between information, providing it in a visually easy-to-understand format. Specifically, it uses generative AI to extract important parts of the information and visually analyze them. For example, it can illustrate project progress or the collaboration status between departments. The generative AI analyzes data structure and patterns to analyze the correlations and relationships between information and provide them in a visually easy-to-understand format. For example, it can display project progress using Gantt charts and timelines, and collaboration status between departments using flowcharts and network diagrams. The visualization unit can use generative AI to extract important parts of information and visually analyze them. For example, the generative AI analyzes data structure and patterns to analyze the correlations and relationships between information and provide them in a visually easy-to-understand format. This allows the visualization unit to quickly and accurately visualize collected information and extract important information. Furthermore, the visualization unit can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of future project success. This allows the visualization unit to perform not only real-time information visualization but also long-term trend analysis and prediction.

[0034] The service provider provides links to the locations of the source data of the information analyzed by the visualization service provider. The service provider presents links to the locations of the source data of the provided information, allowing users to view details. Specifically, it uses generative AI to provide links to the locations of the source data of the information. For example, by clicking a link, the user can access the original source and view detailed information. The service provider can provide links to the locations of the source data of the information using generative AI. For example, the generative AI analyzes the location of the source data of the information and generates links. This allows the service provider to provide collected information quickly and accurately and extract important information. Furthermore, the service provider can also use historical data and statistical information to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of success of future projects. This allows the service provider to not only provide real-time information but also perform long-term trend analysis and predictions.

[0035] The data collection unit can collect information from a variety of sources, such as documents, databases, emails, and chat logs. For example, the data collection unit can prioritize the collection of information related to a specific project or information containing specific keywords. The data collection unit can efficiently collect information by setting the scope and target of the crawl. This enables comprehensive information collection by collecting information from a variety of sources. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, when collecting information from a variety of sources, such as documents, databases, emails, and chat logs, the data collection unit can efficiently collect information using AI.

[0036] The analysis unit can understand the context of the collected information and extract key points. For example, the analysis unit can understand the context of the collected information and extract key points. The analysis unit can analyze and summarize documents containing important information, such as meeting minutes or project progress reports. The analysis unit can extract and summarize important parts of the information using, for example, generative AI. This allows for efficient grasp of important information by understanding the context of the information and extracting key points. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit understands the context of the collected information and extracts key points, it can use generative AI to extract and summarize important parts of the information.

[0037] The summarization unit can summarize information such as meeting minutes and project progress reports. For example, the summarization unit can summarize information such as meeting minutes and project progress reports. For example, the summarization unit can extract the key points of collected information and summarize them into a short report. For example, the summarization unit can use generative AI to concisely summarize the key points of the information. This allows for a concise understanding of important information by summarizing information such as meeting minutes and project progress reports. Some or all of the above-described processes in the summarization unit may be performed using, for example, generative AI, or without generative AI. For example, when the summarization unit extracts the key points of collected information and summarizes them into a short report, it can use generative AI to extract the important parts of the information and perform the summary.

[0038] The visualization unit can illustrate the correlations and relationships between information. For example, the visualization unit can illustrate the correlations and relationships between information. For example, the visualization unit can illustrate the progress of a project or the status of collaboration between departments. The visualization unit can extract important parts of information and analyze them visually, for example, using a generative AI. This makes it easier to understand the information by illustrating the correlations and relationships between information. Some or all of the above-described processing in the visualization unit may be performed using a generative AI, for example, or without a generative AI. For example, when the visualization unit illustrates the correlations and relationships between information and provides it in a visually easy-to-understand format, it can extract important parts of the information and analyze them visually using a generative AI.

[0039] The provider can provide a link to the location of the source data of the provided information. For example, the provider can provide a link to the location of the source data of the provided information, allowing users to view details. For example, the provider can allow users to access the original source and view detailed information by clicking the link. The provider can provide a link to the location of the source data of the information, for example, using generative AI. This allows for quick access to detailed information by providing a link to the location of the source data of the provided information. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, when providing a link to the location of the source data of the provided information, allowing users to view details, the provider can use AI to provide a link to the location of the source data of the information.

[0040] The data collection unit can analyze the user's past information gathering history and select the optimal data collection method. For example, the data collection unit can prioritize crawling information sources that the user has frequently collected in the past. For example, the data collection unit can collect relevant information based on keywords the user has used in the past. For example, the data collection unit can optimize the information to be collected at specific time periods based on the user's past data collection history. This allows the optimal data collection method to be selected by analyzing the user's past data gathering history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit analyzes the user's past data gathering history and selects the optimal data collection method, it may use AI to select the optimal data collection method.

[0041] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. For example, the data collection unit can collect necessary information according to the progress of the user's projects. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to filter information when filtering based on the user's current projects and areas of interest during data collection.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter and collect relevant information based on the user's geographical location. For example, if the user is on the move, the data collection unit can prioritize the collection of information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit prioritizes the collection of highly relevant information by considering the user's geographical location when collecting information, it may use AI to filter the information.

[0043] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can collect relevant information based on information shared by the user's social media followers and friends. For example, the data collection unit can collect relevant information by analyzing the user's social media activity history. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit analyzes the user's social media activity and collects relevant information during data collection, it may use AI to collect the information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis, it can use a generative AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply an image analysis algorithm to image data. By applying different analysis algorithms depending on the category of information, appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit applies different analysis algorithms depending on the category of information during analysis, it can use generative AI to apply the analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the information submission date during analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for information that has been submitted earlier. For example, the analysis unit may dynamically adjust the priority of analysis according to the submission date. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the information submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when determining the priority of analysis based on the information submission date during analysis, the analysis unit may use a generative AI to determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis according to the relevance of the information. This allows for prioritizing the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, when the analysis unit adjusts the order of analysis based on the relevance of the information during analysis, it can use a generative AI to adjust the order of analysis.

[0048] The summarization unit can adjust the level of detail in the summary based on the importance of the information during summary generation. For example, the summarization unit can provide a detailed summary for highly important information. For example, the summarization unit can provide a concise summary for less important information. The summarization unit can dynamically adjust the level of detail in the summary according to the importance of the information. This allows important information to be summarized in detail by adjusting the level of detail in the summary based on the importance of the information. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, when the summarization unit adjusts the level of detail in the summary based on the importance of the information during summary generation, it can use a generation AI to adjust the level of detail in the summary.

[0049] The summarization unit can apply different summarization algorithms depending on the category of information when generating summaries. For example, the summarization unit can apply a natural language processing algorithm to text information. For example, the summarization unit can apply a statistical analysis algorithm to numerical data. For example, the summarization unit can apply an image analysis algorithm to image data. By applying different summarization algorithms depending on the category of information, appropriate summarization results can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the summarization unit applies different summarization algorithms depending on the category of information when generating summaries, it can use a generative AI to apply the summarization algorithm.

[0050] The summarization unit can determine the priority of summaries based on the submission date of the information when generating summaries. For example, the summarization unit prioritizes summarizing the most recent information. For example, the summarization unit can lower the priority of summarizing information that has been submitted earlier. For example, the summarization unit can dynamically adjust the priority of summaries according to the submission date. This allows for prioritizing summaries based on the submission date of the information, thereby prioritizing the most recent information. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or not. For example, when the summarization unit determines the priority of summaries based on the submission date of the information when generating summaries, it may use a generation AI to determine the priority of summaries.

[0051] The summarization unit can adjust the order of summaries based on the relevance of the information during summary generation. For example, the summarization unit prioritizes summarizing highly relevant information. For example, the summarization unit can postpone the summarization of less relevant information. For example, the summarization unit can dynamically adjust the order of summaries according to the relevance of the information. This allows for prioritizing the summarization of highly relevant information by adjusting the order of summaries based on the relevance of the information. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, when the summarization unit adjusts the order of summaries based on the relevance of the information during summary generation, it can use a generation AI to adjust the order of summaries.

[0052] The visualization unit can improve the accuracy of visualization by considering the interrelationships of information during visualization. For example, the visualization unit can illustrate the interrelationships of information and provide them in a visually easy-to-understand format. For example, the visualization unit can highlight important information by considering the relationships between the information. For example, the visualization unit can dynamically adjust the accuracy of visualization based on the interrelationships of information. This improves the accuracy of visualization by considering the interrelationships of information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when the visualization unit improves the accuracy of visualization by considering the interrelationships of information during visualization, it can use a generative AI to improve the accuracy of visualization.

[0053] The visualization unit can perform visualization while considering the attribute information of the information submitter. The visualization unit can adjust the visualization method based on, for example, the information submitter's job title or department. The visualization unit can adjust the level of detail of the visualization according to, for example, the information submitter's expertise. The visualization unit can dynamically adjust the visualization method based on, for example, the information submitter's attribute information. This allows for the provision of more appropriate visualization results by considering the attribute information of the information submitter. Some or all of the above processing in the visualization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit can use a generating AI when performing visualization while considering the attribute information of the information submitter.

[0054] The visualization unit can perform visualization while considering the geographical distribution of information. For example, the visualization unit can display the geographical distribution of information on a map and provide it in a visually easy-to-understand format. For example, the visualization unit can highlight important information based on geographical distribution. For example, the visualization unit can dynamically adjust the visualization method according to the geographical distribution. This allows for more appropriate visualization results by considering the geographical distribution of information. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the visualization unit performs visualization while considering the geographical distribution of information, it can use a generative AI.

[0055] The visualization unit can improve the accuracy of visualization by referring to relevant literature on the information during visualization. For example, the visualization unit can refer to relevant literature and illustrate the interrelationships of the information. For example, the visualization unit can highlight important information based on relevant literature. For example, the visualization unit can dynamically adjust the accuracy of visualization by referring to relevant literature. This improves the accuracy of visualization by referring to relevant literature on the information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when the visualization unit improves the accuracy of visualization by referring to relevant literature on the information during visualization, it can use a generative AI to improve the accuracy of visualization.

[0056] The information provider can improve the accuracy of its provision by considering the interrelationships of information at the time of provision. For example, the information provider can illustrate the interrelationships of information and provide it in a visually easy-to-understand format. For example, the information provider can highlight important information by considering the relationships between the information. For example, the information provider can dynamically adjust the accuracy of its provision based on the interrelationships of information. This improves the accuracy of the provision by considering the interrelationships of information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, when the information provider improves the accuracy of its provision by considering the interrelationships of information at the time of provision, it can use AI to improve the accuracy of its provision.

[0057] The information delivery unit can provide information while considering the attribute information of the information submitter. The information delivery unit can adjust the method of delivery based on, for example, the position or department of the information submitter. The information delivery unit can adjust the level of detail of delivery based on, for example, the expertise of the information submitter. The information delivery unit can dynamically adjust the method of delivery based on, for example, the attribute information of the information submitter. This allows for the provision of more appropriate information by considering the attribute information of the information submitter. Some or all of the above processing in the information delivery unit may be performed using, for example, AI, or not using AI. For example, the information delivery unit can use AI when providing information while considering the attribute information of the information submitter.

[0058] The information provider can provide information while considering its geographical distribution. For example, the provider can display the geographical distribution of information on a map and provide it in a visually easy-to-understand format. For example, the provider can highlight important information based on its geographical distribution. For example, the provider can dynamically adjust the method of provision according to the geographical distribution. This allows for the provision of more appropriate information by considering the geographical distribution of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can use AI when providing information while considering its geographical distribution.

[0059] The information provider can improve the accuracy of its provision by referring to relevant literature at the time of provision. For example, the information provider can refer to relevant literature and illustrate the interrelationships of the information. For example, the information provider can highlight important information based on relevant literature. For example, the information provider can dynamically adjust the accuracy of its provision by referring to relevant literature. This improves the accuracy of the provision by referring to relevant literature. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, when the information provider improves the accuracy of its provision by referring to relevant literature at the time of provision, it can use AI to improve the accuracy of its provision.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze a user's past information gathering history and select the optimal collection method. For example, it can prioritize crawling information sources that the user has frequently used in the past. It can also collect relevant information based on keywords the user has used in the past. Furthermore, it can optimize the information collected at specific time periods based on the user's past gathering history. In this way, the optimal collection method can be selected by analyzing the user's past information gathering history.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a concise analysis on less important information. Furthermore, it can dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information.

[0063] The summarization section can apply different summarization algorithms depending on the category of information. For example, natural language processing algorithms can be applied to text information, statistical analysis algorithms to numerical data, and image analysis algorithms to image data. This allows for the application of appropriate summarization results based on the information category.

[0064] The visualization unit can improve the accuracy of visualization by considering the interrelationships of information. For example, it can illustrate the interrelationships of information and provide them in a visually easy-to-understand format. It can also highlight important information by considering its relationships. Furthermore, it can dynamically adjust the accuracy of visualization based on the interrelationships of information. As a result, the accuracy of visualization is improved by considering the interrelationships of information.

[0065] The information provider can deliver information while considering its geographical distribution. For example, the geographical distribution of information can be displayed on a map and presented in a visually easy-to-understand format. Furthermore, important information can be highlighted based on its geographical distribution. In addition, the method of delivery can be dynamically adjusted according to the geographical distribution. This allows for the provision of more appropriate information by considering its geographical distribution.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit crawls the entire intranet to gather information. The collection unit gathers information from various sources, including documents, databases, emails, and chat logs. The collection unit can prioritize the collection of information related to specific projects or information containing specific keywords. It can also set the scope and target of the crawl to efficiently gather information. Step 2: The analysis unit analyzes the collected information using generative AI. The analysis unit understands the context of the collected information and extracts the key points. For example, it can analyze and summarize documents containing important information, such as meeting minutes or project progress reports. Step 3: The summarization unit uses generative AI to summarize the analyzed information. The summarization unit extracts the key points of the collected information and summarizes them into a short report. Generative AI can be used to extract the important parts of the information and perform the summarization. Step 4: The visualization unit uses generation AI to visually analyze the summarized information. The visualization unit illustrates the correlations and relationships between the information, providing it in a visually easy-to-understand format. For example, it can illustrate the progress of a project or the level of collaboration between departments. Step 5: The provider provides a link to the location of the original data for the information analyzed by the visualization unit. The provider presents a link to the location of the original data for the provided information, allowing users to view details. By clicking the link, users can access the original source and view detailed information.

[0068] (Example of form 2) An internal information discovery AI agent system according to an embodiment of the present invention is a system that efficiently searches, analyzes, summarizes, visualizes, and enables rapid access to internal company information. This internal information discovery AI agent system crawls the entire intranet and collects relevant information from various sources such as documents, databases, emails, and chat logs. Next, it uses generative AI to analyze the collected information in natural language and summarize it into a short report. Furthermore, it utilizes generative AI to perform a visual analysis of the information, highlighting important concepts and illustrating the correlations and relationships between each piece of information. Finally, it provides links to the location of the original data of the provided information, allowing users to check the details. For example, the internal information discovery AI agent system crawls the entire intranet and collects relevant information from various sources such as documents, databases, emails, and chat logs. In this process, the scope and target of the crawl are set to efficiently collect information. For example, it prioritizes collecting information related to a specific project or information containing specific keywords. Next, the internal information discovery AI agent system uses generative AI to analyze the collected information in natural language and summarize it into a short report. The generative AI understands the context of the collected information and extracts the key points. For example, by analyzing and summarizing documents containing important information, such as meeting minutes and project progress reports, the system enables quick information gathering. Furthermore, the internal information retrieval AI agent system utilizes generative AI to visually analyze information and highlight key concepts. The generative AI illustrates the correlations and relationships between information, providing it in a visually easy-to-understand format. For example, by illustrating project progress and the collaboration status between departments, it makes it easier to grasp the overall situation. Finally, the internal information retrieval AI agent system provides links to the source data of the information provided, allowing users to check details. By clicking the links, users can access the original sources and check detailed information. This enables quick access to necessary information and improves work efficiency. As a result, the internal information retrieval AI agent system speeds up access to internal information and improves work efficiency. It reduces wasted time on information searching, allowing users to focus on important tasks.Furthermore, it supports decision-making through consistent information provision. For example, by quickly understanding project progress and taking appropriate action, the project's success rate can be increased. As a result, the internal information retrieval AI agent system can expedite access to internal information and improve operational efficiency.

[0069] The internal information discovery AI agent system according to this embodiment comprises a collection unit, an analysis unit, a summarization unit, a visualization unit, and a provision unit. The collection unit crawls the entire intranet to collect information. The collection unit collects information from various sources, such as documents, databases, emails, and chat logs. The collection unit can, for example, prioritize the collection of information related to a specific project or information containing specific keywords. The collection unit can, for example, set the scope and target of the crawl to efficiently collect information. The analysis unit analyzes the collected information using generative AI. The analysis unit can, for example, understand the context of the collected information and extract key points. The analysis unit can, for example, analyze and summarize documents containing important information, such as meeting minutes or project progress reports. The analysis unit can, for example, use generative AI to extract important parts of the information and perform a summary. The summarization unit summarizes the information analyzed using generative AI. The summarization unit can, for example, extract the key points of the collected information and summarize them into a short report. The summarization unit can, for example, use generative AI to concisely summarize the key points of information. The summarization unit can, for example, use generative AI to extract important parts of information and summarize them. The visualization unit visually analyzes the information summarized using generative AI. The visualization unit can, for example, illustrate correlations and relationships between information and provide them in a visually easy-to-understand format. The visualization unit can, for example, illustrate project progress or the status of collaboration between departments. The visualization unit can, for example, use generative AI to extract important parts of information and visually analyze them. The provisioning unit provides links to the location of the original data of the information analyzed by the visualization unit. The provisioning unit can, for example, present links to the location of the original data of the provided information, allowing users to check details. The provisioning unit allows users to, for example, access the original information source and check detailed information by clicking on the links. The provisioning unit can, for example, use generative AI to provide links to the location of the original data of the information. As a result, the internal information discovery AI agent system according to this embodiment can efficiently search, analyze, summarize, visualize, and quickly access internal information.Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit crawls the entire intranet to collect information, it can use AI to set the scope and target of the crawl to efficiently collect information. Some or all of the processing described above in the analysis unit may be performed using generative AI, for example, or without generative AI. For example, when the analysis unit understands the context of the collected information and extracts the key points, it can use generative AI to extract the important parts of the information and summarize it. Some or all of the processing described above in the summarization unit may be performed using generative AI, for example, or without generative AI. For example, when the summarization unit extracts the key points of the collected information and summarizes it into a short report, it can use generative AI to extract the important parts of the information and summarize it. Some or all of the processing described above in the visualization unit may be performed using generative AI, for example, or without generative AI. For example, when the visualization unit illustrates the correlations and relationships of information and provides it in a visually easy-to-understand format, it can use generative AI to extract the important parts of the information and perform a visual analysis. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, when the provisioning unit presents the location of the original data of the provided information via a link so that details can be viewed, it may use AI to provide the location of the original data of the information via a link.

[0070] The data collection unit crawls the entire intranet to gather information. It collects information from diverse sources, such as documents, databases, emails, and chat logs. Specifically, it accesses various servers and databases within the intranet and automatically retrieves regularly updated information. By setting the scope and target of the crawl, the data collection unit can prioritize the collection of information related to specific projects or information containing specific keywords. For example, project names and related keywords can be pre-set, and relevant documents and emails can be collected preferentially based on these. Furthermore, the data collection unit can quickly obtain the latest information by adjusting the frequency and timing of crawls. For instance, information related to important projects can be crawled daily to collect the latest information, while other information can be crawled weekly, enabling efficient information gathering. In addition, the data collection unit centrally manages the collected information and stores it in a database. This allows the analysis and summarization units to quickly access the necessary information. The data collection unit can also use AI to dynamically set the scope and target of the crawl to efficiently collect information. For example, the AI ​​can analyze past collected data and automatically adjust the crawl scope based on specific keywords and patterns. This allows the data collection unit to efficiently gather the latest and most relevant information at all times.

[0071] The analysis unit analyzes collected information using generative AI. The analysis unit understands the context of the collected information and extracts key points. Specifically, it uses natural language processing techniques to analyze document content and extract important keywords and phrases. For example, it can analyze and summarize documents containing important information, such as meeting minutes or project progress reports. The generative AI analyzes context and meaning to understand the document content and extract important parts. For example, it can extract important decisions and action items from meeting minutes and important milestones and issues from project progress reports. The analysis unit can use generative AI to extract and summarize important parts of information. For example, the generative AI understands context and meaning to analyze document content and summarize important parts. This allows the analysis unit to quickly and accurately analyze collected information and extract important information. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of success for future projects. This allows the analysis unit to perform not only real-time information analysis but also long-term trend analysis and predictions.

[0072] The summarization unit uses generative AI to summarize the analyzed information. It extracts the key points of the collected information and summarizes them into a short report. Specifically, it uses generative AI to extract and summarize the important parts of the information. For example, it summarizes documents containing important information, such as meeting minutes or project progress reports, into short reports. The generative AI analyzes the context and meaning to understand the content of the document and extract the important parts. For example, it extracts important decisions and action items from meeting minutes, and important milestones and issues from project progress reports. The summarization unit can use generative AI to concisely summarize the key points of the information. For example, the generative AI analyzes the content of the document and understands the context and meaning to summarize the important parts. This allows the summarization unit to quickly and accurately summarize the collected information and extract the important information. Furthermore, the summarization unit can also utilize historical data and statistics to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of success for future projects. This allows the summarization unit to perform not only real-time information summarization but also long-term trend analysis and forecasting.

[0073] The visualization unit uses generative AI to visually analyze summarized information. The visualization unit illustrates the correlations and relationships between information, providing it in a visually easy-to-understand format. Specifically, it uses generative AI to extract important parts of the information and visually analyze them. For example, it can illustrate project progress or the collaboration status between departments. The generative AI analyzes data structure and patterns to analyze the correlations and relationships between information and provide them in a visually easy-to-understand format. For example, it can display project progress using Gantt charts and timelines, and collaboration status between departments using flowcharts and network diagrams. The visualization unit can use generative AI to extract important parts of information and visually analyze them. For example, the generative AI analyzes data structure and patterns to analyze the correlations and relationships between information and provide them in a visually easy-to-understand format. This allows the visualization unit to quickly and accurately visualize collected information and extract important information. Furthermore, the visualization unit can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of future project success. This allows the visualization unit to perform not only real-time information visualization but also long-term trend analysis and prediction.

[0074] The service provider provides links to the locations of the source data of the information analyzed by the visualization service provider. The service provider presents links to the locations of the source data of the provided information, allowing users to view details. Specifically, it uses generative AI to provide links to the locations of the source data of the information. For example, by clicking a link, the user can access the original source and view detailed information. The service provider can provide links to the locations of the source data of the information using generative AI. For example, the generative AI analyzes the location of the source data of the information and generates links. This allows the service provider to provide collected information quickly and accurately and extract important information. Furthermore, the service provider can also use historical data and statistical information to analyze long-term trends and patterns. For example, by analyzing past project data and identifying specific patterns and trends, it can predict the probability of success of future projects. This allows the service provider to not only provide real-time information but also perform long-term trend analysis and predictions.

[0075] The data collection unit can collect information from a variety of sources, such as documents, databases, emails, and chat logs. For example, the data collection unit can prioritize the collection of information related to a specific project or information containing specific keywords. The data collection unit can efficiently collect information by setting the scope and target of the crawl. This enables comprehensive information collection by collecting information from a variety of sources. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, when collecting information from a variety of sources, such as documents, databases, emails, and chat logs, the data collection unit can efficiently collect information using AI.

[0076] The analysis unit can understand the context of the collected information and extract key points. For example, the analysis unit can understand the context of the collected information and extract key points. The analysis unit can analyze and summarize documents containing important information, such as meeting minutes or project progress reports. The analysis unit can extract and summarize important parts of the information using, for example, generative AI. This allows for efficient grasp of important information by understanding the context of the information and extracting key points. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit understands the context of the collected information and extracts key points, it can use generative AI to extract and summarize important parts of the information.

[0077] The summarization unit can summarize information such as meeting minutes and project progress reports. For example, the summarization unit can summarize information such as meeting minutes and project progress reports. For example, the summarization unit can extract the key points of collected information and summarize them into a short report. For example, the summarization unit can use generative AI to concisely summarize the key points of the information. This allows for a concise understanding of important information by summarizing information such as meeting minutes and project progress reports. Some or all of the above-described processes in the summarization unit may be performed using, for example, generative AI, or without generative AI. For example, when the summarization unit extracts the key points of collected information and summarizes them into a short report, it can use generative AI to extract the important parts of the information and perform the summary.

[0078] The visualization unit can illustrate the correlations and relationships between information. For example, the visualization unit can illustrate the correlations and relationships between information. For example, the visualization unit can illustrate the progress of a project or the status of collaboration between departments. The visualization unit can extract important parts of information and analyze them visually, for example, using a generative AI. This makes it easier to understand the information by illustrating the correlations and relationships between information. Some or all of the above-described processing in the visualization unit may be performed using a generative AI, for example, or without a generative AI. For example, when the visualization unit illustrates the correlations and relationships between information and provides it in a visually easy-to-understand format, it can extract important parts of the information and analyze them visually using a generative AI.

[0079] The provider can provide a link to the location of the source data of the provided information. For example, the provider can provide a link to the location of the source data of the provided information, allowing users to view details. For example, the provider can allow users to access the original source and view detailed information by clicking the link. The provider can provide a link to the location of the source data of the information, for example, using generative AI. This allows for quick access to detailed information by providing a link to the location of the source data of the provided information. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, when providing a link to the location of the source data of the provided information, allowing users to view details, the provider can use AI to provide a link to the location of the source data of the information.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only important information. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection and collect detailed information. For example, if the user is in a hurry, the data collection unit can quickly collect the necessary information and provide it immediately. This allows information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, when the data collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions, it may use AI to adjust the timing of information collection.

[0081] The data collection unit can analyze the user's past information gathering history and select the optimal data collection method. For example, the data collection unit can prioritize crawling information sources that the user has frequently collected in the past. For example, the data collection unit can collect relevant information based on keywords the user has used in the past. For example, the data collection unit can optimize the information to be collected at specific time periods based on the user's past data collection history. This allows the optimal data collection method to be selected by analyzing the user's past data gathering history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit analyzes the user's past data gathering history and selects the optimal data collection method, it may use AI to select the optimal data collection method.

[0082] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. For example, the data collection unit can collect necessary information according to the progress of the user's projects. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to filter information when filtering based on the user's current projects and areas of interest during data collection.

[0083] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit can prioritize collecting information that is needed quickly. In this way, important information can be collected preferentially by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, when the data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions, it may use AI to determine the priority of information.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter and collect relevant information based on the user's geographical location. For example, if the user is on the move, the data collection unit can prioritize the collection of information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit prioritizes the collection of highly relevant information by considering the user's geographical location when collecting information, it may use AI to filter the information.

[0085] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can collect relevant information based on information shared by the user's social media followers and friends. For example, the data collection unit can collect relevant information by analyzing the user's social media activity history. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when the data collection unit analyzes the user's social media activity and collects relevant information during data collection, it may use AI to collect the information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, when the analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions, it can use generative AI to adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis, it can use a generative AI to adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply an image analysis algorithm to image data. By applying different analysis algorithms depending on the category of information, appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit applies different analysis algorithms depending on the category of information during analysis, it can use generative AI to apply the analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, when the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions, it can use a generative AI to adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the information submission date during analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for information that has been submitted earlier. For example, the analysis unit may dynamically adjust the priority of analysis according to the submission date. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the information submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when determining the priority of analysis based on the information submission date during analysis, the analysis unit may use a generative AI to determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the information during analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis according to the relevance of the information. This allows for prioritizing the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, when the analysis unit adjusts the order of analysis based on the relevance of the information during analysis, it can use a generative AI to adjust the order of analysis.

[0092] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is tense, the summarization unit can provide a simple and easy-to-read summary. For example, if the user is relaxed, the summarization unit can provide a detailed summary. For example, if the user is in a hurry, the summarization unit can provide a concise summary. This allows for the provision of a more appropriate summary by adjusting the way the summary is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using generative AI, or not. For example, when the summarization unit estimates the user's emotions and adjusts the way the summary is presented based on the estimated emotions, it may use generative AI to adjust the way the summary is presented.

[0093] The summarization unit can adjust the level of detail in the summary based on the importance of the information during summary generation. For example, the summarization unit can provide a detailed summary for highly important information. For example, the summarization unit can provide a concise summary for less important information. The summarization unit can dynamically adjust the level of detail in the summary according to the importance of the information. This allows important information to be summarized in detail by adjusting the level of detail in the summary based on the importance of the information. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, when the summarization unit adjusts the level of detail in the summary based on the importance of the information during summary generation, it can use a generation AI to adjust the level of detail in the summary.

[0094] The summarization unit can apply different summarization algorithms depending on the category of information when generating summaries. For example, the summarization unit can apply a natural language processing algorithm to text information. For example, the summarization unit can apply a statistical analysis algorithm to numerical data. For example, the summarization unit can apply an image analysis algorithm to image data. By applying different summarization algorithms depending on the category of information, appropriate summarization results can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the summarization unit applies different summarization algorithms depending on the category of information when generating summaries, it can use a generative AI to apply the summarization algorithm.

[0095] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a detailed summary. If the user is excited, the summarization unit can provide a visually stimulating summary. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using generative AI or not. For example, when the summarization unit estimates the user's emotions and adjusts the length of the summary based on the estimated emotions, it may use generative AI to adjust the length of the summary.

[0096] The summarization unit can determine the priority of summaries based on the submission date of the information when generating summaries. For example, the summarization unit prioritizes summarizing the most recent information. For example, the summarization unit can lower the priority of summarizing information that has been submitted earlier. For example, the summarization unit can dynamically adjust the priority of summaries according to the submission date. This allows for prioritizing summaries based on the submission date of the information, thereby prioritizing the most recent information. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or not. For example, when the summarization unit determines the priority of summaries based on the submission date of the information when generating summaries, it may use a generation AI to determine the priority of summaries.

[0097] The summarization unit can adjust the order of summaries based on the relevance of the information during summary generation. For example, the summarization unit prioritizes summarizing highly relevant information. For example, the summarization unit can postpone the summarization of less relevant information. For example, the summarization unit can dynamically adjust the order of summaries according to the relevance of the information. This allows for prioritizing the summarization of highly relevant information by adjusting the order of summaries based on the relevance of the information. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, when the summarization unit adjusts the order of summaries based on the relevance of the information during summary generation, it can use a generation AI to adjust the order of summaries.

[0098] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. By adjusting the display method of the visualization according to the user's emotions, more appropriate visualization results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the visualization unit may be performed using a generative AI, or not. For example, when the visualization unit estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions, it may use a generative AI to adjust the display method of the visualization.

[0099] The visualization unit can improve the accuracy of visualization by considering the interrelationships of information during visualization. For example, the visualization unit can illustrate the interrelationships of information and provide them in a visually easy-to-understand format. For example, the visualization unit can highlight important information by considering the relationships between the information. For example, the visualization unit can dynamically adjust the accuracy of visualization based on the interrelationships of information. This improves the accuracy of visualization by considering the interrelationships of information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when the visualization unit improves the accuracy of visualization by considering the interrelationships of information during visualization, it can use a generative AI to improve the accuracy of visualization.

[0100] The visualization unit can perform visualization while considering the attribute information of the information submitter. The visualization unit can adjust the visualization method based on, for example, the information submitter's job title or department. The visualization unit can adjust the level of detail of the visualization according to, for example, the information submitter's expertise. The visualization unit can dynamically adjust the visualization method based on, for example, the information submitter's attribute information. This allows for the provision of more appropriate visualization results by considering the attribute information of the information submitter. Some or all of the above processing in the visualization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit can use a generating AI when performing visualization while considering the attribute information of the information submitter.

[0101] The visualization unit can estimate the user's emotions and adjust the order in which the visualization results are displayed based on the estimated emotions. For example, if the user is nervous, the visualization unit can display important information first. If the user is relaxed, the visualization unit can display detailed information in a sequential manner. If the user is in a hurry, the visualization unit can display concise information first. By adjusting the order in which the visualization results are displayed according to the user's emotions, more appropriate visualization results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using a generative AI, or not. For example, when the visualization unit estimates the user's emotions and adjusts the order in which the visualization results are displayed based on the estimated emotions, it can use a generative AI to adjust the order of visualization.

[0102] The visualization unit can perform visualization while considering the geographical distribution of information. For example, the visualization unit can display the geographical distribution of information on a map and provide it in a visually easy-to-understand format. For example, the visualization unit can highlight important information based on geographical distribution. For example, the visualization unit can dynamically adjust the visualization method according to the geographical distribution. This allows for more appropriate visualization results by considering the geographical distribution of information. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the visualization unit performs visualization while considering the geographical distribution of information, it can use a generative AI.

[0103] The visualization unit can improve the accuracy of visualization by referring to relevant literature on the information during visualization. For example, the visualization unit can refer to relevant literature and illustrate the interrelationships of the information. For example, the visualization unit can highlight important information based on relevant literature. For example, the visualization unit can dynamically adjust the accuracy of visualization by referring to relevant literature. This improves the accuracy of visualization by referring to relevant literature on the information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when the visualization unit improves the accuracy of visualization by referring to relevant literature on the information during visualization, it can use a generative AI to improve the accuracy of visualization.

[0104] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing information of high importance. For example, if the user is relaxed, the service provider can prioritize providing detailed information. For example, if the user is in a hurry, the service provider can prioritize providing information that is needed quickly. In this way, important information can be prioritized by determining the priority of the information to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, when the service provider estimates the user's emotions and determines the priority of the information to be provided based on the estimated emotions, it may use AI to determine the priority of information.

[0105] The information provider can improve the accuracy of its provision by considering the interrelationships of information at the time of provision. For example, the information provider can illustrate the interrelationships of information and provide it in a visually easy-to-understand format. For example, the information provider can highlight important information by considering the relationships between the information. For example, the information provider can dynamically adjust the accuracy of its provision based on the interrelationships of information. This improves the accuracy of the provision by considering the interrelationships of information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, when the information provider improves the accuracy of its provision by considering the interrelationships of information at the time of provision, it can use AI to improve the accuracy of its provision.

[0106] The information delivery unit can provide information while considering the attribute information of the information submitter. The information delivery unit can adjust the method of delivery based on, for example, the position or department of the information submitter. The information delivery unit can adjust the level of detail of delivery based on, for example, the expertise of the information submitter. The information delivery unit can dynamically adjust the method of delivery based on, for example, the attribute information of the information submitter. This allows for the provision of more appropriate information by considering the attribute information of the information submitter. Some or all of the above processing in the information delivery unit may be performed using, for example, AI, or not using AI. For example, the information delivery unit can use AI when providing information while considering the attribute information of the information submitter.

[0107] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the way the information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, when the service provider estimates the user's emotions and adjusts the way the information is displayed based on the estimated emotions, it may use AI to adjust the way the information is displayed.

[0108] The information provider can provide information while considering its geographical distribution. For example, the provider can display the geographical distribution of information on a map and provide it in a visually easy-to-understand format. For example, the provider can highlight important information based on its geographical distribution. For example, the provider can dynamically adjust the method of provision according to the geographical distribution. This allows for the provision of more appropriate information by considering the geographical distribution of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can use AI when providing information while considering its geographical distribution.

[0109] The information provider can improve the accuracy of its provision by referring to relevant literature at the time of provision. For example, the information provider can refer to relevant literature and illustrate the interrelationships of the information. For example, the information provider can highlight important information based on relevant literature. For example, the information provider can dynamically adjust the accuracy of its provision by referring to relevant literature. This improves the accuracy of the provision by referring to relevant literature. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, when the information provider improves the accuracy of its provision by referring to relevant literature at the time of provision, it can use AI to improve the accuracy of its provision.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is stressed, the frequency of information collection can be reduced, and only essential information can be collected. Conversely, if the user is relaxed, the frequency of information collection can be increased, and more detailed information can be collected. Furthermore, if the user is in a hurry, the necessary information can be quickly collected and provided immediately. In this way, by adjusting the timing of information collection according to the user's emotions, information can be collected at a more appropriate time.

[0112] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide more appropriate analysis results.

[0113] The summarization function can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read summary. If the user is relaxed, it can provide a more detailed summary. Furthermore, if the user is in a hurry, it can provide a concise summary. By adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided.

[0114] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the visualization according to the user's emotions, more appropriate visualization results can be provided.

[0115] The information delivery unit can estimate the user's emotions and prioritize the information to be delivered based on those emotions. For example, if the user is stressed, it can prioritize providing high-priority information. If the user is relaxed, it can prioritize providing detailed information. Furthermore, if the user is in a hurry, it can prioritize providing information that is needed quickly. In this way, by prioritizing the information delivered according to the user's emotions, important information can be delivered preferentially.

[0116] The data collection unit can analyze a user's past information gathering history and select the optimal collection method. For example, it can prioritize crawling information sources that the user has frequently used in the past. It can also collect relevant information based on keywords the user has used in the past. Furthermore, it can optimize the information collected at specific time periods based on the user's past gathering history. In this way, the optimal collection method can be selected by analyzing the user's past information gathering history.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a concise analysis on less important information. Furthermore, it can dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information.

[0118] The summarization section can apply different summarization algorithms depending on the category of information. For example, natural language processing algorithms can be applied to text information, statistical analysis algorithms to numerical data, and image analysis algorithms to image data. This allows for the application of appropriate summarization results based on the information category.

[0119] The visualization unit can improve the accuracy of visualization by considering the interrelationships of information. For example, it can illustrate the interrelationships of information and provide them in a visually easy-to-understand format. It can also highlight important information by considering its relationships. Furthermore, it can dynamically adjust the accuracy of visualization based on the interrelationships of information. As a result, the accuracy of visualization is improved by considering the interrelationships of information.

[0120] The information provider can deliver information while considering its geographical distribution. For example, the geographical distribution of information can be displayed on a map and presented in a visually easy-to-understand format. Furthermore, important information can be highlighted based on its geographical distribution. In addition, the method of delivery can be dynamically adjusted according to the geographical distribution. This allows for the provision of more appropriate information by considering its geographical distribution.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit crawls the entire intranet to gather information. The collection unit gathers information from various sources, including documents, databases, emails, and chat logs. The collection unit can prioritize the collection of information related to specific projects or information containing specific keywords. It can also set the scope and target of the crawl to efficiently gather information. Step 2: The analysis unit analyzes the collected information using generative AI. The analysis unit understands the context of the collected information and extracts the key points. For example, it can analyze and summarize documents containing important information, such as meeting minutes or project progress reports. Step 3: The summarization unit uses generative AI to summarize the analyzed information. The summarization unit extracts the key points of the collected information and summarizes them into a short report. Generative AI can be used to extract the important parts of the information and perform the summarization. Step 4: The visualization unit uses generation AI to visually analyze the summarized information. The visualization unit illustrates the correlations and relationships between the information, providing it in a visually easy-to-understand format. For example, it can illustrate the progress of a project or the level of collaboration between departments. Step 5: The provider provides a link to the location of the original data for the information analyzed by the visualization unit. The provider presents a link to the location of the original data for the provided information, allowing users to view details. By clicking the link, users can access the original source and view detailed information.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, summarization unit, visualization unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects information by crawling the entire intranet. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using generating AI. The summarization unit is implemented by the control unit 46A of the smart device 14 and summarizes the analyzed information. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visually analyzes the summarized information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the location of the original data of the analyzed information via a link. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, summarization unit, visualization unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects information by crawling the entire intranet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using generating AI. The summarization unit is implemented by the control unit 46A of the smart glasses 214 and summarizes the analyzed information. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visually analyzes the summarized information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the location of the original data of the analyzed information via a link. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, summarization unit, visualization unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects information by crawling the entire intranet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using a generating AI. The summarization unit is implemented by the control unit 46A of the headset terminal 314 and summarizes the analyzed information. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visually analyzes the summarized information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the location of the original data of the analyzed information via a link. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, summarization unit, visualization unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects information by crawling the entire intranet. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using a generating AI. The summarization unit is implemented, for example, by the control unit 46A of the robot 414 and summarizes the analyzed information. The visualization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and visually analyzes the summarized information. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the location of the original data of the analyzed information via a link. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A collection unit that crawls the entire intranet to collect information, An analysis unit analyzes the information collected by the aforementioned collection unit using a generation AI, A summarization unit that summarizes the information analyzed by the aforementioned analysis unit, A visualization unit visually analyzes the information summarized by the summarization unit, The system includes a providing unit that provides a link to the location of the original data of the information analyzed by the visualization unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information from a variety of sources, including documents, databases, emails, and chat logs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Understand the context of the collected information and extract the key points. The system described in Appendix 1, characterized by the features described herein. (Note 4) The summary section above is, Summarize information such as meeting minutes and project progress reports. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned visualization unit, To illustrate the correlations and relationships between pieces of information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The source data of the provided information is shown via a link. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The summary section above is, When generating summaries, prioritize summaries based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The summary section above is, When generating summaries, adjust the order of the summaries based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, When visualizing information, consider the interrelationships between pieces of data to improve the accuracy of the visualization. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, When visualizing the data, the attribute information of the data submitter should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, It estimates the user's emotions and adjusts the order in which the visualization results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, When visualizing information, consider its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visualization unit, When visualizing information, refer to relevant literature to improve the accuracy of the visualization. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, we improve the accuracy of the information by considering the interrelationships between the pieces of information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, the attribute information of the information submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing information, the geographical distribution of the information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing information, we refer to relevant literature to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that crawls the entire intranet to collect information, An analysis unit analyzes the information collected by the aforementioned collection unit using generation AI, A summarization unit that summarizes the information analyzed by the aforementioned analysis unit, A visualization unit visually analyzes the information summarized by the summarization unit, The system includes a providing unit that provides a link to the location of the original data of the information analyzed by the visualization unit. A system characterized by the following features.

2. The aforementioned collection unit is Gather information from a variety of sources, including documents, databases, emails, and chat logs. The system according to feature 1.

3. The aforementioned analysis unit, Understand the context of the collected information and extract the key points. The system according to feature 1.

4. The summary section above is, Summarize information such as meeting minutes and project progress reports. The system according to feature 1.

5. The aforementioned visualization unit, To illustrate the correlations and relationships between pieces of information. The system according to feature 1.

6. The aforementioned supply unit is, The source data of the provided information is shown via a link. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

Images