system

Abstract

The system according to the embodiment aims to efficiently provide necessary information from among a vast amount of communication information. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an aggregation unit, and a provision unit. The collection unit collects information from various communication tools. The analysis unit analyzes the information collected by the collection unit. The aggregation unit aggregates the information analyzed by the analysis unit. The provision unit manages the information aggregated by the aggregation unit and provides users with information they need.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently find necessary information from a vast amount of communication information.

[0005] The system according to the embodiment aims to efficiently provide necessary information from among a vast amount of communication information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an aggregation unit, and a provision unit. The collection unit collects information from various communication tools. The analysis unit analyzes the information collected by the collection unit. The aggregation unit aggregates the information analyzed by the analysis unit. The provision unit manages the information aggregated by the aggregation unit and provides information required by the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide necessary information from a vast amount of communication information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information management system according to an embodiment of the present invention uses AI to extract, aggregate, and manage information and support business operations. This information management system extracts information from various communication tools, analyzes the extracted information, aggregates relevant information, and manages the aggregated information, allowing users to quickly search for the information they need. Furthermore, it automatically provides necessary information to support business operations. For example, to understand the progress of a project, all related communication information can be centrally managed and the necessary information can be quickly obtained. First, AI extracts information from various communication tools, such as email content, chat messages, and web form input data. Next, AI analyzes the extracted information and aggregates relevant information. For example, emails and chat messages related to the same project can be grouped together. Furthermore, AI manages the aggregated information, allowing users to quickly search for the information they need. For example, by entering a specific keyword, related emails and chat messages can be instantly displayed. This saves users the trouble of searching for the necessary information from a vast amount of information. Furthermore, AI automatically provides necessary information to support business operations. For example, by automatically providing relevant emails and chat messages before a meeting, users can efficiently prepare. This system improves work efficiency and allows users to significantly reduce the time they spend searching for and organizing information. For example, to understand the progress of a project, all related communication information can be managed centrally and the necessary information can be quickly obtained. This information management system allows users to quickly obtain the information they need, improving work efficiency.

[0029] An information management system according to an embodiment includes a collection unit, an analysis unit, an aggregation unit, and a provision unit. The collection unit collects information from various communication tools. For example, the collection unit can collect email content, chat messages, web form input data, and the like. For example, the collection unit can acquire email content from a mail server, acquire messages from a chat application, and collect web form input data. The collection unit can also use AI to adjust the type of information to be collected and the frequency of collection. For example, the collection unit can use AI to select the type of information to be collected based on the user's work and adjust the frequency of collection. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the content of the collected information using natural language processing technology and identify relevant information. For example, the analysis unit can analyze the content of collected emails and extract related keywords and topics. The analysis unit can also use AI to improve the accuracy of the analysis. For example, the analysis unit can use AI to analyze the content of the collected information in detail and identify highly relevant information. The aggregation unit aggregates the information analyzed by the analysis unit. The aggregating unit can, for example, aggregate information related to the same project into one group. The aggregating unit can, for example, aggregate related emails or chat messages identified by the analyzing unit into one group. The aggregating unit can also use AI to improve the accuracy of the aggregation. For example, the aggregating unit can use AI to analyze the interrelationships between information and improve the accuracy of the aggregation. The providing unit manages the information aggregated by the aggregating unit and provides the user with information needed. The providing unit can, for example, provide the user with related information based on specific keywords. For example, the providing unit can instantly display related emails or chat messages based on keywords entered by the user. The providing unit can also use AI to improve the accuracy of the information it provides. For example, the providing unit can use AI to provide optimal information based on the user's work content and interests.As a result, the information management system according to the embodiment allows the user to quickly obtain the information he or she needs, thereby improving the efficiency of work.

[0030] The collection unit can collect email content, chat messages, and web form input data. The collection unit, for example, acquires email content from a mail server. For example, the collection unit can acquire information such as email subject lines, email bodies, and attachments from a mail server using the IMAP or POP3 protocol. The collection unit can also acquire messages from a chat application. For example, the collection unit can acquire information such as chat text messages, images, and links using the chat application's API. The collection unit can also collect web form input data. For example, the collection unit can acquire web form input data through an HTTP request and collect information such as text input, selection options, and file uploads. This allows the collection unit to collect information from a variety of communication tools. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the type of information to be collected and the frequency of collection using AI.

[0031] The analysis unit can analyze the collected information and identify relevant information. The analysis unit can analyze the content of the collected information using, for example, natural language processing technology. For example, the analysis unit can analyze the content of collected emails and chat messages using a text analysis algorithm and extract related keywords and topics. The analysis unit can also use AI to improve the accuracy of the analysis. For example, the analysis unit can use AI to analyze the content of the collected information in detail and identify highly relevant information. Furthermore, the analysis unit can classify information based on common keywords or related topics to evaluate the relevance of the information. For example, the analysis unit can group information with common keywords and identify highly relevant information. This allows the analysis unit to efficiently analyze the collected information and identify relevant information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the content of the collected information in detail and identify highly relevant information.

[0032] The aggregating unit can aggregate information related to the same project into one group. For example, the aggregating unit aggregates related information identified by the analyzing unit into one group. For example, the aggregating unit can aggregate emails and chat messages related to the same project into one group. The aggregating unit can also improve the accuracy of the aggregation using AI. For example, the aggregating unit can analyze the interrelationships of information using AI to improve the accuracy of the aggregation. Furthermore, the aggregating unit can classify information based on project names or project IDs and efficiently aggregate related information. For example, the aggregating unit can group information having project names or project IDs and aggregate information related to the same project into one group. This allows the aggregating unit to efficiently manage information related to projects. Some or all of the above-described processing in the aggregating unit may be performed using AI, for example, or may be performed without using AI. For example, the aggregating unit can analyze the interrelationships of information using AI to improve the accuracy of the aggregation.

[0033] The providing unit can provide a user with related information based on specific keywords. The providing unit can provide related information based on, for example, keywords entered by a user. For example, the providing unit can instantly display related emails or chat messages based on keywords entered by a user. The providing unit can also use AI to improve the accuracy of the information provided. For example, the providing unit can use AI to provide optimal information based on the user's business content and interests. Furthermore, the providing unit can classify information based on frequently used keywords or keywords specified by a user and efficiently provide related information. For example, the providing unit can preferentially display information containing frequently used keywords and filter information based on keywords specified by a user. This allows the providing unit to quickly obtain the information the user needs. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can use AI to provide optimal information based on the user's business content and interests.

[0034] The providing unit can automatically compile and provide relevant information before a meeting. The providing unit, for example, automatically compiles and provides relevant information before a meeting. For example, the providing unit can automatically compile and provide relevant emails and chat messages one hour before the meeting or the day before the meeting. The providing unit can also use AI to improve the accuracy of the information to be provided. For example, the providing unit can use AI to provide optimal information based on the content and purpose of the meeting. Furthermore, the providing unit can automate the collection and analysis of information to efficiently provide relevant information before a meeting. For example, the providing unit can automatically collect, analyze, and provide relevant information before a meeting. This allows the providing unit to efficiently prepare for the meeting. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can use AI to provide optimal information based on the content and purpose of the meeting.

[0035] The collection unit can analyze the user's past communication history and select the optimal collection method. The collection unit can, for example, use data mining technology to analyze the user's past communication history. For example, the collection unit can collect the user's past email and chat history and analyze it using a data mining algorithm. The collection unit can also use AI to analyze the past communication history and select the optimal collection method. For example, the collection unit can use AI to identify communication tools that the user has frequently used in the past and select a collection method based on that. Furthermore, the collection unit can determine the priority of information to be collected based on information that the user has previously determined to be important. For example, the collection unit can prioritize collecting emails and chat messages that the user has previously determined to be important. This allows the collection unit to optimally collect information based on the user's past history. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can use AI to analyze the user's past communication history and select the optimal collection method.

[0036] When collecting information, the collection unit may filter the information based on the user's current project or area of ​​interest. For example, the collection unit may refer to a project management tool or a database of areas of interest to identify the user's current project or area of ​​interest. For example, the collection unit may obtain a project name or project ID from the project management tool and filter the information based on the obtained project name or project ID. The collection unit may also filter relevant information based on the user's area of ​​interest. For example, the collection unit may refer to a database of keywords or areas of interest set by the user and filter the information based on the obtained keywords or areas of interest. Furthermore, the collection unit may use AI to identify the user's current project or area of ​​interest and collect optimal information. For example, the collection unit may use AI to analyze the user's project management tool or area of ​​interest database and filter relevant information. This allows the collection unit to collect highly relevant information based on the user's interests. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit may use AI to identify the user's current project or area of ​​interest and collect optimal information.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses GPS data or an IP address to acquire the user's geographical location information. For example, the collection unit can acquire GPS data from the user's smartphone or device and collect relevant information based on the data. The collection unit can also analyze the user's IP address to identify the geographical location information. Furthermore, the collection unit can use AI to analyze the user's geographical location information and collect optimal information. For example, the collection unit can use AI to prioritize collecting information related to the user's current location. This allows the collection unit to collect highly relevant information based on the user's location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can use AI to analyze the user's geographical location information and collect optimal information.

[0038] When collecting information, the collection unit can analyze the user's social media activity and collect related information. The collection unit, for example, uses a social media API to analyze the user's social media activity. For example, the collection unit can use the social media API to obtain information such as the user's posted content, the number of likes, and the number of followers, and collect related information based on the information. The collection unit can also use AI to analyze the user's social media activity and collect optimal information. For example, the collection unit can use AI to collect relevant information based on information shared by the user on social media. Furthermore, the collection unit can analyze the user's social media activity history and collect relevant information. For example, the collection unit can collect relevant information based on information about accounts the user follows. This allows the collection unit to collect highly relevant information based on the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can use AI to analyze the user's social media activity and collect optimal information.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, uses impact and relevance as criteria to evaluate the importance of the information. For example, the analysis unit can evaluate the impact of the information and perform a detailed analysis of information with high importance. The analysis unit can also evaluate the relevance of the information and perform a simplified analysis of information with low importance. Furthermore, the analysis unit can use AI to evaluate the importance of the information and adjust the level of detail of the analysis. For example, the analysis unit can use AI to evaluate the impact and relevance of the information and determine the priority of the analysis according to the importance. This allows the analysis unit to perform an optimal analysis according to the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the importance of the information and adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit uses criteria such as technical information or business information to classify the category of information. For example, the analysis unit can apply a natural language processing algorithm to technical information and a statistical analysis algorithm to business information. The analysis unit can also apply an image analysis algorithm to image information. For example, the analysis unit can use image recognition technology to analyze image information. Furthermore, the analysis unit can use AI to select the optimal analysis algorithm depending on the category of information. For example, the analysis unit can use AI to classify the category of information and apply the optimal analysis algorithm accordingly. This allows the analysis unit to perform the optimal analysis depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to classify the category of information and apply the optimal analysis algorithm accordingly.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit uses the submission date or time as a criterion to evaluate the time of information submission. For example, the analysis unit can prioritize analysis of the most recent information and lower the priority of information submitted earlier. The analysis unit can also use AI to evaluate the time of information submission and determine the priority of analysis. For example, the analysis unit can use AI to adjust the analysis schedule based on the time of submission. This allows the analysis unit to perform optimal analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the time of information submission and determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. For example, the analysis unit uses common keywords or related topics as a criterion to evaluate the relevance of information. For example, the analysis unit can prioritize the analysis of information with common keywords and postpone the analysis order of less relevant information. The analysis unit can also use AI to evaluate the relevance of information and adjust the order of analysis. For example, the analysis unit can use AI to adjust the analysis schedule based on the relevance of information. This allows the analysis unit to perform analysis in an optimal order based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the relevance of information and adjust the order of analysis.

[0043] The aggregator can improve the accuracy of aggregation by taking into account the interrelationships between information during aggregation. For example, the aggregator uses common keywords or related topics as criteria to evaluate the interrelationships between information. For example, the aggregator can group information with common keywords and aggregate the related information. The aggregator can also use AI to evaluate the interrelationships between information and improve the accuracy of aggregation. For example, the aggregator can use AI to analyze the interrelationships between information and determine aggregation priorities. This allows the aggregator to improve the accuracy of aggregation by taking into account the interrelationships between information. Some or all of the above-described processing in the aggregator may be performed using AI, or may be performed without using AI. For example, the aggregator can use AI to evaluate the interrelationships between information and improve the accuracy of aggregation.

[0044] The aggregating unit can perform aggregation while taking into account the attribute information of the information submitter. For example, the aggregating unit uses job title or field of expertise as criteria to evaluate the attribute information of the information submitter. For example, the aggregating unit can aggregate information based on the submitter's job title. The aggregating unit can also aggregate information based on the submitter's field of expertise. Furthermore, the aggregating unit can use AI to evaluate the attribute information of the information submitter and improve the accuracy of the aggregation. For example, the aggregating unit can use AI to aggregate information based on the submitter's past performance. This allows the aggregating unit to improve the accuracy of the aggregation by taking into account the attribute information of the information submitter. Some or all of the above-described processing in the aggregating unit may be performed using AI, for example, or may be performed without using AI. For example, the aggregating unit can use AI to evaluate the attribute information of the information submitter and improve the accuracy of the aggregation.

[0045] The aggregating unit can perform aggregation taking into account the geographical distribution of information. For example, the aggregating unit uses regional information or country-specific information as a criterion to evaluate the geographical distribution of information. For example, the aggregating unit can prioritize aggregating information that is geographically close. The aggregating unit can also group information based on geographical distribution to improve the accuracy of aggregation. Furthermore, the aggregating unit can use AI to evaluate the geographical distribution of information and determine the priority of aggregation. For example, the aggregating unit can use AI to group information taking into account geographical distribution to improve the accuracy of aggregation. This allows the aggregating unit to improve the accuracy of aggregation taking into account the geographical distribution of information. Some or all of the above-described processing in the aggregating unit may be performed using AI, or may be performed without using AI. For example, the aggregating unit can use AI to evaluate the geographical distribution of information to improve the accuracy of aggregation.

[0046] The aggregating unit can improve the accuracy of aggregation by referring to literature related to the information during aggregation. For example, the aggregating unit uses cited literature and reference literature as a basis for referring to literature related to the information. For example, the aggregating unit can confirm the reliability of the information by referring to the related literature. The aggregating unit can also analyze interrelationships of information based on the related literature and improve the accuracy of aggregation. Furthermore, the aggregating unit can use AI to refer to literature related to the information and improve the accuracy of aggregation. For example, the aggregating unit can use AI to analyze interrelationships of information based on the related literature and determine aggregation priorities. This allows the aggregating unit to improve the accuracy of aggregation by referring to literature related to the information. Some or all of the above-mentioned processing in the aggregating unit may be performed using AI, or may be performed without using AI. For example, the aggregating unit can use AI to refer to literature related to the information and improve the accuracy of aggregation.

[0047] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the information when providing the information. For example, the providing unit uses common keywords or related topics as criteria to evaluate the interrelationships between the information. For example, the providing unit can group information having common keywords and provide the related information. The providing unit can also use AI to evaluate the interrelationships between the information and improve the accuracy of the information provided. For example, the providing unit can use AI to analyze the interrelationships between the information and determine the priority of the information to be provided. This allows the providing unit to improve the accuracy of the information provided by taking into account the interrelationships between the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can use AI to evaluate the interrelationships between the information and improve the accuracy of the information provided.

[0048] The providing unit can provide the information while taking into consideration the attribute information of the person submitting the information. The providing unit, for example, uses job position or field of expertise as criteria to evaluate the attribute information of the person submitting the information. For example, the providing unit can provide information based on the job title of the person submitting the information. The providing unit can also provide information based on the person's field of expertise. Furthermore, the providing unit can use AI to evaluate the attribute information of the person submitting the information and improve the accuracy of the information provided. For example, the providing unit can use AI to provide information based on the person's past performance. This allows the providing unit to improve the accuracy of the information provided by taking into consideration the attribute information of the person submitting the information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can use AI to evaluate the attribute information of the person submitting the information and improve the accuracy of the information provided.

[0049] The providing unit may provide the information taking into consideration the geographical distribution of the information. For example, the providing unit may use regional information or country-specific information as a criterion to evaluate the geographical distribution of the information. For example, the providing unit may preferentially provide geographically close information. The providing unit may also group the information based on the geographical distribution to improve the accuracy of the provision. Furthermore, the providing unit may use AI to evaluate the geographical distribution of the information and determine the priority of the provision. For example, the providing unit may use AI to group the information taking into consideration the geographical distribution to improve the accuracy of the provision. In this way, the providing unit may improve the accuracy of the provision taking into consideration the geographical distribution of the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may use AI to evaluate the geographical distribution of the information to improve the accuracy of the provision.

[0050] The providing unit can improve the accuracy of the information provided by referring to literature related to the information when providing the information. For example, the providing unit uses cited literature and reference literature as a basis for referring to literature related to the information. For example, the providing unit can confirm the reliability of the information by referring to the related literature. The providing unit can also analyze interrelationships of the information based on the related literature and improve the accuracy of the information provided. Furthermore, the providing unit can use AI to refer to literature related to the information and improve the accuracy of the information provided. For example, the providing unit can use AI to analyze interrelationships of the information based on the related literature and determine the priority of the information provided. This allows the providing unit to improve the accuracy of the information provided by referring to literature related to the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can use AI to refer to literature related to the information and improve the accuracy of the information provided.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The information management system may further include a notification unit. The notification unit can notify the user of relevant information in real time based on specific conditions set by the user. For example, if the user receives a new email or chat message related to a specific project, the notification unit can immediately notify the user. The notification unit can also notify the user of important information at an appropriate time based on the user's schedule. For example, by notifying the user of relevant information just before a meeting, the user can efficiently prepare. Furthermore, the notification unit can adjust the importance of notifications based on the user's priority. For example, highly urgent information can be notified immediately, and less urgent information can be notified later. This allows the user to perform their work efficiently without missing important information.

[0053] The collection unit can analyze the user's past behavioral history and select the optimal collection method. For example, the collection unit can identify information sources that the user has frequently accessed in the past and prioritize information collection based on that. The collection unit can also determine the priority of information to be collected based on information that the user has previously deemed important. For example, the collection unit can prioritize collection of emails and chat messages that the user has previously deemed important. Furthermore, the collection unit can analyze the user's past behavioral patterns and optimize the timing of collection. For example, if the user works intensively during a specific time period, the collection unit can collect information according to that time period. This allows the collection unit to optimally collect information based on the user's past behavioral history.

[0054] The aggregation unit can improve the accuracy of aggregation by taking into account the interrelationships between information. For example, the aggregation unit can group information based on common keywords or related topics and aggregate related information. The aggregation unit can also use AI to evaluate the interrelationships between information and determine aggregation priorities. This allows the aggregation unit to improve the accuracy of aggregation by taking into account the interrelationships between information. Furthermore, the aggregation unit can also aggregate information by taking into account attribute information of the person who submitted the information. For example, aggregating information based on the submitter's job title or field of expertise enables more accurate information management. This allows the aggregation unit to efficiently aggregate information by taking into account the interrelationships between information and the submitter's attribute information.

[0055] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the providing unit can prioritize providing nearby events and news based on the user's current location. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to the user's travel destination. Furthermore, the providing unit can provide region-specific information based on the user's location information. For example, if the user is in a specific region, business information and local news related to that region can be provided. This allows the providing unit to provide more relevant information based on the user's geographical location information.

[0056] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance based on the impact and relevance of the information. Also, the analysis unit can perform a simplified analysis of information with low importance. Furthermore, the analysis unit can use AI to evaluate the importance of the information and determine the priority of the analysis. This allows the analysis unit to perform optimal analysis based on the importance of the information. For example, the analysis unit can use AI to evaluate the impact and relevance of the information and determine the priority of the analysis based on the importance. This allows the analysis unit to perform optimal analysis based on the importance of the information.

[0057] The providing unit can improve the accuracy of the information provided by referring to related literature when providing the information. For example, the providing unit can confirm the reliability of the information by referring to related literature. The providing unit can also analyze the interrelationships of the information based on the related literature and improve the accuracy of the information provided. Furthermore, the providing unit can also use AI to improve the accuracy of the information provided by referring to related literature. For example, the providing unit can use AI to analyze the interrelationships of the information based on the related literature and determine the priority of the information provided. This allows the providing unit to improve the accuracy of the information provided by referring to related literature.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects information from various communication tools. For example, the collection unit can collect email content, chat messages, and web form input data. The collection unit obtains email content from the mail server, obtains messages from the chat application, and collects web form input data. The collection unit can also use AI to adjust the type of information to be collected and the frequency of collection. For example, it can select the type of information to be collected based on the user's work and adjust the frequency of collection. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to analyze the content of the collected information and identify relevant information. For example, it analyzes the content of collected emails and extracts related keywords and topics. The analysis unit can also use AI to improve the accuracy of the analysis. For example, it analyzes the content of the collected information in detail and identifies highly relevant information. Step 3: The aggregation unit aggregates the information analyzed by the analysis unit. The aggregation unit groups together information related to the same project. For example, it groups together related emails and chat messages identified by the analysis unit. The aggregation unit can also use AI to improve the accuracy of the aggregation. For example, it can analyze the interrelationships between information to improve the accuracy of the aggregation. Step 4: The providing unit manages the information aggregated by the aggregating unit and provides the user with the information they need. The providing unit provides the user with relevant information based on specific keywords. For example, based on keywords entered by the user, it can instantly display related emails or chat messages. The providing unit can also use AI to improve the accuracy of the information it provides. For example, it can provide the most appropriate information based on the user's business activities and interests.

[0060] (Example 2) An information management system according to an embodiment of the present invention uses AI to extract, aggregate, and manage information and support business operations. This information management system extracts information from various communication tools, analyzes the extracted information, aggregates relevant information, and manages the aggregated information, allowing users to quickly search for the information they need. Furthermore, it automatically provides necessary information to support business operations. For example, to understand the progress of a project, all related communication information can be centrally managed and the necessary information can be quickly obtained. First, AI extracts information from various communication tools, such as email content, chat messages, and web form input data. Next, AI analyzes the extracted information and aggregates relevant information. For example, emails and chat messages related to the same project can be grouped together. Furthermore, AI manages the aggregated information, allowing users to quickly search for the information they need. For example, by entering a specific keyword, related emails and chat messages can be instantly displayed. This saves users the trouble of searching for the necessary information from a vast amount of information. Furthermore, AI automatically provides necessary information to support business operations. For example, by automatically providing relevant emails and chat messages before a meeting, users can efficiently prepare. This system improves work efficiency and allows users to significantly reduce the time they spend searching for and organizing information. For example, to understand the progress of a project, all related communication information can be managed centrally and the necessary information can be quickly obtained. This information management system allows users to quickly obtain the information they need, improving work efficiency.

[0061] An information management system according to an embodiment includes a collection unit, an analysis unit, an aggregation unit, and a provision unit. The collection unit collects information from various communication tools. For example, the collection unit can collect email content, chat messages, web form input data, and the like. For example, the collection unit can acquire email content from a mail server, acquire messages from a chat application, and collect web form input data. The collection unit can also use AI to adjust the type of information to be collected and the frequency of collection. For example, the collection unit can use AI to select the type of information to be collected based on the user's work and adjust the frequency of collection. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the content of the collected information using natural language processing technology and identify relevant information. For example, the analysis unit can analyze the content of collected emails and extract related keywords and topics. The analysis unit can also use AI to improve the accuracy of the analysis. For example, the analysis unit can use AI to analyze the content of the collected information in detail and identify highly relevant information. The aggregation unit aggregates the information analyzed by the analysis unit. The aggregating unit can, for example, aggregate information related to the same project into one group. The aggregating unit can, for example, aggregate related emails or chat messages identified by the analyzing unit into one group. The aggregating unit can also use AI to improve the accuracy of the aggregation. For example, the aggregating unit can use AI to analyze the interrelationships between information and improve the accuracy of the aggregation. The providing unit manages the information aggregated by the aggregating unit and provides the user with information needed. The providing unit can, for example, provide the user with related information based on specific keywords. For example, the providing unit can instantly display related emails or chat messages based on keywords entered by the user. The providing unit can also use AI to improve the accuracy of the information it provides. For example, the providing unit can use AI to provide optimal information based on the user's work content and interests.As a result, the information management system according to the embodiment allows the user to quickly obtain the information he or she needs, thereby improving the efficiency of work.

[0062] The collection unit can collect email content, chat messages, and web form input data. The collection unit, for example, acquires email content from a mail server. For example, the collection unit can acquire information such as email subject lines, email bodies, and attachments from a mail server using the IMAP or POP3 protocol. The collection unit can also acquire messages from a chat application. For example, the collection unit can acquire information such as chat text messages, images, and links using the chat application's API. The collection unit can also collect web form input data. For example, the collection unit can acquire web form input data through an HTTP request and collect information such as text input, selection options, and file uploads. This allows the collection unit to collect information from a variety of communication tools. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the type of information to be collected and the frequency of collection using AI.

[0063] The analysis unit can analyze the collected information and identify relevant information. The analysis unit can analyze the content of the collected information using, for example, natural language processing technology. For example, the analysis unit can analyze the content of collected emails and chat messages using a text analysis algorithm and extract related keywords and topics. The analysis unit can also use AI to improve the accuracy of the analysis. For example, the analysis unit can use AI to analyze the content of the collected information in detail and identify highly relevant information. Furthermore, the analysis unit can classify information based on common keywords or related topics to evaluate the relevance of the information. For example, the analysis unit can group information with common keywords and identify highly relevant information. This allows the analysis unit to efficiently analyze the collected information and identify relevant information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the content of the collected information in detail and identify highly relevant information.

[0064] The aggregating unit can aggregate information related to the same project into one group. For example, the aggregating unit aggregates related information identified by the analyzing unit into one group. For example, the aggregating unit can aggregate emails and chat messages related to the same project into one group. The aggregating unit can also improve the accuracy of the aggregation using AI. For example, the aggregating unit can analyze the interrelationships of information using AI to improve the accuracy of the aggregation. Furthermore, the aggregating unit can classify information based on project names or project IDs and efficiently aggregate related information. For example, the aggregating unit can group information having project names or project IDs and aggregate information related to the same project into one group. This allows the aggregating unit to efficiently manage information related to projects. Some or all of the above-described processing in the aggregating unit may be performed using AI, for example, or may be performed without using AI. For example, the aggregating unit can analyze the interrelationships of information using AI to improve the accuracy of the aggregation.

[0065] The providing unit can provide a user with related information based on specific keywords. The providing unit can provide related information based on, for example, keywords entered by a user. For example, the providing unit can instantly display related emails or chat messages based on keywords entered by a user. The providing unit can also use AI to improve the accuracy of the information provided. For example, the providing unit can use AI to provide optimal information based on the user's business content and interests. Furthermore, the providing unit can classify information based on frequently used keywords or keywords specified by a user and efficiently provide related information. For example, the providing unit can preferentially display information containing frequently used keywords and filter information based on keywords specified by a user. This allows the providing unit to quickly obtain the information the user needs. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can use AI to provide optimal information based on the user's business content and interests.

[0066] The providing unit can automatically compile and provide relevant information before a meeting. The providing unit, for example, automatically compiles and provides relevant information before a meeting. For example, the providing unit can automatically compile and provide relevant emails and chat messages one hour before the meeting or the day before the meeting. The providing unit can also use AI to improve the accuracy of the information to be provided. For example, the providing unit can use AI to provide optimal information based on the content and purpose of the meeting. Furthermore, the providing unit can automate the collection and analysis of information to efficiently provide relevant information before a meeting. For example, the providing unit can automatically collect, analyze, and provide relevant information before a meeting. This allows the providing unit to efficiently prepare for the meeting. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can use AI to provide optimal information based on the content and purpose of the meeting.

[0067] The collection unit can estimate the user's emotions and select a collection method based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions using a camera and estimate the user's emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can record the user's voice using a microphone and estimate the user's emotions using a voice analysis algorithm. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit can analyze text entered by the user and estimate the user's emotions using a text analysis algorithm. This allows the collection unit to select an optimal collection method based on the user's emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to collect information when the user is relaxed. Also, if the user is concentrating, the collection unit can advance the collection timing to collect information in real time. Furthermore, if the user is tired, the collection unit can adjust the collection timing to collect information while the user is resting. This allows the collection unit to collect information at the optimal timing depending on the user's emotions. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can use AI to estimate the user's emotions and select the optimal collection method.

[0068] The collection unit can analyze the user's past communication history and select the optimal collection method. The collection unit can, for example, use data mining technology to analyze the user's past communication history. For example, the collection unit can collect the user's past email and chat history and analyze it using a data mining algorithm. The collection unit can also use AI to analyze the past communication history and select the optimal collection method. For example, the collection unit can use AI to identify communication tools that the user has frequently used in the past and select a collection method based on that. Furthermore, the collection unit can determine the priority of information to be collected based on information that the user has previously determined to be important. For example, the collection unit can prioritize collecting emails and chat messages that the user has previously determined to be important. This allows the collection unit to optimally collect information based on the user's past history. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can use AI to analyze the user's past communication history and select the optimal collection method.

[0069] When collecting information, the collection unit may filter the information based on the user's current project or area of ​​interest. For example, the collection unit may refer to a project management tool or a database of areas of interest to identify the user's current project or area of ​​interest. For example, the collection unit may obtain a project name or project ID from the project management tool and filter the information based on the obtained project name or project ID. The collection unit may also filter relevant information based on the user's area of ​​interest. For example, the collection unit may refer to a database of keywords or areas of interest set by the user and filter the information based on the obtained keywords or areas of interest. Furthermore, the collection unit may use AI to identify the user's current project or area of ​​interest and collect optimal information. For example, the collection unit may use AI to analyze the user's project management tool or area of ​​interest database and filter relevant information. This allows the collection unit to collect highly relevant information based on the user's interests. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit may use AI to identify the user's current project or area of ​​interest and collect optimal information.

[0070] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions using a camera and estimate the user's emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can record the user's voice using a microphone and estimate the user's emotions using a voice analysis algorithm. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit can analyze text entered by the user and estimate the user's emotions using a text analysis algorithm. This allows the collection unit to determine the priority of information to be collected based on the user's emotions. For example, the collection unit can prioritize collecting important information when the user is stressed. Furthermore, the collection unit can prioritize collecting detailed information when the user is relaxed. Furthermore, the collection unit can prioritize collecting information that can be collected quickly when the user is in a hurry. This allows the collection unit to prioritize collecting important information according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use AI to estimate the user's emotions and determine the priority of information to be collected.

[0071] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses GPS data or an IP address to acquire the user's geographical location information. For example, the collection unit can acquire GPS data from the user's smartphone or device and collect relevant information based on the data. The collection unit can also analyze the user's IP address to identify the geographical location information. Furthermore, the collection unit can use AI to analyze the user's geographical location information and collect optimal information. For example, the collection unit can use AI to prioritize collecting information related to the user's current location. This allows the collection unit to collect highly relevant information based on the user's location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can use AI to analyze the user's geographical location information and collect optimal information.

[0072] When collecting information, the collection unit can analyze the user's social media activity and collect related information. The collection unit, for example, uses a social media API to analyze the user's social media activity. For example, the collection unit can use the social media API to obtain information such as the user's posted content, the number of likes, and the number of followers, and collect related information based on the information. The collection unit can also use AI to analyze the user's social media activity and collect optimal information. For example, the collection unit can use AI to collect relevant information based on information shared by the user on social media. Furthermore, the collection unit can analyze the user's social media activity history and collect relevant information. For example, the collection unit can collect relevant information based on information about accounts the user follows. This allows the collection unit to collect highly relevant information based on the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can use AI to analyze the user's social media activity and collect optimal information.

[0073] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions using a camera and estimate the user's emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can record the user's voice using a microphone and estimate the user's emotions using a voice analysis algorithm. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit can analyze text entered by the user and estimate the user's emotions using a text analysis algorithm. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions. For example, the analysis unit can provide a simple, highly visible analysis result when the user is nervous. The analysis unit can provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a concise analysis result when the user is in a hurry. This allows the analysis unit to provide an optimal analysis result according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may use AI to estimate the user's emotions and adjust the way the analysis is presented.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, uses impact and relevance as criteria to evaluate the importance of the information. For example, the analysis unit can evaluate the impact of the information and perform a detailed analysis of information with high importance. The analysis unit can also evaluate the relevance of the information and perform a simplified analysis of information with low importance. Furthermore, the analysis unit can use AI to evaluate the importance of the information and adjust the level of detail of the analysis. For example, the analysis unit can use AI to evaluate the impact and relevance of the information and determine the priority of the analysis according to the importance. This allows the analysis unit to perform an optimal analysis according to the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the importance of the information and adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit uses criteria such as technical information or business information to classify the category of information. For example, the analysis unit can apply a natural language processing algorithm to technical information and a statistical analysis algorithm to business information. The analysis unit can also apply an image analysis algorithm to image information. For example, the analysis unit can use image recognition technology to analyze image information. Furthermore, the analysis unit can use AI to select the optimal analysis algorithm depending on the category of information. For example, the analysis unit can use AI to classify the category of information and apply the optimal analysis algorithm accordingly. This allows the analysis unit to perform the optimal analysis depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to classify the category of information and apply the optimal analysis algorithm accordingly.

[0076] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the analysis unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit can analyze text entered by the user and estimate the user's emotion using a text analysis algorithm. This allows the analysis unit to adjust the length of the analysis based on the user's emotion. For example, the analysis unit can provide a short and to-the-point analysis result if the user is in a hurry. The analysis unit can provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide an analysis result with visually stimulating effects if the user is excited. This allows the analysis unit to provide an analysis result of an optimal length depending on the user's emotion. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may use AI to estimate the user's emotions and adjust the length of the analysis.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit uses the submission date or time as a criterion to evaluate the time of information submission. For example, the analysis unit can prioritize analysis of the most recent information and lower the priority of information submitted earlier. The analysis unit can also use AI to evaluate the time of information submission and determine the priority of analysis. For example, the analysis unit can use AI to adjust the analysis schedule based on the time of submission. This allows the analysis unit to perform optimal analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the time of information submission and determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. For example, the analysis unit uses common keywords or related topics as a criterion to evaluate the relevance of information. For example, the analysis unit can prioritize the analysis of information with common keywords and postpone the analysis order of less relevant information. The analysis unit can also use AI to evaluate the relevance of information and adjust the order of analysis. For example, the analysis unit can use AI to adjust the analysis schedule based on the relevance of information. This allows the analysis unit to perform analysis in an optimal order based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the relevance of information and adjust the order of analysis.

[0079] The aggregation unit can estimate the user's emotion and adjust aggregation criteria based on the estimated user's emotion. The aggregation unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the aggregation unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The aggregation unit can also estimate the user's emotion using voice analysis technology. For example, the aggregation unit can record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. The aggregation unit can also estimate the user's emotion using text analysis technology. For example, the aggregation unit can analyze text entered by the user and estimate the user's emotion using a text analysis algorithm. This allows the aggregation unit to adjust the aggregation criteria based on the user's emotion. For example, if the user is nervous, the aggregation unit can provide a simple, highly visible aggregation result. If the user is relaxed, the aggregation unit can provide a detailed aggregation result. If the user is in a hurry, the aggregation unit can provide a summary of the main points. This allows the aggregation unit to aggregate information using optimal criteria according to the user's emotion. Some or all of the above-described processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit may use AI to estimate the user's emotions and adjust aggregation criteria.

[0080] The aggregator can improve the accuracy of aggregation by taking into account the interrelationships between information during aggregation. For example, the aggregator uses common keywords or related topics as criteria to evaluate the interrelationships between information. For example, the aggregator can group information with common keywords and aggregate the related information. The aggregator can also use AI to evaluate the interrelationships between information and improve the accuracy of aggregation. For example, the aggregator can use AI to analyze the interrelationships between information and determine aggregation priorities. This allows the aggregator to improve the accuracy of aggregation by taking into account the interrelationships between information. Some or all of the above-described processing in the aggregator may be performed using AI, or may be performed without using AI. For example, the aggregator can use AI to evaluate the interrelationships between information and improve the accuracy of aggregation.

[0081] The aggregating unit can perform aggregation while taking into account the attribute information of the information submitter. For example, the aggregating unit uses job title or field of expertise as criteria to evaluate the attribute information of the information submitter. For example, the aggregating unit can aggregate information based on the submitter's job title. The aggregating unit can also aggregate information based on the submitter's field of expertise. Furthermore, the aggregating unit can use AI to evaluate the attribute information of the information submitter and improve the accuracy of the aggregation. For example, the aggregating unit can use AI to aggregate information based on the submitter's past performance. This allows the aggregating unit to improve the accuracy of the aggregation by taking into account the attribute information of the information submitter. Some or all of the above-described processing in the aggregating unit may be performed using AI, for example, or may be performed without using AI. For example, the aggregating unit can use AI to evaluate the attribute information of the information submitter and improve the accuracy of the aggregation.

[0082] The aggregation unit can estimate the user's emotion and adjust the order in which the aggregation results are displayed based on the estimated user's emotion. The aggregation unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the aggregation unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The aggregation unit can also estimate the user's emotion using voice analysis technology. For example, the aggregation unit can record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. The aggregation unit can also estimate the user's emotion using text analysis technology. For example, the aggregation unit can analyze text entered by the user and estimate the user's emotion using a text analysis algorithm. This allows the aggregation unit to adjust the order in which the aggregation results are displayed based on the user's emotion. For example, if the user is nervous, the aggregation unit can prioritize displaying information of high importance. If the user is relaxed, the aggregation unit can prioritize displaying detailed information. If the user is in a hurry, the aggregation unit can prioritize displaying information that summarizes the main points. This allows the aggregating unit to display the aggregated results in an optimal order according to the user's emotions. Some or all of the above-described processing in the aggregating unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregating unit may use AI to estimate the user's emotions and adjust the order in which the aggregated results are displayed.

[0083] The aggregating unit can perform aggregation taking into account the geographical distribution of information. For example, the aggregating unit uses regional information or country-specific information as a criterion to evaluate the geographical distribution of information. For example, the aggregating unit can prioritize aggregating information that is geographically close. The aggregating unit can also group information based on geographical distribution to improve the accuracy of aggregation. Furthermore, the aggregating unit can use AI to evaluate the geographical distribution of information and determine the priority of aggregation. For example, the aggregating unit can use AI to group information taking into account geographical distribution to improve the accuracy of aggregation. This allows the aggregating unit to improve the accuracy of aggregation taking into account the geographical distribution of information. Some or all of the above-described processing in the aggregating unit may be performed using AI, or may be performed without using AI. For example, the aggregating unit can use AI to evaluate the geographical distribution of information to improve the accuracy of aggregation.

[0084] The aggregating unit can improve the accuracy of aggregation by referring to literature related to the information during aggregation. For example, the aggregating unit uses cited literature and reference literature as a basis for referring to literature related to the information. For example, the aggregating unit can confirm the reliability of the information by referring to the related literature. The aggregating unit can also analyze interrelationships of information based on the related literature and improve the accuracy of aggregation. Furthermore, the aggregating unit can use AI to refer to literature related to the information and improve the accuracy of aggregation. For example, the aggregating unit can use AI to analyze interrelationships of information based on the related literature and determine aggregation priorities. This allows the aggregating unit to improve the accuracy of aggregation by referring to literature related to the information. Some or all of the above-mentioned processing in the aggregating unit may be performed using AI, or may be performed without using AI. For example, the aggregating unit can use AI to refer to literature related to the information and improve the accuracy of aggregation.

[0085] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit can capture the user's facial expression using a camera and estimate the user's emotions using a facial expression recognition algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can record the user's voice using a microphone and estimate the user's emotions using a voice analysis algorithm. The providing unit can also estimate the user's emotions using text analysis technology. For example, the providing unit can analyze text entered by the user and estimate the user's emotions using a text analysis algorithm. This allows the providing unit to determine the priority of information to be provided based on the user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing information of high importance. Also, if the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information that can be provided quickly. This allows the providing unit to prioritize providing important information according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use AI to estimate the user's emotions and determine the priority of the information to be provided.

[0086] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the information when providing the information. For example, the providing unit uses common keywords or related topics as criteria to evaluate the interrelationships between the information. For example, the providing unit can group information having common keywords and provide the related information. The providing unit can also use AI to evaluate the interrelationships between the information and improve the accuracy of the information provided. For example, the providing unit can use AI to analyze the interrelationships between the information and determine the priority of the information to be provided. This allows the providing unit to improve the accuracy of the information provided by taking into account the interrelationships between the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can use AI to evaluate the interrelationships between the information and improve the accuracy of the information provided.

[0087] The providing unit can provide the information while taking into consideration the attribute information of the person submitting the information. The providing unit, for example, uses job position or field of expertise as criteria to evaluate the attribute information of the person submitting the information. For example, the providing unit can provide information based on the job title of the person submitting the information. The providing unit can also provide information based on the person's field of expertise. Furthermore, the providing unit can use AI to evaluate the attribute information of the person submitting the information and improve the accuracy of the information provided. For example, the providing unit can use AI to provide information based on the person's past performance. This allows the providing unit to improve the accuracy of the information provided by taking into consideration the attribute information of the person submitting the information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can use AI to evaluate the attribute information of the person submitting the information and improve the accuracy of the information provided.

[0088] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the providing unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. The providing unit can also estimate the user's emotion using text analysis technology. For example, the providing unit can analyze text entered by the user and estimate the user's emotion using a text analysis algorithm. This allows the providing unit to adjust the display method of the information to be provided based on the user's emotion. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can provide a display method including detailed information. If the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows the providing unit to provide information in an optimal display method according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to estimate the user's emotions and adjust the display method of the information to be provided.

[0089] The providing unit may provide the information taking into consideration the geographical distribution of the information. For example, the providing unit may use regional information or country-specific information as a criterion to evaluate the geographical distribution of the information. For example, the providing unit may preferentially provide geographically close information. The providing unit may also group the information based on the geographical distribution to improve the accuracy of the provision. Furthermore, the providing unit may use AI to evaluate the geographical distribution of the information and determine the priority of the provision. For example, the providing unit may use AI to group the information taking into consideration the geographical distribution to improve the accuracy of the provision. In this way, the providing unit may improve the accuracy of the provision taking into consideration the geographical distribution of the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may use AI to evaluate the geographical distribution of the information to improve the accuracy of the provision.

[0090] The providing unit can improve the accuracy of the information provided by referring to literature related to the information when providing the information. For example, the providing unit uses cited literature and reference literature as a basis for referring to literature related to the information. For example, the providing unit can confirm the reliability of the information by referring to the related literature. The providing unit can also analyze interrelationships of the information based on the related literature and improve the accuracy of the information provided. Furthermore, the providing unit can use AI to refer to literature related to the information and improve the accuracy of the information provided. For example, the providing unit can use AI to analyze interrelationships of the information based on the related literature and determine the priority of the information provided. This allows the providing unit to improve the accuracy of the information provided by referring to literature related to the information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can use AI to refer to literature related to the information and improve the accuracy of the information provided. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, aggregation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and the processor 28 of the data processing device 12, and collects information from various communication tools. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12, and manages the aggregated information and provides it to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, aggregation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and the processor 28 of the data processing device 12 and collects information from various communication tools. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and aggregates the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and manages the aggregated information and provides it to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, aggregation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314 and the processor 28 of the data processing device 12, and collects information from various communication tools. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and aggregates the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12, and manages the aggregated information and provides it to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, aggregation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and the processor 28 of the data processing device 12 and collects information from various communication tools. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The aggregation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and aggregates the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12 and manages the aggregated information and provides it to the user.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The information management system may further include a notification unit. The notification unit can notify the user of relevant information in real time based on specific conditions set by the user. For example, if the user receives a new email or chat message related to a specific project, the notification unit can immediately notify the user. The notification unit can also notify the user of important information at an appropriate time based on the user's schedule. For example, by notifying the user of relevant information just before a meeting, the user can efficiently prepare. Furthermore, the notification unit can adjust the importance of notifications based on the user's priority. For example, highly urgent information can be notified immediately, and less urgent information can be notified later. This allows the user to perform their work efficiently without missing important information.

[0093] The collection unit can analyze the user's past behavioral history and select the optimal collection method. For example, the collection unit can identify information sources that the user has frequently accessed in the past and prioritize information collection based on that. The collection unit can also determine the priority of information to be collected based on information that the user has previously deemed important. For example, the collection unit can prioritize collection of emails and chat messages that the user has previously deemed important. Furthermore, the collection unit can analyze the user's past behavioral patterns and optimize the timing of collection. For example, if the user works intensively during a specific time period, the collection unit can collect information according to that time period. This allows the collection unit to optimally collect information based on the user's past behavioral history.

[0094] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide an analysis result that focuses on the main points. This allows the analysis unit to provide optimal analysis results according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to estimate the user's emotions and adjust the way the analysis is presented.

[0095] The aggregation unit can improve the accuracy of aggregation by taking into account the interrelationships between information. For example, the aggregation unit can group information based on common keywords or related topics and aggregate related information. The aggregation unit can also use AI to evaluate the interrelationships between information and determine aggregation priorities. This allows the aggregation unit to improve the accuracy of aggregation by taking into account the interrelationships between information. Furthermore, the aggregation unit can also aggregate information by taking into account attribute information of the person who submitted the information. For example, aggregating information based on the submitter's job title or field of expertise enables more accurate information management. This allows the aggregation unit to efficiently aggregate information by taking into account the interrelationships between information and the submitter's attribute information.

[0096] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing information of high importance. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information that can be provided quickly. This allows the providing unit to prioritize providing important information according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use AI to estimate the user's emotions and determine the priority of information to be provided.

[0097] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the providing unit can prioritize providing nearby events and news based on the user's current location. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to the user's travel destination. Furthermore, the providing unit can provide region-specific information based on the user's location information. For example, if the user is in a specific region, business information and local news related to that region can be provided. This allows the providing unit to provide more relevant information based on the user's geographical location information.

[0098] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting information of high importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This allows the collection unit to prioritize collecting important information according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to estimate the user's emotions and determine the priority of information to be collected.

[0099] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance based on the impact and relevance of the information. Also, the analysis unit can perform a simplified analysis of information with low importance. Furthermore, the analysis unit can use AI to evaluate the importance of the information and determine the priority of the analysis. This allows the analysis unit to perform optimal analysis based on the importance of the information. For example, the analysis unit can use AI to evaluate the impact and relevance of the information and determine the priority of the analysis based on the importance. This allows the analysis unit to perform optimal analysis based on the importance of the information.

[0100] The aggregation unit can estimate the user's emotions and adjust aggregation criteria based on the estimated user emotions. For example, if the user is nervous, the aggregation unit can provide a simple, highly visible aggregation result. If the user is relaxed, the aggregation unit can provide a detailed aggregation result. If the user is in a hurry, the aggregation unit can provide an aggregation result that focuses on the main points. This allows the aggregation unit to aggregate information using optimal criteria depending on the user's emotions. Some or all of the above-mentioned processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit can use AI to estimate the user's emotions and adjust the aggregation criteria.

[0101] The providing unit can improve the accuracy of the information provided by referring to related literature when providing the information. For example, the providing unit can confirm the reliability of the information by referring to related literature. The providing unit can also analyze the interrelationships of the information based on the related literature and improve the accuracy of the information provided. Furthermore, the providing unit can also use AI to improve the accuracy of the information provided by referring to related literature. For example, the providing unit can use AI to analyze the interrelationships of the information based on the related literature and determine the priority of the information provided. This allows the providing unit to improve the accuracy of the information provided by referring to related literature.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects information from various communication tools. For example, the collection unit can collect email content, chat messages, and web form input data. The collection unit obtains email content from the mail server, obtains messages from the chat application, and collects web form input data. The collection unit can also use AI to adjust the type of information to be collected and the frequency of collection. For example, it can select the type of information to be collected based on the user's work and adjust the frequency of collection. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to analyze the content of the collected information and identify relevant information. For example, it analyzes the content of collected emails and extracts related keywords and topics. The analysis unit can also use AI to improve the accuracy of the analysis. For example, it analyzes the content of the collected information in detail and identifies highly relevant information. Step 3: The aggregation unit aggregates the information analyzed by the analysis unit. The aggregation unit groups together information related to the same project. For example, it groups together related emails and chat messages identified by the analysis unit. The aggregation unit can also use AI to improve the accuracy of the aggregation. For example, it can analyze the interrelationships between information to improve the accuracy of the aggregation. Step 4: The providing unit manages the information aggregated by the aggregating unit and provides the user with the information they need. The providing unit provides the user with relevant information based on specific keywords. For example, based on keywords entered by the user, it can instantly display related emails or chat messages. The providing unit can also use AI to improve the accuracy of the information it provides. For example, it can provide the most appropriate information based on the user's business activities and interests.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information from various communication tools; an analysis unit that analyzes the information collected by the collection unit; an aggregation unit that aggregates the information analyzed by the analysis unit; a providing unit that manages the information aggregated by the aggregation unit and provides information required by the user. A system characterized by:

2. The collecting unit Collect email content, chat messages, and web form input data The system of claim 1 .

3. The analysis unit Analyze the collected information and identify relevant information The system of claim 1 .

4. The collecting unit is Group information related to the same project together The system of claim 1 .

5. The providing unit Providing users with relevant information based on specific keywords The system of claim 1 .

6. The providing unit Automatically gather relevant information before a meeting The system of claim 1 .

7. The collecting unit Estimate the user's emotions and select a collection method based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Analyze the user's past communication history and select the optimal collection method The system of claim 1 .

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