system

Abstract

The system according to the embodiment aims to understand the business operations of each company and each department, and to support business collaboration and the creation of new ideas. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects business details of each company and department. The analysis unit analyzes the information collected by the collection unit. The provision unit supports business collaboration and the creation of new ideas based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently grasp the business operations of each company and department, and do not adequately support business collaboration and the creation of new ideas, so there is room for improvement.

[0005] The system according to the embodiment aims to understand the business operations of each company and each department, and to support business collaboration and the creation of new ideas. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects business details of each company and department. The analysis unit analyzes the information collected by the collection unit. The provision unit supports business collaboration and the creation of new ideas based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the business operations of each company and each department, and can support business collaboration and the creation of new ideas. [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 AI system according to an embodiment of the present invention supports business collaboration, consultation, and the creation of new ideas within a corporate group. This system stores the business details of each company and department in a database and trains AI to learn from emails and documents, thereby facilitating business collaboration, consultation, and the creation of new ideas. The system identifies the business details of each company and department within the group and provides the necessary information to improve business efficiency and create new value. For example, the system stores the business details of each company and department in a database. Detailed information about each department, such as the business details, staff, and contact information, is collected and registered in the database. For example, the business details of the sales department and the staff's contact information, and the project details of the technical department, are registered in the database. Next, the AI ​​is trained to learn from emails and documents. The AI ​​analyzes past emails and documents to learn business details and collaboration patterns. For example, the AI ​​analyzes emails and documents containing project progress, problems, and solutions to identify business collaboration patterns. Furthermore, the system supports business collaboration, consultation, and the creation of new ideas based on the information learned by the AI. For example, when a department starts a new project, the system can provide information on similar past projects and suggest ways to collaborate with other departments. It can also provide related information to support the creation of new ideas across different departments. This system can facilitate business collaboration within the group and is expected to create new value. For example, it can promote information sharing between different departments, enabling more efficient business execution. It can also promote the creation of new ideas, improving the competitiveness of the entire company. In this way, the AI ​​system can support business collaboration and the creation of new ideas within a corporate group.

[0029] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects business details for each company and department. The collection unit collects detailed information, such as the business details, personnel, and contact information for each department. For example, the collection unit can collect information such as the business details of the sales department, the personnel's contact information, and the project details of the technical department. The collection unit can also automatically collect business details for each department using AI. For example, the collection unit periodically updates the business details of each department to maintain the latest information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes past emails and documents to learn business details and collaboration patterns. For example, the analysis unit can analyze emails and documents containing project progress, problems, and solutions to identify business collaboration patterns. The analysis unit can also automatically analyze the collected information using AI. For example, the analysis unit uses natural language processing technology to analyze the content of emails and documents and extract business details and collaboration patterns. The provision unit provides information to support business collaboration and new idea generation based on the analysis results obtained by the analysis unit. For example, when a department starts a new project, the provision unit provides information on similar past projects. For example, the provision unit can provide information such as the progress, deliverables, and problems of similar past projects. The provision unit also provides related information to support the creation of new ideas across different business operations. For example, the provision unit can promote information sharing between different departments and support the creation of new ideas. As a result, the AI ​​system according to the embodiment can support business collaboration and the creation of new ideas within a corporate group.

[0030] The collection unit can collect information on the work content, personnel, and contact information of each department. The collection unit, for example, collects detailed information such as the work content, personnel, and contact information of each department. For example, it can collect the work content of the sales department, the personnel's contact information, and project details of the technical department. The collection unit can also use AI to automatically collect the work content of each department. For example, the collection unit regularly updates the work content of each department and keeps the latest information. This allows for the collection of detailed information, which facilitates business collaboration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the work content of each department, the collection unit can automatically collect information using AI and register it in a database.

[0031] The analysis unit can analyze past emails or documents to learn business content or collaboration patterns. The analysis unit, for example, analyzes past emails and documents to learn business content and collaboration patterns. For example, the analysis unit can analyze emails and documents containing project progress, problems, solutions, etc., to understand business collaboration patterns. The analysis unit can also automatically analyze collected information using AI. For example, the analysis unit can analyze the content of emails and documents using natural language processing technology to extract business content and collaboration patterns. In this way, business collaboration patterns can be understood by analyzing past data. 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, when analyzing past emails and documents, the analysis unit can automatically analyze information using AI to learn business content and collaboration patterns.

[0032] The provision unit can provide information to support business collaboration and the creation of new ideas based on the analysis results. The provision unit can provide information to support business collaboration and the creation of new ideas based on, for example, the analysis results. For example, when a department starts a new project, the provision unit can provide information on past similar projects. The provision unit can provide information such as the progress, deliverables, and problems of past similar projects. The provision unit can also provide related information to support the creation of new ideas between different businesses. For example, the provision unit can promote information sharing between different departments and support the creation of new ideas. This can support business collaboration and the creation of new ideas by providing appropriate information based on the analysis results. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without AI. For example, the provision unit can automatically provide information using AI based on the analysis results to support business collaboration and the creation of new ideas.

[0033] The provision unit can provide information on similar past projects when a certain department starts a project. For example, when a certain department starts a new project, the provision unit can provide information on similar past projects. For example, the provision unit can provide information on the progress, deliverables, problems, etc. of similar past projects. By providing information on similar past projects, it is possible to support the smooth start of a new project. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can automatically collect and provide information on similar past projects using AI.

[0034] The provision unit can provide information to support idea creation across different business operations. The provision unit, for example, provides related information to support new idea creation across different business operations. For example, the provision unit can promote information sharing between different departments and support the creation of new ideas. This can improve the competitiveness of the entire company by supporting new idea creation across different business operations. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can automatically collect and provide related information to support new idea creation across different business operations using AI.

[0035] The collection unit can monitor changes in the work content of each department and automatically update the information when changes occur. For example, the collection unit can monitor changes in the work content of each department in real time and automatically update the information when changes occur. For example, the collection unit can update the database in real time when changes occur in the work content of each department. Furthermore, when a new project is started, the collection unit can immediately collect information about that project and reflect it in the database. Furthermore, the collection unit can periodically collect information from departments whose work content changes frequently and maintain the latest information. This allows for real-time monitoring of changes in work content and automatic updating of information, thereby always maintaining the latest information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can monitor changes in the work content of each department in real time using AI and automatically update the information when changes occur.

[0036] The collection unit can determine the priority of work when collecting the work content of each department. For example, when collecting the work content of each department, the collection unit determines the priority of collection based on the importance of the work. For example, the collection unit can prioritize collecting information about important projects and register it in a database. The collection unit can also prioritize collecting work content with high urgency and provide it quickly. Furthermore, the collection unit can prioritize collecting information about short-term projects over long-term projects. As a result, by determining the priority of collection based on the importance of the work, important information can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the work content of each department, the collection unit can automatically determine the priority of collection based on the importance of the work using AI.

[0037] When collecting the business details of each department, the collection unit can prioritize collecting information based on geographical location information. For example, when collecting the business details of each department, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. For example, the collection unit can prioritize collecting business details of neighboring departments to strengthen collaboration. Furthermore, the collection unit can prioritize collecting information on geographically close projects to ensure efficient collaboration. Furthermore, the collection unit can prioritize collecting highly relevant information based on geographical location information. This enables efficient business collaboration by prioritizing the collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the business details of each department, the collection unit can prioritize collecting information based on geographical location information by using AI.

[0038] The collection unit can analyze social media activity and collect information when collecting the work content of each department. For example, when collecting the work content of each department, the collection unit can analyze social media activity and collect related information. For example, the collection unit can monitor the social media activity of each department and collect related information. The collection unit can also collect project progress on social media and reflect it in a database. Furthermore, the collection unit can monitor changes in work content on social media in real time and update the information. This makes it possible to efficiently collect related information by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the work content of each department, the collection unit can analyze social media activity using AI and automatically collect related information.

[0039] The analysis unit can adjust the level of detail of the work when analyzing past emails and documents. For example, when analyzing past emails and documents, the analysis unit adjusts the level of detail of the analysis based on the importance of the work. For example, the analysis unit can analyze emails and documents related to important projects in detail. The analysis unit can also prioritize analysis of emails and documents containing urgent work content. Furthermore, the analysis unit can analyze emails and documents related to short-term projects in more detail than long-term projects. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the work. 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, when analyzing past emails and documents, the analysis unit can automatically adjust the level of detail of the analysis based on the importance of the work using AI.

[0040] When analyzing past emails and documents, the analysis unit can apply different analysis algorithms depending on the business. For example, when analyzing past emails and documents, the analysis unit can apply different analysis algorithms depending on the business category. For example, the analysis unit can apply a technical analysis algorithm to emails and documents from the technical department. Furthermore, the analysis unit can apply a sales-related analysis algorithm to emails and documents from the sales department. Furthermore, the analysis unit can apply an analysis algorithm specialized for administrative work to emails and documents from the administrative department. In this way, by applying different analysis algorithms depending on the business category, the accuracy of the analysis is improved. 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, when analyzing past emails and documents, the analysis unit can automatically apply different analysis algorithms depending on the business category using AI.

[0041] When analyzing past emails and documents, the analysis unit can determine the priority of analysis based on the time of submission. For example, when analyzing past emails and documents, the analysis unit can determine the priority of analysis based on the time of submission. For example, the analysis unit can prioritize analysis of recently submitted emails and documents. The analysis unit can also prioritize analysis of documents with high urgency. Furthermore, the analysis unit can prioritize analysis of newer documents over older documents. In this way, by determining the priority of analysis based on the time of submission, the latest information can be analyzed preferentially. 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, when analyzing past emails and documents, the analysis unit can automatically determine the priority of analysis based on the time of submission using AI.

[0042] The analysis unit can improve the accuracy of the analysis by referring to literature when analyzing past emails and documents. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing past emails and documents. For example, the analysis unit can improve the accuracy of the technical analysis by referring to related technical literature. The analysis unit can also improve the accuracy of the sales-related analysis by referring to related sales literature. Furthermore, the analysis unit can improve the accuracy of the management task analysis by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature. 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 refer to related literature when analyzing past emails and documents, and automatically improve the accuracy of the analysis.

[0043] The providing unit can adjust the level of detail of the information to be provided based on the analysis results. The providing unit can adjust the level of detail of the information to be provided based on, for example, the importance of the information to be provided based on the analysis results. For example, the providing unit can provide important information in detail to make it easier for the user to understand. The providing unit can also quickly provide highly urgent information to enable the user to respond immediately. Furthermore, the providing unit can prioritize providing short-term information over long-term information. As a result, by adjusting the level of detail of the information to be provided based on the importance of the information, it is possible to provide information that is easy for the user to understand. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can automatically adjust the level of detail of the information to be provided based on the analysis results using AI.

[0044] The providing unit can apply different provision algorithms depending on the information to be provided based on the analysis results. The providing unit can apply different provision algorithms depending on, for example, the category of information to be provided based on the analysis results. For example, the providing unit can apply a technical provision algorithm to technology-related information. The providing unit can also apply a sales-related provision algorithm to sales-related information. The providing unit can also apply a provision algorithm specialized for management work to management-related information. This makes it possible to provide optimal information to users by applying an appropriate provision algorithm depending on the category of 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 automatically apply different provision algorithms using AI depending on the category of information to be provided based on the analysis results.

[0045] The providing unit can determine the priority of provision based on the submission time of the information to be provided based on the analysis results. The providing unit can determine the priority of provision based on, for example, the submission time of the information to be provided based on the analysis results. For example, the providing unit can prioritize providing recently submitted information. The providing unit can also prioritize providing information with high urgency. Furthermore, the providing unit can prioritize providing newer information over information submitted earlier. In this way, by determining the priority of information based on the submission time, the latest information can be prioritized. 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 automatically determine the priority of provision using AI based on the submission time of the information to be provided based on the analysis results.

[0046] The providing unit can adjust the order of provision based on the relevance of the information to be provided based on the analysis results. The providing unit can adjust the order of provision based on, for example, the relevance of the information to be provided based on the analysis results. For example, the providing unit can prioritize providing highly relevant information, allowing the user to quickly obtain the information they need. The providing unit can also prioritize providing important information, putting less relevant information aside. Furthermore, the providing unit can prioritize providing information related to the user's work content. As a result, by adjusting the order of provision based on the relevance of the information, the user can quickly obtain the information they need. 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 automatically adjust the order of provision using AI based on the relevance of the information to be provided based on the analysis results.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When collecting work details from each department, the collection department can monitor the progress of the work in real time and update the information according to the progress. For example, the collection department can periodically check the progress of a project and automatically update the database if there is progress. In addition, if progress is delayed, the collection department can prioritize collecting related information and provide support to resolve the problem. Furthermore, for projects that are progressing smoothly, the collection department can collect detailed information and strengthen collaboration with other departments. In this way, by updating information according to the progress of work, the latest information can always be maintained and efficient business collaboration can be achieved.

[0049] When analyzing past emails and documents, the analysis department can assess the risks of tasks and perform detailed analysis of high-risk tasks. For example, the analysis department can identify risk factors for a project and prioritize analysis of high-risk tasks. The analysis department can also propose risk mitigation measures based on the risk assessment. Furthermore, the analysis department can perform a simplified analysis of low-risk tasks and provide information efficiently. This allows the level of detail in the analysis to be adjusted based on the risk assessment of the task, allowing for detailed analysis of important information and strengthening risk management.

[0050] The providing unit can evaluate the reliability of the information to be provided based on the analysis results and provide highly reliable information preferentially. For example, the providing unit can evaluate reliability based on the source of the information and past performance, and provide highly reliable information preferentially. The providing unit can also warn users about information with low reliability and encourage them to make careful decisions. Furthermore, the providing unit can provide the reliability evaluation results to the user, which can be used to help them select information. This allows the user to quickly obtain highly reliable information by determining the priority of information to be provided based on the reliability of the information.

[0051] When collecting the business details of each department, the collection department can adjust the frequency of collection based on the importance of the business. For example, the collection department can frequently collect information about important projects to keep the database up to date. The collection department can also reduce the frequency of collection for less important business, allowing for efficient information management. Furthermore, the collection department can adjust the level of detail of the information to be collected depending on the importance of the business. In this way, by adjusting the frequency of collection based on the importance of the business, important information can be collected on a priority basis, enabling efficient information management.

[0052] When analyzing past emails and documents, the analysis department can apply different analysis methods depending on the business category. For example, the analysis department applies technical analysis methods to emails and documents from the technical department to extract technical problems and solutions. The analysis department also applies sales-related analysis methods to emails and documents from the sales department to understand sales strategies and customer response patterns. Furthermore, the analysis department applies analysis methods specialized for administrative work to emails and documents from the administrative department to make suggestions for improving business efficiency. In this way, applying the appropriate analysis method depending on the business category improves the accuracy of the analysis and enables the provision of effective information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection department collects the business details of each company and department. For example, detailed information such as the business details of the sales department, contact information for the person in charge, and project details of the technical department is collected. The collection department uses AI to automatically collect the business details of each department and regularly updates it to keep the information up to date. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes past emails and documents to learn about business operations and collaboration patterns. The analysis unit uses AI and natural language processing technology to analyze the content of emails and documents and extract business operations and collaboration patterns. Step 3: The provision unit provides information to support business collaboration and the creation of new ideas based on the analysis results obtained by the analysis unit. For example, when a department starts a new project, it provides information on the progress, deliverables, and problems of past similar projects. It also promotes information sharing between different departments and supports the creation of new ideas.

[0055] (Example 2) An AI system according to an embodiment of the present invention supports business collaboration, consultation, and the creation of new ideas within a corporate group. This system stores the business details of each company and department in a database and trains AI to learn from emails and documents, thereby facilitating business collaboration, consultation, and the creation of new ideas. The system identifies the business details of each company and department within the group and provides the necessary information to improve business efficiency and create new value. For example, the system stores the business details of each company and department in a database. Detailed information about each department, such as the business details, staff, and contact information, is collected and registered in the database. For example, the business details of the sales department and the staff's contact information, and the project details of the technical department, are registered in the database. Next, the AI ​​is trained to learn from emails and documents. The AI ​​analyzes past emails and documents to learn business details and collaboration patterns. For example, the AI ​​analyzes emails and documents containing project progress, problems, and solutions to identify business collaboration patterns. Furthermore, the system supports business collaboration, consultation, and the creation of new ideas based on the information learned by the AI. For example, when a department starts a new project, the system can provide information on similar past projects and suggest ways to collaborate with other departments. It can also provide related information to support the creation of new ideas across different departments. This system can facilitate business collaboration within the group and is expected to create new value. For example, it can promote information sharing between different departments, enabling more efficient business execution. It can also promote the creation of new ideas, improving the competitiveness of the entire company. In this way, the AI ​​system can support business collaboration and the creation of new ideas within a corporate group.

[0056] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects business details for each company and department. The collection unit collects detailed information, such as the business details, personnel, and contact information for each department. For example, the collection unit can collect information such as the business details of the sales department, the personnel's contact information, and the project details of the technical department. The collection unit can also automatically collect business details for each department using AI. For example, the collection unit periodically updates the business details of each department to maintain the latest information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes past emails and documents to learn business details and collaboration patterns. For example, the analysis unit can analyze emails and documents containing project progress, problems, and solutions to identify business collaboration patterns. The analysis unit can also automatically analyze the collected information using AI. For example, the analysis unit uses natural language processing technology to analyze the content of emails and documents and extract business details and collaboration patterns. The provision unit provides information to support business collaboration and new idea generation based on the analysis results obtained by the analysis unit. For example, when a department starts a new project, the provision unit provides information on similar past projects. For example, the provision unit can provide information such as the progress, deliverables, and problems of similar past projects. The provision unit also provides related information to support the creation of new ideas across different business operations. For example, the provision unit can promote information sharing between different departments and support the creation of new ideas. As a result, the AI ​​system according to the embodiment can support business collaboration and the creation of new ideas within a corporate group.

[0057] The collection unit can collect information on the work content, personnel, and contact information of each department. The collection unit, for example, collects detailed information such as the work content, personnel, and contact information of each department. For example, it can collect the work content of the sales department, the personnel's contact information, and project details of the technical department. The collection unit can also use AI to automatically collect the work content of each department. For example, the collection unit regularly updates the work content of each department and keeps the latest information. This allows for the collection of detailed information, which facilitates business collaboration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the work content of each department, the collection unit can automatically collect information using AI and register it in a database.

[0058] The analysis unit can analyze past emails or documents to learn business content or collaboration patterns. The analysis unit, for example, analyzes past emails and documents to learn business content and collaboration patterns. For example, the analysis unit can analyze emails and documents containing project progress, problems, solutions, etc., to understand business collaboration patterns. The analysis unit can also automatically analyze collected information using AI. For example, the analysis unit can analyze the content of emails and documents using natural language processing technology to extract business content and collaboration patterns. In this way, business collaboration patterns can be understood by analyzing past data. 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, when analyzing past emails and documents, the analysis unit can automatically analyze information using AI to learn business content and collaboration patterns.

[0059] The provision unit can provide information to support business collaboration and the creation of new ideas based on the analysis results. The provision unit can provide information to support business collaboration and the creation of new ideas based on, for example, the analysis results. For example, when a department starts a new project, the provision unit can provide information on past similar projects. The provision unit can provide information such as the progress, deliverables, and problems of past similar projects. The provision unit can also provide related information to support the creation of new ideas between different businesses. For example, the provision unit can promote information sharing between different departments and support the creation of new ideas. This can support business collaboration and the creation of new ideas by providing appropriate information based on the analysis results. Some or all of the above-described processing in the provision unit may be performed using AI, or may be performed without AI. For example, the provision unit can automatically provide information using AI based on the analysis results to support business collaboration and the creation of new ideas.

[0060] The provision unit can provide information on similar past projects when a certain department starts a project. For example, when a certain department starts a new project, the provision unit can provide information on similar past projects. For example, the provision unit can provide information on the progress, deliverables, problems, etc. of similar past projects. By providing information on similar past projects, it is possible to support the smooth start of a new project. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can automatically collect and provide information on similar past projects using AI.

[0061] The provision unit can provide information to support idea creation across different business operations. The provision unit, for example, provides related information to support new idea creation across different business operations. For example, the provision unit can promote information sharing between different departments and support the creation of new ideas. This can improve the competitiveness of the entire company by supporting new idea creation across different business operations. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can automatically collect and provide related information to support new idea creation across different business operations using AI.

[0062] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection to reduce the burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of information collection to collect detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important information and provide it quickly. This reduces the burden on the user by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can estimate the user's emotions and automatically adjust the timing of information collection based on the estimated emotions using AI.

[0063] The collection unit can monitor changes in the work content of each department and automatically update the information when changes occur. For example, the collection unit can monitor changes in the work content of each department in real time and automatically update the information when changes occur. For example, the collection unit can update the database in real time when changes occur in the work content of each department. Furthermore, when a new project is started, the collection unit can immediately collect information about that project and reflect it in the database. Furthermore, the collection unit can periodically collect information from departments whose work content changes frequently and maintain the latest information. This allows for real-time monitoring of changes in work content and automatic updating of information, thereby always maintaining the latest information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can monitor changes in the work content of each department in real time using AI and automatically update the information when changes occur.

[0064] The collection unit can determine the priority of work when collecting the work content of each department. For example, when collecting the work content of each department, the collection unit determines the priority of collection based on the importance of the work. For example, the collection unit can prioritize collecting information about important projects and register it in a database. The collection unit can also prioritize collecting work content with high urgency and provide it quickly. Furthermore, the collection unit can prioritize collecting information about short-term projects over long-term projects. As a result, by determining the priority of collection based on the importance of the work, important information can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the work content of each department, the collection unit can automatically determine the priority of collection based on the importance of the work using AI.

[0065] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit can prioritize collecting only important information. 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 enables information collection according to the user's needs by determining the priority of information to be collected based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can estimate the user's emotions and automatically determine the priority of information to be collected based on the estimated emotions using AI.

[0066] When collecting the business details of each department, the collection unit can prioritize collecting information based on geographical location information. For example, when collecting the business details of each department, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. For example, the collection unit can prioritize collecting business details of neighboring departments to strengthen collaboration. Furthermore, the collection unit can prioritize collecting information on geographically close projects to ensure efficient collaboration. Furthermore, the collection unit can prioritize collecting highly relevant information based on geographical location information. This enables efficient business collaboration by prioritizing the collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the business details of each department, the collection unit can prioritize collecting information based on geographical location information by using AI.

[0067] The collection unit can analyze social media activity and collect information when collecting the work content of each department. For example, when collecting the work content of each department, the collection unit can analyze social media activity and collect related information. For example, the collection unit can monitor the social media activity of each department and collect related information. The collection unit can also collect project progress on social media and reflect it in a database. Furthermore, the collection unit can monitor changes in work content on social media in real time and update the information. This makes it possible to efficiently collect related information by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, when collecting the work content of each department, the collection unit can analyze social media activity using AI and automatically collect related information.

[0068] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easy for the user to view. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can estimate the user's emotions and automatically adjust the display method of the analysis results based on the estimated emotions using AI.

[0069] The analysis unit can adjust the level of detail of the work when analyzing past emails and documents. For example, when analyzing past emails and documents, the analysis unit adjusts the level of detail of the analysis based on the importance of the work. For example, the analysis unit can analyze emails and documents related to important projects in detail. The analysis unit can also prioritize analysis of emails and documents containing urgent work content. Furthermore, the analysis unit can analyze emails and documents related to short-term projects in more detail than long-term projects. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the work. 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, when analyzing past emails and documents, the analysis unit can automatically adjust the level of detail of the analysis based on the importance of the work using AI.

[0070] When analyzing past emails and documents, the analysis unit can apply different analysis algorithms depending on the business. For example, when analyzing past emails and documents, the analysis unit can apply different analysis algorithms depending on the business category. For example, the analysis unit can apply a technical analysis algorithm to emails and documents from the technical department. Furthermore, the analysis unit can apply a sales-related analysis algorithm to emails and documents from the sales department. Furthermore, the analysis unit can apply an analysis algorithm specialized for administrative work to emails and documents from the administrative department. In this way, by applying different analysis algorithms depending on the business category, the accuracy of the analysis is improved. 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, when analyzing past emails and documents, the analysis unit can automatically apply different analysis algorithms depending on the business category using AI.

[0071] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, when the user is stressed, the analysis unit can prioritize displaying only important analysis results. Furthermore, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can quickly provide analysis results. Thus, by prioritizing the analysis results according to the user's emotions, important information can be prioritized and provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can estimate the user's emotions and automatically prioritize the analysis results based on the estimated emotions using AI.

[0072] When analyzing past emails and documents, the analysis unit can determine the priority of analysis based on the time of submission. For example, when analyzing past emails and documents, the analysis unit can determine the priority of analysis based on the time of submission. For example, the analysis unit can prioritize analysis of recently submitted emails and documents. The analysis unit can also prioritize analysis of documents with high urgency. Furthermore, the analysis unit can prioritize analysis of newer documents over older documents. In this way, by determining the priority of analysis based on the time of submission, the latest information can be analyzed preferentially. 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, when analyzing past emails and documents, the analysis unit can automatically determine the priority of analysis based on the time of submission using AI.

[0073] The analysis unit can improve the accuracy of the analysis by referring to literature when analyzing past emails and documents. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing past emails and documents. For example, the analysis unit can improve the accuracy of the technical analysis by referring to related technical literature. The analysis unit can also improve the accuracy of the sales-related analysis by referring to related sales literature. Furthermore, the analysis unit can improve the accuracy of the management task analysis by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature. 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 refer to related literature when analyzing past emails and documents, and automatically improve the accuracy of the analysis.

[0074] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows the display method of information to be adjusted according to the user's emotions, making it easy for the user to view. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can estimate the user's emotions and automatically adjust the display method of information based on the estimated emotions using AI.

[0075] The providing unit can adjust the level of detail of the information to be provided based on the analysis results. The providing unit can adjust the level of detail of the information to be provided based on, for example, the importance of the information to be provided based on the analysis results. For example, the providing unit can provide important information in detail to make it easier for the user to understand. The providing unit can also quickly provide highly urgent information to enable the user to respond immediately. Furthermore, the providing unit can prioritize providing short-term information over long-term information. As a result, by adjusting the level of detail of the information to be provided based on the importance of the information, it is possible to provide information that is easy for the user to understand. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can automatically adjust the level of detail of the information to be provided based on the analysis results using AI.

[0076] The providing unit can apply different provision algorithms depending on the information to be provided based on the analysis results. The providing unit can apply different provision algorithms depending on, for example, the category of information to be provided based on the analysis results. For example, the providing unit can apply a technical provision algorithm to technology-related information. The providing unit can also apply a sales-related provision algorithm to sales-related information. The providing unit can also apply a provision algorithm specialized for management work to management-related information. This makes it possible to provide optimal information to users by applying an appropriate provision algorithm depending on the category of 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 automatically apply different provision algorithms using AI depending on the category of information to be provided based on the analysis results.

[0077] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information to be provided based on the estimated user emotions. For example, when the user is stressed, the providing unit can prioritize providing only important information. 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. In this way, by determining the priority of information according to the user's emotions, it is possible to prioritize providing information that is important to the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can estimate the user's emotions and automatically determine the priority of information based on the estimated emotions using AI.

[0078] The providing unit can determine the priority of provision based on the submission time of the information to be provided based on the analysis results. The providing unit can determine the priority of provision based on, for example, the submission time of the information to be provided based on the analysis results. For example, the providing unit can prioritize providing recently submitted information. The providing unit can also prioritize providing information with high urgency. Furthermore, the providing unit can prioritize providing newer information over information submitted earlier. In this way, by determining the priority of information based on the submission time, the latest information can be prioritized. 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 automatically determine the priority of provision using AI based on the submission time of the information to be provided based on the analysis results.

[0079] The providing unit can adjust the order of provision based on the relevance of the information to be provided based on the analysis results. The providing unit can adjust the order of provision based on, for example, the relevance of the information to be provided based on the analysis results. For example, the providing unit can prioritize providing highly relevant information, allowing the user to quickly obtain the information they need. The providing unit can also prioritize providing important information, putting less relevant information aside. Furthermore, the providing unit can prioritize providing information related to the user's work content. As a result, by adjusting the order of provision based on the relevance of the information, the user can quickly obtain the information they need. 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 automatically adjust the order of provision using AI based on the relevance of the information to be provided based on the analysis results. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis 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 control unit 46A of the smart device 14 and collects information such as the business content of each department and contact information of the person in charge. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand business collaboration patterns. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides information to support business collaboration and new idea creation based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information such as the business content of each department and contact information of the person in charge. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand business collaboration patterns. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides information to support business collaboration and new idea creation based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information such as the business content of each department and contact information of the person in charge. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand patterns of business collaboration. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides information to support business collaboration and the creation of new ideas based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information such as the business content of each department and contact information of the person in charge. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand patterns of business collaboration. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides information to support business collaboration and the creation of new ideas based on the analysis results.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When collecting work details from each department, the collection department can monitor the progress of the work in real time and update the information according to the progress. For example, the collection department can periodically check the progress of a project and automatically update the database if there is progress. In addition, if progress is delayed, the collection department can prioritize collecting related information and provide support to resolve the problem. Furthermore, for projects that are progressing smoothly, the collection department can collect detailed information and strengthen collaboration with other departments. In this way, by updating information according to the progress of work, the latest information can always be maintained and efficient business collaboration can be achieved.

[0082] When analyzing past emails and documents, the analysis department can assess the risks of tasks and perform detailed analysis of high-risk tasks. For example, the analysis department can identify risk factors for a project and prioritize analysis of high-risk tasks. The analysis department can also propose risk mitigation measures based on the risk assessment. Furthermore, the analysis department can perform a simplified analysis of low-risk tasks and provide information efficiently. This allows the level of detail in the analysis to be adjusted based on the risk assessment of the task, allowing for detailed analysis of important information and strengthening risk management.

[0083] The providing unit can evaluate the reliability of the information to be provided based on the analysis results and provide highly reliable information preferentially. For example, the providing unit can evaluate reliability based on the source of the information and past performance, and provide highly reliable information preferentially. The providing unit can also warn users about information with low reliability and encourage them to make careful decisions. Furthermore, the providing unit can provide the reliability evaluation results to the user, which can be used to help them select information. This allows the user to quickly obtain highly reliable information by determining the priority of information to be provided based on the reliability of the information.

[0084] When collecting the business details of each department, the collection department can adjust the frequency of collection based on the importance of the business. For example, the collection department can frequently collect information about important projects to keep the database up to date. The collection department can also reduce the frequency of collection for less important business, allowing for efficient information management. Furthermore, the collection department can adjust the level of detail of the information to be collected depending on the importance of the business. In this way, by adjusting the frequency of collection based on the importance of the business, important information can be collected on a priority basis, enabling efficient information management.

[0085] When analyzing past emails and documents, the analysis department can apply different analysis methods depending on the business category. For example, the analysis department applies technical analysis methods to emails and documents from the technical department to extract technical problems and solutions. The analysis department also applies sales-related analysis methods to emails and documents from the sales department to understand sales strategies and customer response patterns. Furthermore, the analysis department applies analysis methods specialized for administrative work to emails and documents from the administrative department to make suggestions for improving business efficiency. In this way, applying the appropriate analysis method depending on the business category improves the accuracy of the analysis and enables the provision of effective information.

[0086] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection to reduce the burden on the user. Furthermore, if the user is relaxed, the collection unit can increase the frequency of information collection to collect more detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important information and provide it quickly. In this way, the burden on the user can be reduced by adjusting the timing of information collection according to the user's emotions.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to see.

[0088] 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 provide only important information with priority. Furthermore, when the user is relaxed, the providing unit can provide detailed information with priority. Furthermore, when the user is in a hurry, the providing unit can provide information that can be provided quickly with priority. In this way, by determining the priority of information according to the user's emotions, it is possible to provide information that is important to the user with priority.

[0089] 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 only important information. When the user is relaxed, the collection unit can prioritize collecting detailed information. When the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. Thus, by determining the priority of information to be collected according to the user's emotions, it becomes possible to collect information that meets the user's needs.

[0090] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of information according to the user's emotions, it is possible to provide a display that is easy for the user to view.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The collection department collects the business details of each company and department. For example, detailed information such as the business details of the sales department, contact information for the person in charge, and project details of the technical department is collected. The collection department uses AI to automatically collect the business details of each department and regularly updates it to keep the information up to date. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes past emails and documents to learn about business operations and collaboration patterns. The analysis unit uses AI and natural language processing technology to analyze the content of emails and documents and extract business operations and collaboration patterns. Step 3: The provision unit provides information to support business collaboration and the creation of new ideas based on the analysis results obtained by the analysis unit. For example, when a department starts a new project, it provides information on the progress, deliverables, and problems of past similar projects. It also promotes information sharing between different departments and supports the creation of new ideas.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 business information from each company and department; an analysis unit that analyzes the information collected by the collection unit; a providing unit that supports business collaboration and new idea creation based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect information on the work content, person in charge, and contact information of each department 2. The system of claim 1.

3. The analysis unit Analyze past emails or documents to learn business content or collaboration patterns 2. The system of claim 1.

4. The providing unit Based on the analysis results, we provide information to support business collaboration and the creation of new ideas.

2. The system of claim 1.

5. The providing unit When a department starts a project, provide information on similar projects from the past 2. The system of claim 1.

6. The providing unit Providing information to support idea generation across different departments 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Monitor changes in the work content of each department and automatically update information when changes occur.

2. The system of claim 1.

Images