System

Abstract

To provide a system according to an embodiment that generates personae on the basis of an operation style or a characteristic of communication of a specific person to streamline job taking-over.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a generation unit; and a response unit. The collection unit is configured to collect an operation use material or mail contents. The analysis unit is configured to analyze information collected by the collection unit, and extract an operation style or a characteristic of communication of an object person. The generation unit is configured to generate personae on the basis of information extracted by the analysis unit. The response unit is configured to respond in real time by personae generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently grasp the working style and communication characteristics of a specific person when handing over work tasks.

[0005] The system according to the embodiment aims to generate a persona based on the working style and communication characteristics of a specific person, thereby making task handover more efficient. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a response unit. The collection unit collects business use materials and email contents. The analysis unit analyzes the information collected by the collection unit and extracts the subject's work style and communication characteristics. The generation unit generates a persona based on the information extracted by the analysis unit. The response unit allows the persona generated by the generation unit to respond in real time. Effect of the Invention

[0007] The system according to the embodiment can generate a persona based on the working style and communication characteristics of a specific person, thereby making task handover more efficient. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The persona generation system according to the embodiment of the present invention is a system that generates a persona of a specific person. In this persona generation system, when a task handover is required, the generation AI generates a persona from the task use materials and email contents of the person, and the person can respond. This is expected to improve the productivity of both parties. For example, when a task handover is required, the task use materials and email contents of the target person are collected. For example, project progress reports, daily task notes, email exchanges, etc. are included. This information is input into the generation AI. Next, the generation AI analyzes the collected information and generates a persona of the target person. The generated persona reflects the task style and communication characteristics of the target person, and can respond. For example, the generated persona provides an appropriate response to a question about the progress of the project. Furthermore, the generated persona can respond in real time, and the task handover can be performed smoothly. For example, if a new person in charge asks a question about the details of the project, the generated persona immediately provides an answer. This minimizes interruptions to the task and realizes an efficient handover. This system allows for smooth handover of work duties during maternity leave or leave of absence, improving the productivity of both parties. For example, when an employee on maternity leave returns to work, the generated persona will understand the progress of the work, allowing her to return to work smoothly. Similarly, when an employee on leave returns to work, the generated persona will understand the details of the work, allowing her to return to work quickly. In this way, the persona generation system allows for smooth handover of work duties, improving the productivity of both parties.

[0029] The persona generation system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a response unit. The collection unit collects business use materials and email contents. The business use materials and email contents include, but are not limited to, project progress reports, daily work notes, and customer correspondence emails, for example. The collection unit collects, for example, project progress reports. The collection unit can also collect daily work notes. Furthermore, the collection unit can collect customer correspondence emails. For example, the collection unit automatically collects project progress reports and stores them in a database. Daily work notes can be collected by scanning handwritten notes and converting them into digital data. Customer correspondence emails are automatically obtained from a mail server and stored for analysis. The analysis unit analyzes the information collected by the collection unit and extracts the work style and communication characteristics of the subject. The analysis is performed, for example, using text mining or natural language processing technology, but is not limited to such examples. For example, the analysis unit uses text mining technology to extract important keywords from project progress reports. The analysis unit can also analyze the work style from the daily work memo using natural language processing technology. Furthermore, the analysis unit can extract communication characteristics from customer correspondence emails. For example, the analysis unit uses text mining technology to extract important keywords from the project progress report and analyze the work style. The natural language processing technology is used to analyze the work style from the daily work memo and extract communication characteristics. The generation unit generates a persona based on the information extracted by the analysis unit. The persona reflects, for example, the work style and communication characteristics of the subject, and can respond. The generation unit generates, for example, a persona reflecting the work style and communication characteristics of the subject. Furthermore, the generation unit enables the generated persona to respond in real time. For example, the generation unit generates a persona reflecting the work style and communication characteristics of the subject, and enables responses in real time. The response unit enables the persona generated by the generation unit to respond in real time.The response is, for example, in response to a question about the progress of the project, the generated persona provides an appropriate response, but is not limited to such an example. For example, the response unit provides an appropriate response to a question about the progress of the project by the generated persona. In addition, when a new person in charge asks about the details of the project, the response unit can also provide an answer immediately. Furthermore, the response unit can minimize interruptions to business and realize efficient handover. For example, when a new person in charge asks about the details of the project, the response unit provides an answer immediately to minimize interruptions to business. As a result, the persona generation system according to the embodiment can smoothly perform the handover of business and improve the productivity of both parties. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model for the generated persona to respond in real time.

[0030] The collection unit collects business use materials and email contents. Examples of business use materials and email contents include, but are not limited to, project progress reports, daily business notes, and customer correspondence emails. The collection unit collects, for example, project progress reports. The collection unit can also collect daily business notes. Furthermore, the collection unit can collect customer correspondence emails. For example, the collection unit automatically collects project progress reports and stores them in a database. Daily business notes can be collected by scanning handwritten notes and converting them into digital data. Customer correspondence emails are automatically obtained from a mail server and stored for analysis. The collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and generation unit. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. Furthermore, the collection unit can utilize various APIs and scripts to automate the data collection process. For example, IMAP or POP3 protocols can be used to obtain data from mail servers, and APIs of file servers or cloud storage services can be used to collect business-use materials. This allows the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit and extracts the characteristics of the target's work style and communication. The analysis is performed, for example, using text mining or natural language processing technology, but is not limited to such examples. For example, the analysis unit uses text mining technology to extract important keywords from project progress reports. The analysis unit can also use natural language processing technology to analyze the work style from daily work notes. Furthermore, the analysis unit can extract communication characteristics from customer correspondence emails. Specifically, using text mining technology, frequently appearing keywords and phrases are extracted from project progress reports to identify the target's work priorities and areas of interest. Using natural language processing technology, the structure and context of sentences are analyzed from daily work notes to clarify the target's work style and work patterns. For customer correspondence emails, sentiment analysis and topic modeling can be used to grasp the target's communication characteristics and customer correspondence trends. By combining these technologies, the analysis unit can analyze the target's work style and communication characteristics from multiple angles and extract more accurate information. Furthermore, the analysis unit can analyze long-term trends and patterns by utilizing past data and statistical information. For example, by analyzing past project reports and work memos, it is possible to grasp changes in work styles at specific times and situations. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term work improvement and risk management, improving the reliability and usefulness of the entire system.

[0032] The generation unit generates a persona based on the information extracted by the analysis unit. The persona reflects, for example, the work style and communication characteristics of the target person, and can respond. The generation unit generates a persona that reflects, for example, the work style and communication characteristics of the target person. In addition, the generation unit enables the generated persona to respond in real time. Specifically, the generation unit builds a persona of the target person based on keywords and phrases, work style patterns, and communication characteristics provided by the analysis unit. The persona reflects in detail the role and responsibilities in the work of the target person, daily work process, communication style, etc. The generation unit utilizes a generative AI and a large-scale language model (LLM) to integrate this information and generate a persona capable of natural dialogue. For example, the generation AI learns the work style and communication characteristics of the target person and creates prompts to generate appropriate responses. The generation unit uses these prompts to enable the persona of the target person to respond in real time. In addition, the generation unit continuously evaluates the response content of the generated persona and makes adjustments and improvements as necessary. This enables the generation unit to always provide highly accurate personas based on the latest information, helping to improve business efficiency and smooth communication.

[0033] In the response unit, the persona generated by the generation unit responds in real time. For example, the response is, but not limited to, a response in which the generated persona provides an appropriate response to a question about the progress of the project. For example, the response unit provides an appropriate response to a question about the progress of the project. In addition, the response unit can also provide an immediate response when a new person in charge asks about the details of the project. Furthermore, the response unit can minimize interruptions to business and realize efficient handover. For example, when a new person in charge asks about the details of the project, the response unit provides an immediate response from the generated persona, minimizing interruptions to business. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model that allows the generated persona to respond in real time. Specifically, the response unit uses a generation AI or a large-scale language model (LLM) to enable the generated persona to have a natural dialogue. The AI ​​model learns the work style and communication characteristics of the target person and creates a prompt to generate an appropriate response. The response unit uses these prompts to enable the generated persona to respond in real time. Furthermore, the response unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the response content. For example, based on the feedback provided by the user, the learning data of the AI ​​model is updated to improve the accuracy of the response. Furthermore, the response unit can reliably transmit information using multiple communication means. For example, the response unit provides a quick and appropriate response to the user through a chatbot or a voice assistant. As a result, the response unit can provide the user with action instructions quickly and reliably, helping to improve work efficiency and productivity.

[0034] The collection unit can collect at least one of project progress reports, daily work notes, and email exchanges. The collection unit, for example, collects project progress reports. The project progress reports include the progress status, issues, and next steps of the project. The collection unit can also collect daily work notes. The daily work notes include daily tasks, notes, and important points. Furthermore, the collection unit can also collect customer correspondence emails. The customer correspondence emails include inquiries from customers, response contents, feedback, and the like. This makes it possible to efficiently collect information related to the work. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for automatically collecting project progress reports and storing them in a database.

[0035] The analysis unit can analyze the collected information and extract the characteristics of the subject's work style and communication. The analysis unit can use, for example, text mining technology to extract important keywords from a project progress report. Text mining technology is a technology that extracts useful information from a large amount of text data, and is useful for understanding the progress and issues of a project. The analysis unit can also use natural language processing technology to analyze the work style from daily work notes. Natural language processing technology is a technology that analyzes text data and understands the meaning, and is useful for understanding the characteristics of the work style and communication. Furthermore, the analysis unit can extract communication characteristics from customer correspondence emails. For example, the analysis unit uses text mining technology to extract important keywords from a project progress report and analyze the work style. Natural language processing technology is used to analyze the work style from daily work notes and extract communication characteristics. This makes it possible to accurately grasp the work style and communication characteristics of the subject. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can use text mining technology to extract important keywords from project progress reports and analyze the information using an AI model to analyze work styles.

[0036] The generation unit can generate a persona based on the extracted information. The generation unit generates a persona that reflects, for example, the subject's work style and communication characteristics. The persona reflects the subject's work style and communication characteristics and can respond. The generation unit generates a persona that reflects, for example, the subject's work style and communication characteristics. In addition, the generation unit enables the generated persona to respond in real time. For example, the generation unit generates a persona that reflects the subject's work style and communication characteristics and enables real-time response. This makes it possible to generate a persona that reflects the subject's work style and communication characteristics. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a persona using an AI model for generating a persona that reflects the subject's work style and communication characteristics.

[0037] The response unit allows the generated persona to respond in real time. For example, the response unit allows the generated persona to provide an appropriate response to a question about the progress of the project. The question about the progress of the project includes the progress of the project, issues, and next steps. In addition, the response unit can provide an immediate response when a new person in charge asks about the details of the project. When a new person in charge asks about the details of the project, the details of the project, progress, issues, and the like are included. In addition, the response unit can minimize interruptions to business and realize efficient handover. For example, when a new person in charge asks about the details of the project, the response unit allows the generated persona to provide an immediate response, minimizing interruptions to business. This enables a response in real time, and a smooth handover of business can be performed. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can respond using an AI model for the generated persona to respond in real time.

[0038] The response unit can provide an immediate response when the new person in charge asks a question about the details of the project. For example, when the new person in charge asks a question about the details of the project, the generated persona immediately provides an answer. Questions about the details of the project include the progress of the project, issues, and next steps. In addition, the response unit enables the generated persona to respond in real time. For example, when the new person in charge asks a question about the details of the project, the response unit enables the generated persona to immediately provide an answer, minimizing interruptions to business. This allows the new person in charge to quickly grasp the details of the project. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model that allows the generated persona to respond in real time.

[0039] The collection unit can dynamically change the type of information to be collected according to the user's business content. For example, when the user is in charge of project management, the collection unit collects project progress reports and task management data. When the user is in charge of project management, the project progress reports and task management data are included. In addition, when the user is in charge of sales, the collection unit can also collect emails and business negotiation records with customers. When the user is in charge of sales, the collection unit can also collect technical specifications and development notes when the user is in charge of technology development. When the user is in charge of technology development, the collection unit can also collect technical specifications and development notes. This makes it possible to efficiently collect information according to the user's business content. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for dynamically changing the type of information to be collected according to the user's business content.

[0040] The collection unit can set priorities based on the importance of information at the time of collection, and prioritize collection of important information. For example, the collection unit prioritizes collection of project progress reports and minutes of important meetings. The project progress reports and minutes of important meetings include the progress of the project and important decisions. The collection unit can also collect daily business notes and email exchanges with a second priority. The daily business notes and email exchanges include daily tasks and important points. Furthermore, the collection unit can also collect other reference materials and supplementary information last. The reference materials and supplementary information include additional information related to the work. This enables efficient information collection by collecting important information with priority. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for setting priorities based on the importance of information and collecting important information with priority.

[0041] The collection unit can collect highly relevant information preferentially based on the geographical location information of the user at the time of collection. For example, when the user is in the office, the collection unit preferentially collects office-related business materials. When the user is in the office, the office-related business materials are included. In addition, when the user is on a business trip, the collection unit can also preferentially collect information related to the business trip destination. When the user is on a business trip, the information related to the business trip destination is included. Furthermore, when the user is working from home, the collection unit can also preferentially collect information related to remote work. When working from home, the information related to remote work is included. This makes it possible to efficiently collect highly relevant information based on the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for preferentially collecting highly relevant information based on the geographical location information of the user.

[0042] The collection unit can analyze the user's social media activity at the time of collection and collect related information. The collection unit, for example, collects work-related posts shared by the user on social media. The work-related posts shared on social media include information and opinions related to the work. The collection unit can also collect posts from industry experts and companies followed by the user. The posts from the industry experts and companies followed include the latest information and trends in the industry. Furthermore, the collection unit can also collect posts from industry-related groups and communities in which the user participates. The posts from the industry-related groups and communities in which the user participates include the latest information and exchanges of opinions in the industry. This makes it possible to efficiently collect related information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can analyze the user's social media activity and collect information using an AI model for collecting related information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit analyzes the progress report of an important project in detail. The progress report of an important project includes the progress status, issues, and next steps of the project. The analysis unit can also analyze the daily business memo in a simplified manner. The daily business memo includes daily tasks, notes, and important points. Furthermore, the analysis unit can analyze the email exchange by focusing on important contents. The email exchange includes inquiries from customers, response contents, feedback, and the like. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the information using an AI model for adjusting the level of detail of the analysis based on the importance of the information.

[0044] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an algorithm for evaluating the progress of a project to a progress report. The project progress report includes the progress of the project, issues, next steps, and the like. The analysis unit can also apply an algorithm for evaluating the priority of tasks to daily business notes. The daily business notes include daily tasks, notes, important points, and the like. The analysis unit can also apply an algorithm for evaluating the tone of communication to email exchanges. The email exchanges include inquiries from customers, response contents, feedback, and the like. This enables analysis according to the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze information using an AI model for applying different analysis algorithms depending on the category of information.

[0045] The analysis unit can determine the priority of the analysis based on the time of submission of the information during the analysis. For example, the analysis unit preferentially analyzes the latest information. The latest information includes recently collected data and the latest project report. The analysis unit can also analyze old information later. The old information includes past data and old project reports. Furthermore, the analysis unit can also preferentially analyze information whose submission deadline is approaching. The information whose submission deadline is approaching includes tasks and project reports whose deadlines are approaching. This enables efficient analysis by determining the priority of the analysis based on the time of submission of the information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can analyze the information using an AI model for determining the priority of the analysis based on the time of submission of the information.

[0046] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit preferentially analyzes information related to a project progress report. The information related to the project progress report includes the progress status, issues, and next steps of the project. The analysis unit can also analyze information related to daily business notes next. The information related to daily business notes includes daily tasks, notes, and important points. The analysis unit can also analyze information related to email exchanges last. The information related to email exchanges includes inquiries from customers, response contents, feedback, and the like. This allows for efficient analysis by adjusting the order of analysis based on the relevance of information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze information using an AI model for adjusting the order of analysis based on the relevance of information.

[0047] The generation unit can adjust the level of detail of the persona based on the importance of the information at the time of generation. For example, the generation unit generates a persona that reflects information about an important project in detail. The information about the important project includes the progress of the project, issues, and next steps. The generation unit can also generate a persona that simply reflects information about daily work. The information about daily work includes daily tasks, notes, important points, and the like. The generation unit can also generate a persona that reflects information about email exchanges, focusing on important content. The information about email exchanges includes inquiries from customers, response content, feedback, and the like. This allows efficient persona generation by adjusting the level of detail of the persona based on the importance of the information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a persona using an AI model for adjusting the level of detail of the persona based on the importance of the information.

[0048] The generation unit can apply different generation algorithms depending on the category of information at the time of generation. For example, the generation unit applies an algorithm that reflects the progress status to a project progress report. The project progress report includes the project progress status, issues, next steps, etc. The generation unit can also apply an algorithm that reflects the priority of tasks to daily business notes. The daily business notes include daily tasks, notes, important points, etc. The generation unit can also apply an algorithm that reflects the tone of communication to email exchanges. The email exchanges include inquiries from customers, response contents, feedback, etc. This makes it possible to generate personas according to the category of information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate personas using an AI model for applying different generation algorithms depending on the category of information.

[0049] The generation unit can determine the priority of the personas based on the time of information submission at the time of generation. The generation unit, for example, preferentially generates personas based on the latest information. The latest information includes recently collected data and the latest project report. The generation unit can also postpone personas based on old information. The old information includes past data and old project reports. Furthermore, the generation unit can also preferentially generate personas based on information whose submission deadline is approaching. The information whose submission deadline is approaching includes tasks and project reports whose deadlines are approaching. This enables efficient persona generation by determining the priority of personas based on the time of information submission. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate personas using an AI model for determining the priority of personas based on the time of information submission.

[0050] The generation unit can adjust the order of the personas based on the relevance of information at the time of generation. For example, the generation unit preferentially generates a persona related to a project progress report. The personas related to the project progress report include the progress status, issues, and next steps of the project. The generation unit can also generate a persona related to daily business notes next. The personas related to daily business notes include daily tasks, notes, and important points. The generation unit can also generate a persona related to email exchanges last. The personas related to email exchanges include inquiries from customers, response contents, feedback, and the like. This enables efficient persona generation by adjusting the order of personas based on the relevance of information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a persona using an AI model for adjusting the order of personas based on the relevance of information.

[0051] The response unit can adjust the level of detail of the response based on the importance of the question when responding. For example, the response unit provides a detailed response to an important question. Important questions include the progress of the project, issues, and next steps. The response unit can also provide a concise response to a general question. General questions include questions about daily work and basic information. The response unit can also provide a simplified response to a repetitive question by referring to past responses. Repetitive questions include questions that have been asked many times in the past and basic information. This allows for an efficient response by adjusting the level of detail of the response based on the importance of the question. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model for adjusting the level of detail of the response based on the importance of the question.

[0052] The response unit can apply different response algorithms depending on the category of the question when responding. For example, the response unit applies a response algorithm including technical details to a technical question. The technical question includes technical details and specialized information. The response unit can also apply a response algorithm including management procedures to an administrative question. The administrative question includes management procedures and business flows. The response unit can also apply a concise and easy-to-understand response algorithm to a general question. The general question includes questions about daily work and basic information. This enables an appropriate response depending on the category of the question. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can respond using an AI model for applying different response algorithms depending on the category of the question.

[0053] The response unit can determine the priority of the response based on the time of submission of the question when responding. For example, the response unit prioritizes the most recent question. The most recent question includes a question submitted recently and a question related to the most recent project. The response unit can also postpone the response to an older question. The older question includes a question submitted in the past and a question related to an older project. The response unit can also prioritize the response to a question whose submission deadline is approaching. The question whose submission deadline is approaching includes a question related to a task or project whose deadline is approaching. This enables an efficient response by determining the priority of the response based on the time of submission of the question. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can perform a response using an AI model for determining the priority of the response based on the time of submission of the question.

[0054] The response unit can adjust the order of responses based on the relevance of the questions when responding. For example, the response unit prioritizes responses to questions related to the progress of the project. Questions related to the progress of the project include the progress status, issues, and next steps of the project. The response unit can also respond to questions related to daily work next. Questions related to daily work include daily tasks and basic information. Furthermore, the response unit can also respond to questions related to email exchanges last. Questions related to email exchanges include inquiries from customers, response contents, feedback, and the like. This allows for efficient responses by adjusting the order of responses based on the relevance of the questions. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model for adjusting the order of responses based on the relevance of the questions.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The persona generation system may further include a feedback collection unit. The feedback collection unit collects user feedback on the responses of the generated persona and provides the feedback to the analysis unit. For example, a survey is conducted to evaluate whether the user is satisfied with the responses of the persona, and the results are collected. In addition, if the user points out specific improvements to the responses of the persona, the feedback can also be collected. Furthermore, the feedback collection unit can improve the accuracy of the persona by collecting user feedback in real time and providing it to the analysis unit. This allows the persona generation system to generate a more accurate persona that reflects the user feedback.

[0057] The collection unit can adjust the timing of information collection in consideration of the user's work schedule. For example, information collection is refrained from during a time period when the user is in a meeting or concentrating on important tasks, and information is collected during a time period when the user has relatively more time. The frequency and amount of information collection can also be adjusted based on the user's work schedule. Furthermore, the collection unit can dynamically change the information collection plan in response to changes in the user's work schedule. This allows information to be collected efficiently without interfering with the user's work.

[0058] The analysis unit can improve the accuracy of the analysis by referring to the user's past work history. For example, it can analyze past project progress reports and work memos to understand the user's work style and patterns. It can also analyze past customer correspondence emails to extract the user's communication characteristics. Furthermore, the analysis unit can predict the current work situation based on the past work history and reflect it in the analysis results. This makes it possible to more accurately understand the user's work style and communication characteristics and improve the accuracy of the analysis.

[0059] The generation unit can adjust the method of generating a persona based on the business goal of the user. For example, if the user aims to succeed in a project, a persona that focuses on the progress and issues of the project is generated. Also, if the user aims to improve customer satisfaction, a persona that reflects characteristics of customer service can be generated. Furthermore, if the user aims to improve business efficiency, a persona that reflects information useful for improving business efficiency can be generated. In this way, a persona according to the business goal of the user can be generated to support the business.

[0060] The response unit can adjust the way of response according to the user's work environment. For example, if the user is working in an office, the response can be in a formal tone. If the user is working remotely, the response can be in a casual tone. Furthermore, if the user is on a business trip, the response can be quick and concise. This enables an appropriate response according to the user's work environment, and supports the smooth progress of work.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection department collects business documents and email content. Specifically, it collects project progress reports, daily work notes, customer correspondence emails, etc. This data is collected automatically and stored in a database. For example, handwritten notes are scanned and converted into digital data, and emails are automatically retrieved from the mail server. Step 2: The analysis unit analyzes the information collected by the collection unit and extracts the target person's work style and communication characteristics. The analysis is carried out using text mining and natural language processing technology. For example, important keywords are extracted from project progress reports, work style is analyzed from daily work notes, and communication characteristics are extracted from customer correspondence emails. Step 3: The generator generates a persona based on the information extracted by the analyzer. The persona reflects the target person's work style and communication characteristics, and can respond in real time. Step 4: The responder uses the persona generated by the generator to respond in real time. For example, it provides appropriate answers to questions about the progress of the project, and also provides immediate answers when a new person in charge asks about the details of the project. This minimizes interruptions to business operations and ensures efficient handover.

[0063] (Example 2) The persona generation system according to the embodiment of the present invention is a system that generates a persona of a specific person. In this persona generation system, when a task handover is required, the generation AI generates a persona from the task use materials and email contents of the person, and the person can respond. This is expected to improve the productivity of both parties. For example, when a task handover is required, the task use materials and email contents of the target person are collected. For example, project progress reports, daily task notes, email exchanges, etc. are included. This information is input into the generation AI. Next, the generation AI analyzes the collected information and generates a persona of the target person. The generated persona reflects the task style and communication characteristics of the target person, and can respond. For example, the generated persona provides an appropriate response to a question about the progress of the project. Furthermore, the generated persona can respond in real time, and the task handover can be performed smoothly. For example, if a new person in charge asks a question about the details of the project, the generated persona immediately provides an answer. This minimizes interruptions to the task and realizes an efficient handover. This system allows for smooth handover of work duties during maternity leave or leave of absence, improving the productivity of both parties. For example, when an employee on maternity leave returns to work, the generated persona will understand the progress of the work, allowing her to return to work smoothly. Similarly, when an employee on leave returns to work, the generated persona will understand the details of the work, allowing her to return to work quickly. In this way, the persona generation system allows for smooth handover of work duties, improving the productivity of both parties.

[0064] The persona generation system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a response unit. The collection unit collects business use materials and email contents. The business use materials and email contents include, but are not limited to, project progress reports, daily work notes, and customer correspondence emails, for example. The collection unit collects, for example, project progress reports. The collection unit can also collect daily work notes. Furthermore, the collection unit can collect customer correspondence emails. For example, the collection unit automatically collects project progress reports and stores them in a database. Daily work notes can be collected by scanning handwritten notes and converting them into digital data. Customer correspondence emails are automatically obtained from a mail server and stored for analysis. The analysis unit analyzes the information collected by the collection unit and extracts the work style and communication characteristics of the subject. The analysis is performed, for example, using text mining or natural language processing technology, but is not limited to such examples. For example, the analysis unit uses text mining technology to extract important keywords from project progress reports. The analysis unit can also analyze the work style from the daily work memo using natural language processing technology. Furthermore, the analysis unit can extract communication characteristics from customer correspondence emails. For example, the analysis unit uses text mining technology to extract important keywords from the project progress report and analyze the work style. The natural language processing technology is used to analyze the work style from the daily work memo and extract communication characteristics. The generation unit generates a persona based on the information extracted by the analysis unit. The persona reflects, for example, the work style and communication characteristics of the subject, and can respond. The generation unit generates, for example, a persona reflecting the work style and communication characteristics of the subject. Furthermore, the generation unit enables the generated persona to respond in real time. For example, the generation unit generates a persona reflecting the work style and communication characteristics of the subject, and enables responses in real time. The response unit enables the persona generated by the generation unit to respond in real time.The response is, for example, in response to a question about the progress of the project, the generated persona provides an appropriate response, but is not limited to such an example. For example, the response unit provides an appropriate response to a question about the progress of the project by the generated persona. In addition, when a new person in charge asks about the details of the project, the response unit can also provide an answer immediately. Furthermore, the response unit can minimize interruptions to business and realize efficient handover. For example, when a new person in charge asks about the details of the project, the response unit provides an answer immediately to minimize interruptions to business. As a result, the persona generation system according to the embodiment can smoothly perform the handover of business and improve the productivity of both parties. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model for the generated persona to respond in real time.

[0065] The collection unit collects business use materials and email contents. Examples of business use materials and email contents include, but are not limited to, project progress reports, daily business notes, and customer correspondence emails. The collection unit collects, for example, project progress reports. The collection unit can also collect daily business notes. Furthermore, the collection unit can collect customer correspondence emails. For example, the collection unit automatically collects project progress reports and stores them in a database. Daily business notes can be collected by scanning handwritten notes and converting them into digital data. Customer correspondence emails are automatically obtained from a mail server and stored for analysis. The collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and generation unit. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. Furthermore, the collection unit can utilize various APIs and scripts to automate the data collection process. For example, IMAP or POP3 protocols can be used to obtain data from mail servers, and APIs of file servers or cloud storage services can be used to collect business-use materials. This allows the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The analysis unit analyzes the information collected by the collection unit and extracts the characteristics of the target's work style and communication. The analysis is performed, for example, using text mining or natural language processing technology, but is not limited to such examples. For example, the analysis unit uses text mining technology to extract important keywords from project progress reports. The analysis unit can also use natural language processing technology to analyze the work style from daily work notes. Furthermore, the analysis unit can extract communication characteristics from customer correspondence emails. Specifically, using text mining technology, frequently appearing keywords and phrases are extracted from project progress reports to identify the target's work priorities and areas of interest. Using natural language processing technology, the structure and context of sentences are analyzed from daily work notes to clarify the target's work style and work patterns. For customer correspondence emails, sentiment analysis and topic modeling can be used to grasp the target's communication characteristics and customer correspondence trends. By combining these technologies, the analysis unit can analyze the target's work style and communication characteristics from multiple angles and extract more accurate information. Furthermore, the analysis unit can analyze long-term trends and patterns by utilizing past data and statistical information. For example, by analyzing past project reports and work memos, it is possible to grasp changes in work styles at specific times and situations. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term work improvement and risk management, improving the reliability and usefulness of the entire system.

[0067] The generation unit generates a persona based on the information extracted by the analysis unit. The persona reflects, for example, the work style and communication characteristics of the target person, and can respond. The generation unit generates a persona that reflects, for example, the work style and communication characteristics of the target person. In addition, the generation unit enables the generated persona to respond in real time. Specifically, the generation unit builds a persona of the target person based on keywords and phrases, work style patterns, and communication characteristics provided by the analysis unit. The persona reflects in detail the role and responsibilities in the work of the target person, daily work process, communication style, etc. The generation unit utilizes a generative AI and a large-scale language model (LLM) to integrate this information and generate a persona capable of natural dialogue. For example, the generation AI learns the work style and communication characteristics of the target person and creates prompts to generate appropriate responses. The generation unit uses these prompts to enable the persona of the target person to respond in real time. In addition, the generation unit continuously evaluates the response content of the generated persona and makes adjustments and improvements as necessary. This enables the generation unit to always provide highly accurate personas based on the latest information, helping to improve business efficiency and smooth communication.

[0068] In the response unit, the persona generated by the generation unit responds in real time. For example, the response is, but not limited to, a response in which the generated persona provides an appropriate response to a question about the progress of the project. For example, the response unit provides an appropriate response to a question about the progress of the project. In addition, the response unit can also provide an immediate response when a new person in charge asks about the details of the project. Furthermore, the response unit can minimize interruptions to business and realize efficient handover. For example, when a new person in charge asks about the details of the project, the response unit provides an immediate response from the generated persona, minimizing interruptions to business. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model that allows the generated persona to respond in real time. Specifically, the response unit uses a generation AI or a large-scale language model (LLM) to enable the generated persona to have a natural dialogue. The AI ​​model learns the work style and communication characteristics of the target person and creates a prompt to generate an appropriate response. The response unit uses these prompts to enable the generated persona to respond in real time. Furthermore, the response unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the response content. For example, based on the feedback provided by the user, the learning data of the AI ​​model is updated to improve the accuracy of the response. Furthermore, the response unit can reliably transmit information using multiple communication means. For example, the response unit provides a quick and appropriate response to the user through a chatbot or a voice assistant. As a result, the response unit can provide the user with action instructions quickly and reliably, helping to improve work efficiency and productivity.

[0069] The collection unit can collect at least one of project progress reports, daily work notes, and email exchanges. The collection unit, for example, collects project progress reports. The project progress reports include the progress status, issues, and next steps of the project. The collection unit can also collect daily work notes. The daily work notes include daily tasks, notes, and important points. Furthermore, the collection unit can also collect customer correspondence emails. The customer correspondence emails include inquiries from customers, response contents, feedback, and the like. This makes it possible to efficiently collect information related to the work. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for automatically collecting project progress reports and storing them in a database.

[0070] The analysis unit can analyze the collected information and extract the characteristics of the subject's work style and communication. The analysis unit can use, for example, text mining technology to extract important keywords from a project progress report. Text mining technology is a technology that extracts useful information from a large amount of text data, and is useful for understanding the progress and issues of a project. The analysis unit can also use natural language processing technology to analyze the work style from daily work notes. Natural language processing technology is a technology that analyzes text data and understands the meaning, and is useful for understanding the characteristics of the work style and communication. Furthermore, the analysis unit can extract communication characteristics from customer correspondence emails. For example, the analysis unit uses text mining technology to extract important keywords from a project progress report and analyze the work style. Natural language processing technology is used to analyze the work style from daily work notes and extract communication characteristics. This makes it possible to accurately grasp the work style and communication characteristics of the subject. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can use text mining technology to extract important keywords from project progress reports and analyze the information using an AI model to analyze work styles.

[0071] The generation unit can generate a persona based on the extracted information. The generation unit generates a persona that reflects, for example, the subject's work style and communication characteristics. The persona reflects the subject's work style and communication characteristics and can respond. The generation unit generates a persona that reflects, for example, the subject's work style and communication characteristics. In addition, the generation unit enables the generated persona to respond in real time. For example, the generation unit generates a persona that reflects the subject's work style and communication characteristics and enables real-time response. This makes it possible to generate a persona that reflects the subject's work style and communication characteristics. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a persona using an AI model for generating a persona that reflects the subject's work style and communication characteristics.

[0072] The response unit allows the generated persona to respond in real time. For example, the response unit allows the generated persona to provide an appropriate response to a question about the progress of the project. The question about the progress of the project includes the progress of the project, issues, and next steps. In addition, the response unit can provide an immediate response when a new person in charge asks about the details of the project. When a new person in charge asks about the details of the project, the details of the project, progress, issues, and the like are included. In addition, the response unit can minimize interruptions to business and realize efficient handover. For example, when a new person in charge asks about the details of the project, the response unit allows the generated persona to provide an immediate response, minimizing interruptions to business. This enables a response in real time, and a smooth handover of business can be performed. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can respond using an AI model for the generated persona to respond in real time.

[0073] The response unit can provide an immediate response when the new person in charge asks a question about the details of the project. For example, when the new person in charge asks a question about the details of the project, the generated persona immediately provides an answer. Questions about the details of the project include the progress of the project, issues, and next steps. In addition, the response unit enables the generated persona to respond in real time. For example, when the new person in charge asks a question about the details of the project, the response unit enables the generated persona to immediately provide an answer, minimizing interruptions to business. This allows the new person in charge to quickly grasp the details of the project. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model that allows the generated persona to respond in real time.

[0074] The collection unit can estimate the user's emotion and adjust the timing of information collection based on the estimated user's emotion. For example, when the user is stressed, the collection unit reduces the frequency of information collection to reduce the burden on the user. When the user is stressed, the collection unit reduces the frequency of information collection to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can also increase the frequency of information collection and collect detailed data. When the user is relaxed, the collection unit increases the frequency of information collection and collects detailed data. Furthermore, when the user is in a hurry, the collection unit can also collect important information preferentially and process it quickly. When the user is in a hurry, the collection unit collects important information preferentially and processes it quickly. In this way, the burden can be reduced by adjusting the timing of information collection according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can collect information using an AI model for inferring a user's emotions and adjusting the timing of information collection based on the inferred user's emotions.

[0075] The collection unit can dynamically change the type of information to be collected according to the user's business content. For example, when the user is in charge of project management, the collection unit collects project progress reports and task management data. When the user is in charge of project management, the project progress reports and task management data are included. In addition, when the user is in charge of sales, the collection unit can also collect emails and business negotiation records with customers. When the user is in charge of sales, the collection unit can also collect technical specifications and development notes when the user is in charge of technology development. When the user is in charge of technology development, the collection unit can also collect technical specifications and development notes. This makes it possible to efficiently collect information according to the user's business content. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for dynamically changing the type of information to be collected according to the user's business content.

[0076] The collection unit can set priorities based on the importance of information at the time of collection, and prioritize collection of important information. For example, the collection unit prioritizes collection of project progress reports and minutes of important meetings. The project progress reports and minutes of important meetings include the progress of the project and important decisions. The collection unit can also collect daily business notes and email exchanges with a second priority. The daily business notes and email exchanges include daily tasks and important points. Furthermore, the collection unit can also collect other reference materials and supplementary information last. The reference materials and supplementary information include additional information related to the work. This enables efficient information collection by collecting important information with priority. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for setting priorities based on the importance of information and collecting important information with priority.

[0077] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit prioritizes collecting important information to reduce the burden on the user. When the user is stressed, the collection unit prioritizes collecting important information to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can also collect detailed information to increase the comprehensiveness of the data. When the user is relaxed, the collection unit collects detailed information to increase the comprehensiveness of the data. Furthermore, when the user is in a hurry, the collection unit can also prioritize information that can be collected quickly. When the user is in a hurry, the collection unit prioritizes information that can be collected quickly. This enables efficient information collection by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can collect information using an AI model for inferring a user's emotions and prioritizing the information to be collected based on the inferred user's emotions.

[0078] The collection unit can collect highly relevant information preferentially based on the geographical location information of the user at the time of collection. For example, when the user is in the office, the collection unit preferentially collects office-related business materials. When the user is in the office, the office-related business materials are included. In addition, when the user is on a business trip, the collection unit can also preferentially collect information related to the business trip destination. When the user is on a business trip, the information related to the business trip destination is included. Furthermore, when the user is working from home, the collection unit can also preferentially collect information related to remote work. When working from home, the information related to remote work is included. This makes it possible to efficiently collect highly relevant information based on the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for preferentially collecting highly relevant information based on the geographical location information of the user.

[0079] The collection unit can analyze the user's social media activity at the time of collection and collect related information. The collection unit, for example, collects work-related posts shared by the user on social media. The work-related posts shared on social media include information and opinions related to the work. The collection unit can also collect posts from industry experts and companies followed by the user. The posts from the industry experts and companies followed include the latest information and trends in the industry. Furthermore, the collection unit can also collect posts from industry-related groups and communities in which the user participates. The posts from the industry-related groups and communities in which the user participates include the latest information and exchanges of opinions in the industry. This makes it possible to efficiently collect related information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can analyze the user's social media activity and collect information using an AI model for collecting related information.

[0080] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. For example, when the user is stressed, the analysis unit reduces the level of detail of the analysis and provides the result quickly. When the user is stressed, the analysis unit reduces the level of detail of the analysis and provides the result quickly. In addition, when the user is relaxed, the analysis unit can perform a detailed analysis and provide a comprehensive result. When the user is relaxed, the analysis unit can perform a detailed analysis and provide a comprehensive result. Furthermore, when the user is in a hurry, the analysis unit can perform an analysis focused on important points. When the user is in a hurry, the analysis unit performs an analysis focused on important points. This allows efficient analysis by adjusting the analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can analyze the information using an AI model to estimate a user's emotions and adjust the analysis method based on the estimated user's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit analyzes the progress report of an important project in detail. The progress report of an important project includes the progress status, issues, and next steps of the project. The analysis unit can also analyze the daily business memo in a simplified manner. The daily business memo includes daily tasks, notes, and important points. Furthermore, the analysis unit can analyze the email exchange by focusing on important contents. The email exchange includes inquiries from customers, response contents, feedback, and the like. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the information using an AI model for adjusting the level of detail of the analysis based on the importance of the information.

[0082] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an algorithm for evaluating the progress of a project to a progress report. The project progress report includes the progress of the project, issues, next steps, and the like. The analysis unit can also apply an algorithm for evaluating the priority of tasks to daily business notes. The daily business notes include daily tasks, notes, important points, and the like. The analysis unit can also apply an algorithm for evaluating the tone of communication to email exchanges. The email exchanges include inquiries from customers, response contents, feedback, and the like. This enables analysis according to the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze information using an AI model for applying different analysis algorithms depending on the category of information.

[0083] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. For example, when the user is stressed, the analysis unit prioritizes analysis of important information. When the user is stressed, the analysis unit prioritizes analysis of important information. In addition, when the user is relaxed, the analysis unit can also prioritize analysis of detailed information. When the user is relaxed, the analysis unit prioritizes analysis of detailed information. In addition, when the user is in a hurry, the analysis unit can also prioritize information that can be analyzed quickly. When the user is in a hurry, the analysis unit prioritizes information that can be analyzed quickly. In this way, efficient analysis is possible by determining the analysis priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can analyze information using an AI model for estimating the user's emotion and determining the analysis priority based on the estimated user's emotion.

[0084] The analysis unit can determine the priority of the analysis based on the time of submission of the information during the analysis. For example, the analysis unit preferentially analyzes the latest information. The latest information includes recently collected data and the latest project report. The analysis unit can also analyze old information later. The old information includes past data and old project reports. Furthermore, the analysis unit can also preferentially analyze information whose submission deadline is approaching. The information whose submission deadline is approaching includes tasks and project reports whose deadlines are approaching. This enables efficient analysis by determining the priority of the analysis based on the time of submission of the information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can analyze the information using an AI model for determining the priority of the analysis based on the time of submission of the information.

[0085] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit preferentially analyzes information related to a project progress report. The information related to the project progress report includes the progress status, issues, and next steps of the project. The analysis unit can also analyze information related to daily business notes next. The information related to daily business notes includes daily tasks, notes, and important points. The analysis unit can also analyze information related to email exchanges last. The information related to email exchanges includes inquiries from customers, response contents, feedback, and the like. This allows for efficient analysis by adjusting the order of analysis based on the relevance of information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze information using an AI model for adjusting the order of analysis based on the relevance of information.

[0086] The generation unit can estimate the user's emotion and adjust the persona generation method based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a detailed persona. When the user is relaxed, the generation unit generates a detailed persona. In addition, when the user is in a hurry, the generation unit can also generate a simplified persona. When the user is in a hurry, the generation unit generates a simplified persona. Furthermore, when the user is stressed, the generation unit can also generate a persona that focuses on important points. When the user is stressed, the generation unit generates a persona that focuses on important points. This allows efficient persona generation by adjusting the persona generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can generate a persona using an AI model for inferring a user's emotions and adjusting a method for generating the persona based on the inferred user's emotions.

[0087] The generation unit can adjust the level of detail of the persona based on the importance of the information at the time of generation. For example, the generation unit generates a persona that reflects information about an important project in detail. The information about the important project includes the progress of the project, issues, and next steps. The generation unit can also generate a persona that simply reflects information about daily work. The information about daily work includes daily tasks, notes, important points, and the like. The generation unit can also generate a persona that reflects information about email exchanges, focusing on important content. The information about email exchanges includes inquiries from customers, response content, feedback, and the like. This allows efficient persona generation by adjusting the level of detail of the persona based on the importance of the information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a persona using an AI model for adjusting the level of detail of the persona based on the importance of the information.

[0088] The generation unit can apply different generation algorithms depending on the category of information at the time of generation. For example, the generation unit applies an algorithm that reflects the progress status to a project progress report. The project progress report includes the project progress status, issues, next steps, etc. The generation unit can also apply an algorithm that reflects the priority of tasks to daily business notes. The daily business notes include daily tasks, notes, important points, etc. The generation unit can also apply an algorithm that reflects the tone of communication to email exchanges. The email exchanges include inquiries from customers, response contents, feedback, etc. This makes it possible to generate personas according to the category of information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate personas using an AI model for applying different generation algorithms depending on the category of information.

[0089] The generation unit can estimate the user's emotion and determine the priority of the personas to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit generates an important persona with priority. When the user is stressed, the generation unit generates an important persona with priority. In addition, when the user is relaxed, the generation unit can also generate a detailed persona with priority. When the user is relaxed, the generation unit generates a detailed persona with priority. Furthermore, when the user is in a hurry, the generation unit can also prioritize a persona that can be generated quickly. When the user is in a hurry, the generation unit prioritizes a persona that can be generated quickly. In this way, by determining the priority of the personas according to the user's emotion, efficient persona generation is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can generate personas using an AI model for estimating a user's emotions and determining a priority order for the personas to be generated based on the estimated user's emotions.

[0090] The generation unit can determine the priority of the personas based on the time of information submission at the time of generation. The generation unit, for example, preferentially generates personas based on the latest information. The latest information includes recently collected data and the latest project report. The generation unit can also postpone personas based on old information. The old information includes past data and old project reports. Furthermore, the generation unit can also preferentially generate personas based on information whose submission deadline is approaching. The information whose submission deadline is approaching includes tasks and project reports whose deadlines are approaching. This enables efficient persona generation by determining the priority of personas based on the time of information submission. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate personas using an AI model for determining the priority of personas based on the time of information submission.

[0091] The generation unit can adjust the order of the personas based on the relevance of information at the time of generation. For example, the generation unit preferentially generates a persona related to a project progress report. The personas related to the project progress report include the progress status, issues, and next steps of the project. The generation unit can also generate a persona related to daily business notes next. The personas related to daily business notes include daily tasks, notes, and important points. The generation unit can also generate a persona related to email exchanges last. The personas related to email exchanges include inquiries from customers, response contents, feedback, and the like. This enables efficient persona generation by adjusting the order of personas based on the relevance of information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a persona using an AI model for adjusting the order of personas based on the relevance of information.

[0092] The response unit can estimate the user's emotion and adjust the response method based on the estimated user's emotion. For example, when the user is nervous, the response unit responds in a calm tone. When the user is nervous, the response unit responds in a calm tone. In addition, when the user is relaxed, the response unit can also respond in a friendly tone. When the user is relaxed, the response unit responds in a friendly tone. Furthermore, when the user is in a hurry, the response unit can also provide a quick and concise response. When the user is in a hurry, the response unit provides a quick and concise response. This allows an appropriate response by adjusting the response method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can estimate the user's emotion and respond using an AI model for adjusting the response method based on the estimated user's emotion.

[0093] The response unit can adjust the level of detail of the response based on the importance of the question when responding. For example, the response unit provides a detailed response to an important question. Important questions include the progress of the project, issues, and next steps. The response unit can also provide a concise response to a general question. General questions include questions about daily work and basic information. The response unit can also provide a simplified response to a repetitive question by referring to past responses. Repetitive questions include questions that have been asked many times in the past and basic information. This allows for an efficient response by adjusting the level of detail of the response based on the importance of the question. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model for adjusting the level of detail of the response based on the importance of the question.

[0094] The response unit can apply different response algorithms depending on the category of the question when responding. For example, the response unit applies a response algorithm including technical details to a technical question. The technical question includes technical details and specialized information. The response unit can also apply a response algorithm including management procedures to an administrative question. The administrative question includes management procedures and business flows. The response unit can also apply a concise and easy-to-understand response algorithm to a general question. The general question includes questions about daily work and basic information. This enables an appropriate response depending on the category of the question. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can respond using an AI model for applying different response algorithms depending on the category of the question.

[0095] The response unit can estimate the user's emotion and determine the priority of the response based on the estimated user's emotion. For example, when the user is stressed, the response unit prioritizes responses to important questions. When the user is stressed, the response unit prioritizes responses to important questions. In addition, when the user is relaxed, the response unit can also prioritize responses to detailed questions. When the user is relaxed, the response unit prioritizes responses to detailed questions. Furthermore, when the user is in a hurry, the response unit can also prioritize questions that can be answered quickly. When the user is in a hurry, the response unit prioritizes questions that can be answered quickly. This enables efficient responses by determining the priority of responses according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can respond using an AI model for inferring a user's emotions and prioritizing responses based on the inferred user's emotions.

[0096] The response unit can determine the priority of the response based on the time of submission of the question when responding. For example, the response unit prioritizes the most recent question. The most recent question includes a question submitted recently and a question related to the most recent project. The response unit can also postpone the response to an older question. The older question includes a question submitted in the past and a question related to an older project. The response unit can also prioritize the response to a question whose submission deadline is approaching. The question whose submission deadline is approaching includes a question related to a task or project whose deadline is approaching. This enables an efficient response by determining the priority of the response based on the time of submission of the question. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can perform a response using an AI model for determining the priority of the response based on the time of submission of the question.

[0097] The response unit can adjust the order of responses based on the relevance of the questions when responding. For example, the response unit prioritizes responses to questions related to the progress of the project. Questions related to the progress of the project include the progress status, issues, and next steps of the project. The response unit can also respond to questions related to daily work next. Questions related to daily work include daily tasks and basic information. Furthermore, the response unit can also respond to questions related to email exchanges last. Questions related to email exchanges include inquiries from customers, response contents, feedback, and the like. This allows for efficient responses by adjusting the order of responses based on the relevance of the questions. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can respond using an AI model for adjusting the order of responses based on the relevance of the questions.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The persona generation system may further include a feedback collection unit. The feedback collection unit collects user feedback on the responses of the generated persona and provides the feedback to the analysis unit. For example, a survey is conducted to evaluate whether the user is satisfied with the responses of the persona, and the results are collected. In addition, if the user points out specific improvements to the responses of the persona, the feedback can also be collected. Furthermore, the feedback collection unit can improve the accuracy of the persona by collecting user feedback in real time and providing it to the analysis unit. This allows the persona generation system to generate a more accurate persona that reflects the user feedback.

[0100] The collection unit can adjust the timing of information collection in consideration of the user's work schedule. For example, information collection is refrained from during a time period when the user is in a meeting or concentrating on important tasks, and information is collected during a time period when the user has relatively more time. The frequency and amount of information collection can also be adjusted based on the user's work schedule. Furthermore, the collection unit can dynamically change the information collection plan in response to changes in the user's work schedule. This allows information to be collected efficiently without interfering with the user's work.

[0101] The analysis unit can improve the accuracy of the analysis by referring to the user's past work history. For example, it can analyze past project progress reports and work memos to understand the user's work style and patterns. It can also analyze past customer correspondence emails to extract the user's communication characteristics. Furthermore, the analysis unit can predict the current work situation based on the past work history and reflect it in the analysis results. This makes it possible to more accurately understand the user's work style and communication characteristics and improve the accuracy of the analysis.

[0102] The generation unit can adjust the method of generating a persona based on the business goal of the user. For example, if the user aims to succeed in a project, a persona that focuses on the progress and issues of the project is generated. Also, if the user aims to improve customer satisfaction, a persona that reflects characteristics of customer service can be generated. Furthermore, if the user aims to improve business efficiency, a persona that reflects information useful for improving business efficiency can be generated. In this way, a persona according to the business goal of the user can be generated to support the business.

[0103] The response unit can adjust the way of response according to the user's work environment. For example, if the user is working in an office, the response can be in a formal tone. If the user is working remotely, the response can be in a casual tone. Furthermore, if the user is on a business trip, the response can be quick and concise. This enables an appropriate response according to the user's work environment, and supports the smooth progress of work.

[0104] The collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, only important information is collected, reducing the burden on the user. When the user is relaxed, detailed information can be collected to increase the comprehensiveness of the data. Furthermore, when the user is in a hurry, information that can be collected quickly can be prioritized. This allows efficient information collection by adjusting the type of information to be collected according to the user's emotions.

[0105] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, important information is prioritized in the analysis. Also, if the user is relaxed, detailed information can be prioritized in the analysis. Furthermore, if the user is in a hurry, information that can be analyzed quickly can be prioritized. In this way, efficient analysis is possible by determining the analysis priority according to the user's emotions.

[0106] The generation unit can estimate the user's emotions and adjust the persona generation method based on the estimated user's emotions. For example, if the user is relaxed, a detailed persona can be generated. Also, if the user is in a hurry, a simplified persona can be generated. Furthermore, if the user is stressed, a persona focused on important points can be generated. This allows for efficient persona generation by adjusting the persona generation method according to the user's emotions.

[0107] The response unit can estimate the user's emotion and adjust the response method based on the estimated user's emotion. For example, if the user is nervous, the response can be made in a calm tone. If the user is relaxed, the response can be made in a friendly tone. Furthermore, if the user is in a hurry, the response can be made quickly and concisely. In this way, an appropriate response can be made by adjusting the response method according to the user's emotion.

[0108] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated user's emotions. For example, if the user is stressed, important questions can be given priority in response. Also, if the user is relaxed, detailed questions can be given priority in response. Furthermore, if the user is in a hurry, questions that can be answered quickly can be given priority. In this way, efficient responses can be achieved by determining the priority of responses according to the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection department collects business documents and email content. Specifically, it collects project progress reports, daily work notes, customer correspondence emails, etc. This data is collected automatically and stored in a database. For example, handwritten notes are scanned and converted into digital data, and emails are automatically retrieved from the mail server. Step 2: The analysis unit analyzes the information collected by the collection unit and extracts the target person's work style and communication characteristics. The analysis is carried out using text mining and natural language processing technology. For example, important keywords are extracted from project progress reports, work style is analyzed from daily work notes, and communication characteristics are extracted from customer correspondence emails. Step 3: The generator generates a persona based on the information extracted by the analyzer. The persona reflects the target person's work style and communication characteristics, and can respond in real time. Step 4: The responder uses the persona generated by the generator to respond in real time. For example, it provides appropriate answers to questions about the progress of the project, and also provides immediate answers when a new person in charge asks about the details of the project. This minimizes interruptions to business operations and ensures efficient handover.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and response unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects business use materials and mail contents using the camera 42 and microphone 38B of the smart device 14, and transmits them to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a persona based on the analysis result. The response unit is realized, for example, by the control unit 46A of the smart device 14, and the generated persona responds in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and response unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects business-use materials and mail contents using the camera 42 and microphone 238 of the smart glasses 214, and transmits them to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a persona based on the analysis result. The response unit is realized, for example, by the control unit 46A of the smart glasses 214, and the generated persona responds in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and response unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects business use materials and mail contents using the camera 42 and microphone 238 of the headset type terminal 314, and transmits them to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a persona based on the analysis result. The response unit is realized, for example, by the control unit 46A of the headset type terminal 314, and the generated persona responds in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and response unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects business use materials and mail contents using the camera 42 and microphone 238 of the robot 414, and transmits them to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a persona based on the analysis result. The response unit is realized, for example, by the control unit 46A of the robot 414, and the generated persona responds in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection department that collects business-use materials and email content; an analysis unit that analyzes the information collected by the collection unit and extracts characteristics of the subject's work style and communication; a generation unit that generates a persona based on the information extracted by the analysis unit; A response unit in which the persona generated by the generation unit responds in real time. A system characterized by: (Appendix 2) The system described in Appendix 1, characterized in that the collection unit collects at least one of the following information: project progress reports, daily work notes, and email exchanges. (Appendix 3) The analysis unit is Analyze the collected information and extract the subjects' work style and communication characteristics. 2. The system of claim 1, (Appendix 4) The generation unit is Generate personas based on extracted information 2. The system of claim 1, (Appendix 5) The response unit: The generated persona responds in real time 2. The system of claim 1, (Appendix 6) The response unit: Provide immediate answers when new contacts ask questions about project details 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Dynamically change the type of information collected based on the user's job duties 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting information, set priorities based on the importance of the information, and collect important information first. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: At the time of collection, prioritize collection of information that is more relevant based on the user's geographic location 2. The system of claim 1, (Appendix 12) The collecting unit includes: At the time of collection, we analyze your social media activity and collect related information. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the information 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when information was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of information 2. The system of claim 1, (Appendix 19) The generation unit is Infer user emotions and adjust the persona generation method based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The generation unit is During generation, adjust the detail of the persona based on the importance of the information 2. The system of claim 1, (Appendix 21) The generation unit is During generation, different generation algorithms are applied depending on the category of information. 2. The system of claim 1, (Appendix 22) The generation unit is Estimate user emotions and prioritize personas based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The generation unit is During generation, prioritize personas based on when information was submitted 2. The system of claim 1, (Appendix 24) The generation unit is During generation, adjust the order of personas based on the relevance of the information 2. The system of claim 1, (Appendix 25) The response unit: Inferring a user's emotions and adjusting the response method based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The response unit: When responding, adjust the detail of your response based on the importance of the question 2. The system of claim 1, (Appendix 27) The response unit: When answering, apply different answering algorithms depending on the question category 2. The system of claim 1, (Appendix 28) The response unit: Inferring user emotions and prioritizing responses based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The response unit: When responding, prioritize responses based on when the question was submitted 2. The system of claim 1, (Appendix 30) The response unit: As you respond, adjust the order of responses based on the relevance of the question 2. The system of claim 1, [Explanation of symbols]

[0183] 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-use materials and email content; an analysis unit that analyzes the information collected by the collection unit and extracts characteristics of the subject's work style and communication; a generation unit that generates a persona based on the information extracted by the analysis unit; A response unit in which the persona generated by the generation unit responds in real time. A system characterized by:

2. The collecting unit includes: Collect at least one of the following information: project status reports, daily work notes, and / or email correspondence 2. The system of claim 1 .

3. The response unit: Provide immediate answers when new contacts ask questions about project details 2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions.

2. The system of claim 1 .

5. The collecting unit includes: Dynamically change the type of information collected based on the user's job duties 2. The system of claim 1 .

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