system

Abstract

To provide a system according to an embodiment that implements an operation of a user instead of the user efficiently.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; and a vicarious operation unit. The collection unit is configured to collect a PC of a user, or operation data thereof. The analysis unit is configured to analyze the data collected by the collection unit, and learn a style of the user. The vicarious operation unit is configured to implement the operation of the user instead of the user on the basis of data learned by the analysis 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 is difficult to efficiently perform tasks on behalf of users, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently carry out the work of a user on behalf of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a business agent. The collection unit collects PC or business data of a user. The analysis unit analyzes the data collected by the collection unit and learns the user's style. The business agent performs business on behalf of the user based on the data learned by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently carry out the work of a user on behalf of the user. [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, an aperture, and a 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 business proxy system according to the embodiment of the present invention is a system that uses a generation AI to perform a user's business. This business proxy system has the generation AI learn the user's PC and business data, and can reply to emails, create proposals, attend meetings, and so on, as the user's avatar. This system allows the generation AI to perform work on behalf of the user on one day a week or on holidays. For example, the business proxy system has the generation AI learn the user's PC and business data. At this time, data such as the user's email history, proposal templates, and meeting minutes are collected and analyzed by the generation AI. For example, by having the content of emails sent by the user in the past and the format of proposals created by the user learn, the generation AI can reply to emails and create proposals that match the user's style. Next, the generation AI performs the user's business based on the data learned. For example, when a user requests a reply to an email, the generation AI generates an appropriate reply by referring to the past email history. Also, when a user requests the creation of a proposal, the generation AI creates the proposal based on the learned template. Furthermore, when a user requests attendance at a meeting, the generation AI can generate remarks based on the minutes of the meeting and participate in the meeting. This system allows the generation AI to work on behalf of the user one day a week or on holidays. For example, if the user takes a weekend off, the generation AI can reply to emails and create proposals on the user's behalf, allowing the user to spend the holiday without worry. Even if the user is busy during the weekdays, the generation AI can attend meetings on the user's behalf, reducing the user's burden. In this way, the use of the generation AI can make the user's work more efficient and reduce the burden. Users can work more efficiently by requesting tasks that suit their own style from the generation AI. This allows the business proxy system to efficiently proxy the user's work.

[0029] The business agent system according to the embodiment includes a collection unit, an analysis unit, and a business agent unit. The collection unit collects the user's PC and business data. The user's PC and business data include, for example, email history, proposal templates, and meeting minutes, but are not limited to such examples. The collection unit, for example, collects the user's email history and analyzes the contents of emails sent in the past. The collection unit can also collect proposal templates and learn the format of proposals created by the user in the past. Furthermore, the collection unit can collect meeting minutes and analyze the contents of the meetings. For example, the collection unit automatically collects the user's email history and filters based on a specific period or a specific sender / recipient. The proposal templates are collected in a specific format or related to a project. The meeting minutes are collected based on a specific meeting or topic. The analysis unit analyzes the data collected by the collection unit and learns the user's style. The analysis is performed using, for example, natural language processing technology or machine learning algorithms, but are not limited to such examples. For example, the analysis unit learns the user's way of writing emails and the way of proceeding with work, and performs analysis according to the user's style. The analysis unit can also learn the user's preferred expression method and the progress pattern of work. For example, the analysis unit uses natural language processing technology to analyze the user's way of writing emails and extracts specific keywords and phrases. A machine learning algorithm learns the progress pattern of the user's work and determines the priority of specific tasks. The business agent performs the user's work on behalf of the user based on the data learned by the analysis unit. The business agent includes, for example, replying to emails, creating proposals, attending meetings, etc., but is not limited to such examples. For example, the business agent generates a reply to the user's email and makes an appropriate reply by referring to past email history. The business agent can also create a proposal and create the proposal based on the learned template. Furthermore, the business agent can also attend a meeting and generate remarks based on the minutes of the meeting. For example, the business agent automatically generates replies to user emails and responds based on a specific template or timing, and creates proposals based on a specific format or content structure.Attendance at a meeting is based on a specific agenda and the content of remarks, so that the business agent system according to the embodiment can efficiently act on behalf of the user's business.

[0030] The collection unit collects the user's PC and business data. The user's PC and business data include, but are not limited to, for example, email history, proposal templates, and meeting minutes. For example, the collection unit collects the user's email history and analyzes the contents of emails sent in the past. The collection unit can also collect proposal templates and learn the format of proposals created by the user in the past. Furthermore, the collection unit can collect meeting minutes and analyze the contents of the meetings. For example, the collection unit automatically collects the user's email history and filters based on a specific period or specific sender / recipient. The proposal templates are collected by collecting those related to a specific format or project. The meeting minutes are collected based on a specific meeting or topic. In order to efficiently collect these data, the collection unit can install dedicated software on the user's PC and set it to collect data periodically. For example, the collection of email history is performed by linking with the user's email client and automatically acquiring data based on a specific folder or label. The proposal templates are collected by scanning the user's file system and collecting based on a specific file format or keyword. The minutes of the meeting are linked with the user's calendar application and data is automatically acquired based on the meeting schedule. This allows the collection unit to efficiently and accurately collect the user's business data and provide it to the analysis unit and the business agent unit. Furthermore, the collection unit can centrally manage the collected data and perform data cleansing processing to prevent data duplication and loss. For example, to prevent the same email from being collected multiple times, a duplication check is performed based on the email header information. For proposal templates, the file hash value is calculated and duplicate files are eliminated. For the minutes of the meeting, a duplication check is performed based on the date and time of the meeting and participant information. This allows the collection unit to provide accurate and consistent data and improve the reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit and learns the user's style. The analysis is performed, for example, using natural language processing technology or machine learning algorithms, but is not limited to such examples. For example, the analysis unit learns the user's way of writing emails and how to proceed with work, and performs analysis according to the user's style. The analysis unit can also learn the user's preferred expression method and work progress pattern. For example, the analysis unit uses natural language processing technology to analyze the user's way of writing emails and extracts specific keywords and phrases. The machine learning algorithm learns the user's work progress pattern and determines the priority of specific tasks. The analysis unit combines these technologies to comprehensively understand the user's work style and build a foundation for realizing optimal work delegation. Specifically, the analysis unit uses natural language processing technology to extract frequently occurring keywords and phrases from the user's email history and grasp the user's communication style. For example, it learns greetings, closing words, specific expressions, etc. that the user often uses, and applies them when replying to emails. In addition, the analysis unit uses a machine learning algorithm to analyze the progress pattern of the user's work and grasp the priority and progress of a specific task. For example, it learns in what order the user processes tasks and which tasks the user spends most time on, and proposes an optimal task schedule when acting on behalf of the user. Furthermore, the analysis unit can propose ways to improve the efficiency of work and improve points based on the user's work data. For example, it analyzes past email history and meeting minutes to identify frequently occurring problems and bottlenecks and propose improvement measures. It can also propose the introduction of more efficient work processes and tools based on the user's work style. In this way, the analysis unit can deeply understand the user's work and provide a foundation for the business agent unit to optimally act on behalf of the user.

[0032] The business agent performs the business of the user on the basis of the data learned by the analysis unit. The business agent includes, for example, replying to emails, creating a proposal, attending a meeting, and the like, but is not limited to such examples. For example, the business agent generates a reply to the user's email and performs an appropriate reply by referring to past email history. The business agent can also create a proposal and create the proposal based on the learned template. Furthermore, the business agent can attend a meeting and generate remarks based on the minutes of the meeting. For example, the business agent automatically generates a reply to the user's email and replies based on a specific template and timing of the reply. The proposal is created based on a specific format and content structure. Attendance at a meeting is based on a specific agenda and remarks. In order to efficiently perform these tasks, the business agent utilizes the data provided by the analysis unit to realize business agent matching the user's style. Specifically, in generating a reply to an email, the business agent utilizes keywords and phrases extracted by the analysis unit to generate a reply matching the user's communication style. For example, the system automatically generates a reply incorporating greetings and closing phrases frequently used by the user, realizing a reply that fits the user's style. In creating a proposal, the system automatically generates a proposal that matches the style of the user's past proposals based on the templates and formats learned by the analysis unit. For example, a template related to a specific project is used, and the content structure and expression method are adjusted to match the user's style. In attending a meeting, the system generates remarks on behalf of the user based on the minutes and remarks provided by the analysis unit, supporting the progress of the meeting. For example, the system automatically generates the user's opinions and suggestions on a specific topic, contributing to the progress of the meeting. In this way, the business agent unit can efficiently and accurately perform the user's business and reduce the user's workload. Furthermore, the business agent unit can collect user feedback and continuously improve the accuracy and effectiveness of business agent services. For example, the system reviews the content of email replies and the structure of proposals based on user feedback, realizing business agent services that better fit the user's style.The business agent also has a scheduling function for processing multiple tasks simultaneously, enabling efficient management of user tasks. This allows the business agent to comprehensively support user tasks and contribute to improving the efficiency and productivity of tasks.

[0033] The collection unit can collect data on the user's email history, proposal templates, and meeting minutes. The collection unit, for example, collects the user's email history and filters it based on a specific period or a specific sender / recipient. For example, the collection unit collects email history from the past year and extracts emails containing specific keywords. The collection unit can also collect proposal templates and collect those related to a specific format or project. For example, the collection unit collects proposal templates used in the past and preferentially collects templates related to a specific project. Furthermore, the collection unit can collect meeting minutes and collect them based on a specific meeting or topic. For example, the collection unit collects minutes of past meetings and extracts remarks related to a specific topic. This allows the collection unit to efficiently collect data related to the user's work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's email history into a generation AI and analyze the contents of the email.

[0034] The analysis unit can analyze the collected data and learn the user's style. The analysis unit can analyze the user's email writing style, for example, using natural language processing technology. For example, the analysis unit can extract specific keywords and phrases and learn the user's email style. The analysis unit can also learn the user's work progress pattern using a machine learning algorithm. For example, the analysis unit can analyze the user's work progress pattern and determine the priority of a specific task. Furthermore, the analysis unit can also learn the user's preferred expression method and work progress pattern. For example, the analysis unit can analyze the user's email writing style and work progress pattern and extract specific keywords and phrases. This enables the analysis unit to perform work on behalf of the user in accordance with the user's style. 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 input the collected data into a generation AI and learn the user's style.

[0035] The business agent unit can generate a reply to an email based on the learned data. The business agent unit, for example, generates a reply to a user's email and makes an appropriate reply by referring to past email history. For example, the business agent unit makes a reply based on a specific template or timing of the reply. The business agent unit can also generate a reply to an email that matches the user's style. For example, the business agent unit learns how the user writes and expresses emails, and generates a reply using specific keywords and phrases. This enables the business agent unit to reply to an email that matches the user's style. Some or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent unit can input the user's email history into a generation AI and generate a reply to an email.

[0036] The business agent can create a proposal based on the learned data. The business agent creates, for example, a proposal for a user, and creates the proposal based on the learned template. For example, the business agent creates a proposal based on a specific format or content configuration. The business agent can also create a proposal that matches the user's style. For example, the business agent learns how the user writes and expresses a proposal, and creates a proposal using specific keywords and phrases. This enables the business agent to create a proposal that matches the user's style. A part or all of the above-mentioned processing in the business agent may be performed, for example, using AI, or may be performed without using AI. For example, the business agent can input a template of the user's proposal into a generation AI and create a proposal.

[0037] The business agent can attend a meeting and generate remarks based on the learned data. The business agent can attend a meeting of a user, for example, and generate remarks based on the minutes of the meeting. For example, the business agent attends a meeting based on a specific agenda and remarks. The business agent can also generate remarks that match the user's style. For example, the business agent learns the remarks and expression methods of the user at the meeting, and generates remarks using specific keywords and phrases. This enables the business agent to attend a meeting that matches the user's style. Some or all of the above-mentioned processing in the business agent can be performed using, for example, AI, or can be performed without using AI. For example, the business agent can input the minutes of the meeting into a generating AI and generate remarks.

[0038] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit, for example, preferentially collects the type of data that the user frequently collected in the past. For example, the collection unit analyzes the user's past data collection history and evaluates the frequency of collection of specific data. The collection unit can also preferentially suggest the collection method (manual, API, etc.) that the user used in the past. For example, the collection unit analyzes the user's past collection method and selects the optimal collection method. Furthermore, the collection unit can select the optimal collection method for a specific time period from the user's past collection history. For example, the collection unit analyzes the user's past collection history in chronological order and identifies the collection pattern in the specific time period. This enables the collection unit to collect optimal data based on the user's past history. 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 input the user's past data collection history to the generation AI and select the optimal collection method.

[0039] The collection unit can perform filtering based on the user's current project or field of interest when collecting data. The collection unit, for example, preferentially collects data related to a project currently in progress by the user. For example, the collection unit filters data based on keywords related to the user's current project. The collection unit can also filter and collect highly relevant data based on the user's field of interest. For example, the collection unit filters data based on topics related to the user's field of interest. Furthermore, the collection unit can filter and collect data based on keywords specified by the user. For example, the collection unit filters data based on keywords specified by the user and preferentially collect highly relevant data. This enables the collection unit to collect data according to the user's current needs. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for filtering based on the user's current project or field of interest to the generation AI and perform filtering.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific area, the collection unit preferentially collects data related to the area. For example, the collection unit filters data based on the geographical location information of the user and collects highly relevant data. In addition, when the user is moving, the collection unit can also collect highly relevant data based on the current location. For example, the collection unit analyzes the movement history of the user and collects data based on the current location. Furthermore, when the user is staying in a specific place, the collection unit can also preferentially collect data related to the place. For example, the collection unit filters data based on the user's stay location and collects highly relevant data. This enables the collection unit to collect highly relevant data based on the user's location information. 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 input the user's geographical location information to the generation AI and collect highly relevant data.

[0041] The collection unit can analyze the user's social media activity and collect related data at the time of data collection. The collection unit collects related data based on, for example, information shared by the user on social media. For example, the collection unit analyzes the content of the user's social media posts and collects highly relevant data. The collection unit can also collect related data based on the content of posts of accounts followed by the user. For example, the collection unit analyzes the activity of accounts followed by the user and collects highly relevant data. Furthermore, the collection unit can also collect related data based on the activity of groups and communities in which the user participates. For example, the collection unit analyzes the content of posts of groups in which the user participates and collects highly relevant data. This enables the collection unit to collect data based on the user's social media activity. 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 input the user's social media activity into a generation AI and collect related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on specific data. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis based on the importance of the data and performs a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the importance of the data. For example, the analysis unit evaluates the importance of the data in real time and dynamically adjusts the priority of the analysis. This enables the analysis unit to perform analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit analyzes the text data and extracts specific keywords and phrases. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit analyzes the image data and detects specific objects and patterns. Furthermore, the analysis unit can apply a statistical analysis algorithm to numerical data. For example, the analysis unit analyzes numerical data and identifies specific trends and correlations. This enables the analysis unit to perform optimal analysis according to the category of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit evaluates the collection time of the data and prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of old data. For example, the analysis unit determines the priority of the analysis based on the collection time of the data, and postpones the analysis of old data. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the collection time of the data. For example, the analysis unit evaluates the collection time of the data in real time and dynamically adjusts the priority of the analysis. This enables the analysis unit to prioritize the analysis according to the collection time of the data. 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 input the collection time of the data to the generation AI and determine the priority of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the correlation between data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit determines the order of analysis based on the relevance of data, and postpones analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, the analysis unit evaluates the relevance of data in real time and dynamically adjusts the order of analysis. This enables the analysis unit to adjust the order of analysis according to the relevance of data. 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 input the relevance of data to the generation AI and adjust the order of analysis.

[0046] The business agent unit can select the optimal proxy method by analyzing the user's past business history when proxying the business. The business agent unit selects the optimal proxy method based on, for example, the history of the business performed by the user in the past. For example, the business agent unit analyzes the user's past business history and selects the optimal proxy method for a specific business. The business agent unit can also propose an efficient proxy method from the user's past business history. For example, the business agent unit analyzes the user's past business history and proposes an efficient proxy method for a specific business. Furthermore, the business agent unit can analyze the user's past business history and select the most effective proxy method. For example, the business agent unit selects the most effective proxy method for a specific business based on the user's past business history. This enables the business agent unit to perform optimal business proxy based on the user's past business history. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent can input the user's past business history into the generation AI and select the optimal agent method.

[0047] The business agent unit can customize the business agent means based on the user's current living situation during the business agent. For example, when the user is busy, the business agent unit provides a simplified business agent means. For example, the business agent unit evaluates the user's living rhythm and environment and provides a simplified business agent means. In addition, the business agent unit can provide detailed business agent means when the user is relaxed. For example, the business agent unit evaluates the user's living situation and provides detailed business agent means. Furthermore, the business agent unit can dynamically customize the business agent means according to the user's living situation. For example, the business agent unit evaluates the user's living situation in real time and dynamically customizes the business agent means. This enables the business agent unit to customize the business agent means according to the user's living situation. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent unit can input the user's living situation data into the generation AI and customize the business agent means.

[0048] The business agent unit can select the optimal business agent method in consideration of the geographical location information of the user when performing business agent. For example, when the user is in a specific area, the business agent unit prioritizes business related to the area. For example, the business agent unit filters business based on the geographical location information of the user and performs highly relevant business. In addition, when the user is moving, the business agent unit can select the optimal business agent method based on the current location. For example, the business agent unit analyzes the user's movement history and performs business based on the current location. Furthermore, when the user is staying in a specific location, the business agent unit can prioritize business related to the location. For example, the business agent unit filters business based on the user's staying location and performs highly relevant business. This enables the business agent unit to select the optimal business agent method based on the geographical location information of the user. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agency department can input the user's geographical location information into the generation AI and select the optimal business agency method.

[0049] The business agent unit can analyze the user's social media activities and propose a business agent means when performing business agent. The business agent unit proposes an optimal business agent means based on, for example, information shared by the user on social media. For example, the business agent unit analyzes the content of the user's social media posts and proposes a highly relevant business agent means. The business agent unit can also propose a related business agent means based on the activity of an account followed by the user. For example, the business agent unit analyzes the activity of an account followed by the user and proposes a highly relevant business agent means. Furthermore, the business agent unit can also propose an optimal business agent means based on the activity of a group or community in which the user participates. For example, the business agent unit analyzes the content of posts of a group in which the user participates and proposes a highly relevant business agent means. This enables the business agent unit to propose an optimal business agent means based on the user's social media activity. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent department can input a user's social media activity into the generation AI and suggest ways to perform business on their behalf.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The business proxy system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and sleep patterns, and provides the data to the analysis unit. The analysis unit evaluates the user's health condition based on this data and provides feedback to the business proxy unit. For example, if the user is tired, the business proxy unit can automatically take over part of the work to reduce the user's burden. Also, if the user is in good health, the business proxy unit can support the user so that the user can efficiently handle more work. This enables the business proxy system to flexibly proxy work according to the user's health condition.

[0052] The business proxy system can further include a user schedule management unit. The schedule management unit collects the user's calendar and schedule and provides them to the analysis unit. The analysis unit analyzes the user's schedule based on this data and provides feedback to the business proxy unit. For example, if the user has an important meeting or presentation coming up, the business proxy unit can take over other tasks so that the user can concentrate on preparing for it. Also, if the user goes on vacation, the business proxy unit can take over necessary tasks while the user is absent. This enables the business proxy system to flexibly take over tasks according to the user's schedule.

[0053] The business agency system may further include a user's project management unit. The project management unit collects the progress of the user's projects and the priorities of tasks, and provides them to the analysis unit. The analysis unit analyzes the progress of the user's projects based on this data, and provides feedback to the business agency unit. For example, if the user is working on multiple projects simultaneously, the business agency unit can take over tasks with higher priorities depending on the progress of each project. Also, if the user is falling behind in the progress of a project, the business agency unit can invest additional resources to make up for the delay. This enables the business agency system to flexibly take over business in accordance with the user's project management.

[0054] The business proxy system may further include a user learning support unit. The learning support unit collects the user's learning history and learning goals, and provides them to the analysis unit. The analysis unit analyzes the user's learning situation based on these data, and provides feedback to the business proxy unit. For example, if the user is trying to learn a new skill, the business proxy unit can take over other tasks so that the user can concentrate on learning. Also, if the user is falling behind in his / her learning progress, the business proxy unit can invest additional resources to support the learning progress. This enables the business proxy system to flexibly take over tasks in response to the user's learning support.

[0055] The business proxy system may further include a user feedback collection unit. The feedback collection unit collects feedback and evaluations of users' business operations and provides them to the analysis unit. The analysis unit analyzes the user's satisfaction with the business operations and areas for improvement based on this data, and provides feedback to the business proxy unit. For example, if a user is dissatisfied with a particular business operation, the business proxy unit can improve the method of proxying that business operation. Also, if a user is highly satisfied with a particular business operation, the business proxy unit can continue to use the method of proxying that business operation. This enables the business proxy system to flexibly proxy business operations in response to user feedback.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The collection unit collects the user's PC and business data. The user's PC and business data includes, for example, email history, proposal templates, and meeting minutes. The collection unit automatically collects the user's email history and filters it based on a specific period or specific sender / recipient. Proposal templates related to a specific format or project are collected, and meeting minutes are collected based on a specific meeting or agenda. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the user's style. The analysis is performed using natural language processing technology and machine learning algorithms. For example, the analysis unit learns the user's email writing style and work process style, learning the user's preferred ways of expression and work progress patterns. Step 3: The business agent performs business on behalf of the user based on the data learned by the analysis unit. Business agent tasks include replying to emails, creating proposals, attending meetings, etc. For example, the business agent generates replies to the user's emails and provides appropriate replies by referring to past email history. The business agent also creates proposals based on the learned templates and generates remarks based on meeting minutes.

[0058] (Example 2) The business proxy system according to the embodiment of the present invention is a system that uses a generation AI to perform a user's business. This business proxy system has the generation AI learn the user's PC and business data, and can reply to emails, create proposals, attend meetings, and so on, as the user's avatar. This system allows the generation AI to perform work on behalf of the user on one day a week or on holidays. For example, the business proxy system has the generation AI learn the user's PC and business data. At this time, data such as the user's email history, proposal templates, and meeting minutes are collected and analyzed by the generation AI. For example, by having the content of emails sent by the user in the past and the format of proposals created by the user learn, the generation AI can reply to emails and create proposals that match the user's style. Next, the generation AI performs the user's business based on the data learned. For example, when a user requests a reply to an email, the generation AI generates an appropriate reply by referring to the past email history. Also, when a user requests the creation of a proposal, the generation AI creates the proposal based on the learned template. Furthermore, when a user requests attendance at a meeting, the generation AI can generate remarks based on the minutes of the meeting and participate in the meeting. This system allows the generation AI to work on behalf of the user one day a week or on holidays. For example, if the user takes a weekend off, the generation AI can reply to emails and create proposals on the user's behalf, allowing the user to spend the holiday without worry. Even if the user is busy during the weekdays, the generation AI can attend meetings on the user's behalf, reducing the user's burden. In this way, the use of the generation AI can make the user's work more efficient and reduce the burden. Users can work more efficiently by requesting tasks that suit their own style from the generation AI. This allows the business proxy system to efficiently proxy the user's work.

[0059] The business agent system according to the embodiment includes a collection unit, an analysis unit, and a business agent unit. The collection unit collects the user's PC and business data. The user's PC and business data include, for example, email history, proposal templates, and meeting minutes, but are not limited to such examples. The collection unit, for example, collects the user's email history and analyzes the contents of emails sent in the past. The collection unit can also collect proposal templates and learn the format of proposals created by the user in the past. Furthermore, the collection unit can collect meeting minutes and analyze the contents of the meetings. For example, the collection unit automatically collects the user's email history and filters based on a specific period or a specific sender / recipient. The proposal templates are collected in a specific format or related to a project. The meeting minutes are collected based on a specific meeting or topic. The analysis unit analyzes the data collected by the collection unit and learns the user's style. The analysis is performed using, for example, natural language processing technology or machine learning algorithms, but are not limited to such examples. For example, the analysis unit learns the user's way of writing emails and the way of proceeding with work, and performs analysis according to the user's style. The analysis unit can also learn the user's preferred expression method and the progress pattern of work. For example, the analysis unit uses natural language processing technology to analyze the user's way of writing emails and extracts specific keywords and phrases. A machine learning algorithm learns the progress pattern of the user's work and determines the priority of specific tasks. The business agent performs the user's work on behalf of the user based on the data learned by the analysis unit. The business agent includes, for example, replying to emails, creating proposals, attending meetings, etc., but is not limited to such examples. For example, the business agent generates a reply to the user's email and makes an appropriate reply by referring to past email history. The business agent can also create a proposal and create the proposal based on the learned template. Furthermore, the business agent can also attend a meeting and generate remarks based on the minutes of the meeting. For example, the business agent automatically generates replies to user emails and responds based on a specific template or timing, and creates proposals based on a specific format or content structure.Attendance at a meeting is based on a specific agenda and the content of remarks, so that the business agent system according to the embodiment can efficiently act on behalf of the user's business.

[0060] The collection unit collects the user's PC and business data. The user's PC and business data include, but are not limited to, for example, email history, proposal templates, and meeting minutes. For example, the collection unit collects the user's email history and analyzes the contents of emails sent in the past. The collection unit can also collect proposal templates and learn the format of proposals created by the user in the past. Furthermore, the collection unit can collect meeting minutes and analyze the contents of the meetings. For example, the collection unit automatically collects the user's email history and filters based on a specific period or specific sender / recipient. The proposal templates are collected by collecting those related to a specific format or project. The meeting minutes are collected based on a specific meeting or topic. In order to efficiently collect these data, the collection unit can install dedicated software on the user's PC and set it to collect data periodically. For example, the collection of email history is performed by linking with the user's email client and automatically acquiring data based on a specific folder or label. The proposal templates are collected by scanning the user's file system and collecting based on a specific file format or keyword. The minutes of the meeting are linked with the user's calendar application and data is automatically acquired based on the meeting schedule. This allows the collection unit to efficiently and accurately collect the user's business data and provide it to the analysis unit and the business agent unit. Furthermore, the collection unit can centrally manage the collected data and perform data cleansing processing to prevent data duplication and loss. For example, to prevent the same email from being collected multiple times, a duplication check is performed based on the email header information. For proposal templates, the file hash value is calculated and duplicate files are eliminated. For the minutes of the meeting, a duplication check is performed based on the date and time of the meeting and participant information. This allows the collection unit to provide accurate and consistent data and improve the reliability of the entire system.

[0061] The analysis unit analyzes the data collected by the collection unit and learns the user's style. The analysis is performed, for example, using natural language processing technology or machine learning algorithms, but is not limited to such examples. For example, the analysis unit learns the user's way of writing emails and how to proceed with work, and performs analysis according to the user's style. The analysis unit can also learn the user's preferred expression method and work progress pattern. For example, the analysis unit uses natural language processing technology to analyze the user's way of writing emails and extracts specific keywords and phrases. The machine learning algorithm learns the user's work progress pattern and determines the priority of specific tasks. The analysis unit combines these technologies to comprehensively understand the user's work style and build a foundation for realizing optimal work delegation. Specifically, the analysis unit uses natural language processing technology to extract frequently occurring keywords and phrases from the user's email history and grasp the user's communication style. For example, it learns greetings, closing words, specific expressions, etc. that the user often uses, and applies them when replying to emails. In addition, the analysis unit uses a machine learning algorithm to analyze the progress pattern of the user's work and grasp the priority and progress of a specific task. For example, it learns in what order the user processes tasks and which tasks the user spends most time on, and proposes an optimal task schedule when acting on behalf of the user. Furthermore, the analysis unit can propose ways to improve the efficiency of work and improve points based on the user's work data. For example, it analyzes past email history and meeting minutes to identify frequently occurring problems and bottlenecks and propose improvement measures. It can also propose the introduction of more efficient work processes and tools based on the user's work style. In this way, the analysis unit can deeply understand the user's work and provide a foundation for the business agent unit to optimally act on behalf of the user.

[0062] The business agent performs the business of the user on the basis of the data learned by the analysis unit. The business agent includes, for example, replying to emails, creating a proposal, attending a meeting, and the like, but is not limited to such examples. For example, the business agent generates a reply to the user's email and performs an appropriate reply by referring to past email history. The business agent can also create a proposal and create the proposal based on the learned template. Furthermore, the business agent can attend a meeting and generate remarks based on the minutes of the meeting. For example, the business agent automatically generates a reply to the user's email and replies based on a specific template and timing of the reply. The proposal is created based on a specific format and content structure. Attendance at a meeting is based on a specific agenda and remarks. In order to efficiently perform these tasks, the business agent utilizes the data provided by the analysis unit to realize business agent matching the user's style. Specifically, in generating a reply to an email, the business agent utilizes keywords and phrases extracted by the analysis unit to generate a reply matching the user's communication style. For example, the system automatically generates a reply incorporating greetings and closing phrases frequently used by the user, realizing a reply that fits the user's style. In creating a proposal, the system automatically generates a proposal that matches the style of the user's past proposals based on the templates and formats learned by the analysis unit. For example, a template related to a specific project is used, and the content structure and expression method are adjusted to match the user's style. In attending a meeting, the system generates remarks on behalf of the user based on the minutes and remarks provided by the analysis unit, supporting the progress of the meeting. For example, the system automatically generates the user's opinions and suggestions on a specific topic, contributing to the progress of the meeting. In this way, the business agent unit can efficiently and accurately perform the user's business and reduce the user's workload. Furthermore, the business agent unit can collect user feedback and continuously improve the accuracy and effectiveness of business agent services. For example, the system reviews the content of email replies and the structure of proposals based on user feedback, realizing business agent services that better fit the user's style.The business agent also has a scheduling function for processing multiple tasks simultaneously, enabling efficient management of user tasks. This allows the business agent to comprehensively support user tasks and contribute to improving the efficiency and productivity of tasks.

[0063] The collection unit can collect data on the user's email history, proposal templates, and meeting minutes. The collection unit, for example, collects the user's email history and filters it based on a specific period or a specific sender / recipient. For example, the collection unit collects email history from the past year and extracts emails containing specific keywords. The collection unit can also collect proposal templates and collect those related to a specific format or project. For example, the collection unit collects proposal templates used in the past and preferentially collects templates related to a specific project. Furthermore, the collection unit can collect meeting minutes and collect them based on a specific meeting or topic. For example, the collection unit collects minutes of past meetings and extracts remarks related to a specific topic. This allows the collection unit to efficiently collect data related to the user's work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's email history into a generation AI and analyze the contents of the email.

[0064] The analysis unit can analyze the collected data and learn the user's style. The analysis unit can analyze the user's email writing style, for example, using natural language processing technology. For example, the analysis unit can extract specific keywords and phrases and learn the user's email style. The analysis unit can also learn the user's work progress pattern using a machine learning algorithm. For example, the analysis unit can analyze the user's work progress pattern and determine the priority of a specific task. Furthermore, the analysis unit can also learn the user's preferred expression method and work progress pattern. For example, the analysis unit can analyze the user's email writing style and work progress pattern and extract specific keywords and phrases. This enables the analysis unit to perform work on behalf of the user in accordance with the user's style. 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 input the collected data into a generation AI and learn the user's style.

[0065] The business agent unit can generate a reply to an email based on the learned data. The business agent unit, for example, generates a reply to a user's email and makes an appropriate reply by referring to past email history. For example, the business agent unit makes a reply based on a specific template or timing of the reply. The business agent unit can also generate a reply to an email that matches the user's style. For example, the business agent unit learns how the user writes and expresses emails, and generates a reply using specific keywords and phrases. This enables the business agent unit to reply to an email that matches the user's style. Some or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent unit can input the user's email history into a generation AI and generate a reply to an email.

[0066] The business agent can create a proposal based on the learned data. The business agent creates, for example, a proposal for a user, and creates the proposal based on the learned template. For example, the business agent creates a proposal based on a specific format or content configuration. The business agent can also create a proposal that matches the user's style. For example, the business agent learns how the user writes and expresses a proposal, and creates a proposal using specific keywords and phrases. This enables the business agent to create a proposal that matches the user's style. A part or all of the above-mentioned processing in the business agent may be performed, for example, using AI, or may be performed without using AI. For example, the business agent can input a template of the user's proposal into a generation AI and create a proposal.

[0067] The business agent can attend a meeting and generate remarks based on the learned data. The business agent can attend a meeting of a user, for example, and generate remarks based on the minutes of the meeting. For example, the business agent attends a meeting based on a specific agenda and remarks. The business agent can also generate remarks that match the user's style. For example, the business agent learns the remarks and expression methods of the user at the meeting, and generates remarks using specific keywords and phrases. This enables the business agent to attend a meeting that matches the user's style. Some or all of the above-mentioned processing in the business agent can be performed using, for example, AI, or can be performed without using AI. For example, the business agent can input the minutes of the meeting into a generating AI and generate remarks.

[0068] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit refrains from collecting data and resumes collection when the user is relaxed. For example, the collection unit analyzes the user's facial expression and voice and estimates the user's emotion using an emotion estimation algorithm. In addition, when the user is concentrating, the collection unit can also collect a large amount of data at that timing. For example, the collection unit collects the user's heart rate and electrodermal activity with a sensor and estimates the user's emotion using an emotion estimation algorithm. In addition, when the user is tired, the collection unit can minimize data collection and collect again after resting. For example, the collection unit analyzes the user's emotion data in real time and immediately grasps changes in emotion. This enables the collection unit to collect data at the optimal timing 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. Some or all of the above-mentioned processes in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and adjust the timing of data collection.

[0069] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit, for example, preferentially collects the type of data that the user frequently collected in the past. For example, the collection unit analyzes the user's past data collection history and evaluates the frequency of collection of specific data. The collection unit can also preferentially suggest the collection method (manual, API, etc.) that the user used in the past. For example, the collection unit analyzes the user's past collection method and selects the optimal collection method. Furthermore, the collection unit can select the optimal collection method for a specific time period from the user's past collection history. For example, the collection unit analyzes the user's past collection history in chronological order and identifies the collection pattern in the specific time period. This enables the collection unit to collect optimal data based on the user's past history. 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 input the user's past data collection history to the generation AI and select the optimal collection method.

[0070] The collection unit can perform filtering based on the user's current project or field of interest when collecting data. The collection unit, for example, preferentially collects data related to a project currently in progress by the user. For example, the collection unit filters data based on keywords related to the user's current project. The collection unit can also filter and collect highly relevant data based on the user's field of interest. For example, the collection unit filters data based on topics related to the user's field of interest. Furthermore, the collection unit can filter and collect data based on keywords specified by the user. For example, the collection unit filters data based on keywords specified by the user and preferentially collect highly relevant data. This enables the collection unit to collect data according to the user's current needs. 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 input data for filtering based on the user's current project or field of interest to the generation AI and perform filtering.

[0071] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit postpones the collection of data of low importance. For example, the collection unit analyzes the user's facial expression and voice and estimates the user's emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the collection unit can also preferentially collect data of high importance. For example, the collection unit collects the user's heart rate and electrodermal activity with a sensor and estimates the user's emotion using an emotion estimation algorithm. In addition, when the user is in a hurry, the collection unit can also preferentially collect the most important data. For example, the collection unit analyzes the user's emotion data in real time and immediately grasps changes in emotion. This enables the collection unit to prioritize data 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. Some or all of the above-mentioned processes in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and determine the priority of the data to be collected.

[0072] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific area, the collection unit preferentially collects data related to the area. For example, the collection unit filters data based on the geographical location information of the user and collects highly relevant data. In addition, when the user is moving, the collection unit can also collect highly relevant data based on the current location. For example, the collection unit analyzes the movement history of the user and collects data based on the current location. Furthermore, when the user is staying in a specific place, the collection unit can also preferentially collect data related to the place. For example, the collection unit filters data based on the user's stay location and collects highly relevant data. This enables the collection unit to collect highly relevant data based on the user's location information. 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 input the user's geographical location information to the generation AI and collect highly relevant data.

[0073] The collection unit can analyze the user's social media activity and collect related data at the time of data collection. The collection unit collects related data based on, for example, information shared by the user on social media. For example, the collection unit analyzes the content of the user's social media posts and collects highly relevant data. The collection unit can also collect related data based on the content of posts of accounts followed by the user. For example, the collection unit analyzes the activity of accounts followed by the user and collects highly relevant data. Furthermore, the collection unit can also collect related data based on the activity of groups and communities in which the user participates. For example, the collection unit analyzes the content of posts of groups in which the user participates and collects highly relevant data. This enables the collection unit to collect data based on the user's social media activity. 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 input the user's social media activity into a generation AI and collect related data.

[0074] The analysis unit can estimate the user's emotion and adjust the method of expressing the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. For example, the analysis unit analyzes the user's facial expression and voice, and estimates the user's emotion using an emotion estimation algorithm. In addition, the analysis unit can provide a detailed analysis result when the user is relaxed. For example, the analysis unit collects the user's heart rate and electrodermal activity with a sensor, and estimates the user's emotion using an emotion estimation algorithm. In addition, when the user is in a hurry, the analysis unit can provide a concise analysis result that covers the main points. For example, the analysis unit analyzes the user's emotion data in real time and immediately grasps changes in emotion. This enables the analysis unit to provide an analysis result 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data to the generation AI and adjust the way the analysis is expressed.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on specific data. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis based on the importance of the data and performs a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the importance of the data. For example, the analysis unit evaluates the importance of the data in real time and dynamically adjusts the priority of the analysis. This enables the analysis unit to perform analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit analyzes the text data and extracts specific keywords and phrases. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit analyzes the image data and detects specific objects and patterns. Furthermore, the analysis unit can apply a statistical analysis algorithm to numerical data. For example, the analysis unit analyzes numerical data and identifies specific trends and correlations. This enables the analysis unit to perform optimal analysis according to the category of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and apply an appropriate analysis algorithm.

[0077] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. For example, the analysis unit analyzes the user's facial expression and voice and estimates the user's emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the analysis unit can provide a longer analysis result including a detailed explanation. For example, the analysis unit collects the user's heart rate and electrodermal activity with a sensor and estimates the user's emotion using an emotion estimation algorithm. In addition, when the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. For example, the analysis unit analyzes the user's emotion data in real time and immediately grasps changes in emotion. This enables the analysis unit to adjust the length of the analysis result 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data to the generation AI and adjust the length of the analysis.

[0078] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit evaluates the collection time of the data and prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of old data. For example, the analysis unit determines the priority of the analysis based on the collection time of the data, and postpones the analysis of old data. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the collection time of the data. For example, the analysis unit evaluates the collection time of the data in real time and dynamically adjusts the priority of the analysis. This enables the analysis unit to prioritize the analysis according to the collection time of the data. 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 input the collection time of the data to the generation AI and determine the priority of the analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the correlation between data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit determines the order of analysis based on the relevance of data, and postpones analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, the analysis unit evaluates the relevance of data in real time and dynamically adjusts the order of analysis. This enables the analysis unit to adjust the order of analysis according to the relevance of data. 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 input the relevance of data to the generation AI and adjust the order of analysis.

[0080] The business agent can estimate the user's emotion and adjust the method of business agent based on the estimated user's emotion. For example, when the user is stressed, the business agent provides a simple business agent method. For example, the business agent analyzes the user's facial expression and voice, and estimates the user's emotion using an emotion estimation algorithm. In addition, the business agent can provide a detailed business agent method when the user is relaxed. For example, the business agent collects the user's heart rate and electrodermal activity with a sensor, and estimates the user's emotion using an emotion estimation algorithm. In addition, the business agent can provide a method of quickly acting on behalf of the user when the user is in a hurry. For example, the business agent analyzes the user's emotion data in real time and immediately grasps changes in emotion. This enables the business agent to adjust the business agent 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the business agent unit may be performed, for example, using AI, or may be performed without using AI. For example, the business agent unit may input the user's emotion data to the generation AI and adjust the method of business agent processing.

[0081] The business agent unit can select the optimal proxy method by analyzing the user's past business history when proxying the business. The business agent unit selects the optimal proxy method based on, for example, the history of the business performed by the user in the past. For example, the business agent unit analyzes the user's past business history and selects the optimal proxy method for a specific business. The business agent unit can also propose an efficient proxy method from the user's past business history. For example, the business agent unit analyzes the user's past business history and proposes an efficient proxy method for a specific business. Furthermore, the business agent unit can analyze the user's past business history and select the most effective proxy method. For example, the business agent unit selects the most effective proxy method for a specific business based on the user's past business history. This enables the business agent unit to perform optimal business proxy based on the user's past business history. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent can input the user's past business history into the generation AI and select the optimal agent method.

[0082] The business agent unit can customize the business agent means based on the user's current living situation during the business agent. For example, when the user is busy, the business agent unit provides a simplified business agent means. For example, the business agent unit evaluates the user's living rhythm and environment and provides a simplified business agent means. In addition, the business agent unit can provide detailed business agent means when the user is relaxed. For example, the business agent unit evaluates the user's living situation and provides detailed business agent means. Furthermore, the business agent unit can dynamically customize the business agent means according to the user's living situation. For example, the business agent unit evaluates the user's living situation in real time and dynamically customizes the business agent means. This enables the business agent unit to customize the business agent means according to the user's living situation. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent unit can input the user's living situation data into the generation AI and customize the business agent means.

[0083] The business agent can estimate the user's emotions and determine the priority of the business agent based on the estimated user's emotions. For example, when the user is feeling stressed, the business agent puts off tasks with low importance. For example, the business agent analyzes the user's facial expression and voice and estimates the user's emotions using an emotion estimation algorithm. In addition, when the user is relaxed, the business agent can also prioritize tasks with high importance. For example, the business agent collects the user's heart rate and electrodermal activity with a sensor and estimates the user's emotions using an emotion estimation algorithm. In addition, when the user is in a hurry, the business agent can also prioritize the most important tasks. For example, the business agent analyzes the user's emotion data in real time and immediately grasps changes in emotions. This enables the business agent to prioritize tasks according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the business agent unit may be performed, for example, using AI, or may be performed without using AI. For example, the business agent unit may input user emotion data to the generation AI and determine the priority of business agent.

[0084] The business agent unit can select the optimal business agent method in consideration of the geographical location information of the user when performing business agent. For example, when the user is in a specific area, the business agent unit prioritizes business related to the area. For example, the business agent unit filters business based on the geographical location information of the user and performs highly relevant business. In addition, when the user is moving, the business agent unit can select the optimal business agent method based on the current location. For example, the business agent unit analyzes the user's movement history and performs business based on the current location. Furthermore, when the user is staying in a specific location, the business agent unit can prioritize business related to the location. For example, the business agent unit filters business based on the user's staying location and performs highly relevant business. This enables the business agent unit to select the optimal business agent method based on the geographical location information of the user. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agency department can input the user's geographical location information into the generation AI and select the optimal business agency method.

[0085] The business agent unit can analyze the user's social media activities and propose a business agent means when performing business agent. The business agent unit proposes an optimal business agent means based on, for example, information shared by the user on social media. For example, the business agent unit analyzes the content of the user's social media posts and proposes a highly relevant business agent means. The business agent unit can also propose a related business agent means based on the activity of an account followed by the user. For example, the business agent unit analyzes the activity of an account followed by the user and proposes a highly relevant business agent means. Furthermore, the business agent unit can also propose an optimal business agent means based on the activity of a group or community in which the user participates. For example, the business agent unit analyzes the content of posts of a group in which the user participates and proposes a highly relevant business agent means. This enables the business agent unit to propose an optimal business agent means based on the user's social media activity. A part or all of the above-mentioned processing in the business agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the business agent department can input a user's social media activity into the generation AI and suggest ways to perform business on their behalf.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The business proxy system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and sleep patterns, and provides the data to the analysis unit. The analysis unit evaluates the user's health condition based on this data and provides feedback to the business proxy unit. For example, if the user is tired, the business proxy unit can automatically take over part of the work to reduce the user's burden. Also, if the user is in good health, the business proxy unit can support the user so that the user can efficiently handle more work. This enables the business proxy system to flexibly proxy work according to the user's health condition.

[0088] The business proxy system can further include a user schedule management unit. The schedule management unit collects the user's calendar and schedule and provides them to the analysis unit. The analysis unit analyzes the user's schedule based on this data and provides feedback to the business proxy unit. For example, if the user has an important meeting or presentation coming up, the business proxy unit can take over other tasks so that the user can concentrate on preparing for it. Also, if the user goes on vacation, the business proxy unit can take over necessary tasks while the user is absent. This enables the business proxy system to flexibly take over tasks according to the user's schedule.

[0089] The business agency system may further include a user's project management unit. The project management unit collects the progress of the user's projects and the priorities of tasks, and provides them to the analysis unit. The analysis unit analyzes the progress of the user's projects based on this data, and provides feedback to the business agency unit. For example, if the user is working on multiple projects simultaneously, the business agency unit can take over tasks with higher priorities depending on the progress of each project. Also, if the user is falling behind in the progress of a project, the business agency unit can invest additional resources to make up for the delay. This enables the business agency system to flexibly take over business in accordance with the user's project management.

[0090] The business proxy system may further include a user learning support unit. The learning support unit collects the user's learning history and learning goals, and provides them to the analysis unit. The analysis unit analyzes the user's learning situation based on these data, and provides feedback to the business proxy unit. For example, if the user is trying to learn a new skill, the business proxy unit can take over other tasks so that the user can concentrate on learning. Also, if the user is falling behind in his / her learning progress, the business proxy unit can invest additional resources to support the learning progress. This enables the business proxy system to flexibly take over tasks in response to the user's learning support.

[0091] The business proxy system may further include a user feedback collection unit. The feedback collection unit collects feedback and evaluations of users' business operations and provides them to the analysis unit. The analysis unit analyzes the user's satisfaction with the business operations and areas for improvement based on this data, and provides feedback to the business proxy unit. For example, if a user is dissatisfied with a particular business operation, the business proxy unit can improve the method of proxying that business operation. Also, if a user is highly satisfied with a particular business operation, the business proxy unit can continue to use the method of proxying that business operation. This enables the business proxy system to flexibly proxy business operations in response to user feedback.

[0092] The business proxy system can further estimate the user's emotions and dynamically adjust the priority of tasks based on the estimated user's emotions. For example, when the user is feeling stressed, the business proxy unit postpones tasks of low importance, reducing the user's burden. When the user is relaxed, the business proxy unit can prioritize tasks of high importance. Furthermore, when the user is in a hurry, the business proxy unit can quickly handle the most important tasks. This enables the business proxy system to flexibly proxy tasks according to the user's emotions.

[0093] The business proxy system can further estimate the user's emotions and adjust the way the business is carried out based on the estimated user's emotions. For example, if the user is nervous, the business proxy unit can provide a simple and highly visible way of carrying out the business. If the user is relaxed, a detailed way of carrying out the business can be provided. If the user is in a hurry, the business proxy unit can provide a concise way of carrying out the business that focuses on the main points. This enables the business proxy system to flexibly carry out the business in accordance with the user's emotions.

[0094] The business agent system can further estimate the user's emotions and adjust the business feedback method based on the estimated user's emotions. For example, if the user is feeling stressed, the business agent unit can provide simple, highly visible feedback. If the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback that focuses on the main points can be provided. This enables the business agent system to provide flexible feedback according to the user's emotions.

[0095] The business proxy system can further estimate the user's emotions and adjust the speed at which the business is progressing based on the estimated user's emotions. For example, if the user is feeling stressed, the business proxy unit slows down the speed at which the business is progressing to reduce the burden on the user. Also, if the user is relaxed, the business proxy unit can keep the business progressing at a normal speed. Furthermore, if the user is in a hurry, the business proxy unit can speed up the business progressing. This allows the business proxy system to flexibly progress business in accordance with the user's emotions.

[0096] The business proxy system can further estimate the user's emotions and adjust the content of the business based on the estimated user's emotions. For example, when the user is feeling stressed, the business proxy unit prioritizes simple tasks to reduce the user's burden. When the user is relaxed, the business proxy unit can take over complex tasks. When the user is in a hurry, the business proxy unit can quickly take over important tasks. This enables the business proxy system to flexibly take over tasks in accordance with the user's emotions.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The collection unit collects the user's PC and business data. The user's PC and business data includes, for example, email history, proposal templates, and meeting minutes. The collection unit automatically collects the user's email history and filters it based on a specific period or specific sender / recipient. Proposal templates related to a specific format or project are collected, and meeting minutes are collected based on a specific meeting or agenda. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the user's style. The analysis is performed using natural language processing technology and machine learning algorithms. For example, the analysis unit learns the user's email writing style and work process style, learning the user's preferred ways of expression and work progress patterns. Step 3: The business agent performs business on behalf of the user based on the data learned by the analysis unit. Business agent tasks include replying to emails, creating proposals, attending meetings, etc. For example, the business agent generates replies to the user's emails and provides appropriate replies by referring to past email history. The business agent also creates proposals based on the learned templates and generates remarks based on meeting minutes.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements including the collection unit, analysis unit, and business agent unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects business data of the user using the camera 42 and microphone 38B of the smart device 14, and transmits the data 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, analyzes the collected data, and learns the user's style. The business agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acts on behalf of the user's business based on the analyzed data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and business agent unit may be realized, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements including the collection unit, analysis unit, and business agent unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects business data of the user using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data 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, analyzes the collected data, and learns the user's style. The business agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acts on behalf of the user's business based on the analyzed data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and business agent unit may be realized, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and business agent 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 the user's business data using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the data 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, analyzes the collected data, and learns the user's style. The business agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acts on behalf of the user's business based on the analyzed data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and business agent unit may be realized, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements including the collection unit, analysis unit, and business agent unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects business data of the user using the camera 42 and microphone 238 of the robot 414, and transmits the data 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, analyzes the collected data, and learns the user's style. The business agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acts on behalf of the user's business based on the analyzed data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and business agent unit may be realized, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Appendix 1) A collection unit that collects user PC or business data; an analysis unit that analyzes the data collected by the collection unit and learns a user's style; A business agent unit that performs business on behalf of a user based on the data learned by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on users' email history, proposal templates, and meeting minutes 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected data and learn user style 2. The system of claim 1, (Appendix 4) The business agent department: Generate email replies based on learned data 2. The system of claim 1, (Appendix 5) The business agent department: Create a proposal based on the learned data 2. The system of claim 1, (Appendix 6) The business agent department: Attend meetings and generate speech content based on learned data 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the user's past data collection history and select the appropriate collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data as it is collected based on the user's current projects and interests 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics 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 data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The business agent department: To estimate a user's feelings and adjust a method of performing a task on behalf of the user based on the estimated user's feelings. 2. The system of claim 1, (Appendix 20) The business agent department: When performing work on behalf of a user, analyze the user's past work history and select the appropriate method of substitution. 2. The system of claim 1, (Appendix 21) The business agent department: When performing a task on behalf of another user, customize the means of performing the task on behalf of the user based on the user's current living situation. 2. The system of claim 1, (Appendix 22) The business agent department: To estimate a user's feelings and determine the priority of tasks based on the estimated user's feelings 2. The system of claim 1, (Appendix 23) The business agent department: To select an appropriate method of delegating a task by considering a user's geographic location information when delegating a task. 2. The system of claim 1, (Appendix 24) The business agent department: When acting on behalf of a user, the system analyzes the user's social media activity and suggests ways to carry out the work on behalf of the user. 2. The system of claim 1, [Explanation of symbols]

[0171] 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 unit that collects user PC or business data; an analysis unit that analyzes the data collected by the collection unit and learns a user's style; A business agent unit that performs business on behalf of a user based on the data learned by the analysis unit. A system characterized by:

2. The collecting unit includes: Collect data on users' email history, proposal templates, and meeting minutes 2. The system of claim 1 .

3. The analysis unit is Analyzing the data collected by the collection unit to learn the user's style.

2. The system of claim 1 .

4. The business agent department: Generates an email reply based on the data learned by the analysis unit.

2. The system of claim 1 .

5. The business agent department: A proposal is created based on the data learned by the analysis unit.

2. The system of claim 1 .

6. The business agent department: Attend a meeting and generate speech content based on the data learned by the analysis unit 2. The system of claim 1 .

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

8. The collecting unit includes: Analyze the user's past data collection history and select the appropriate collection method 2. The system of claim 1 .

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