system

Abstract

The system according to the embodiment aims to create a body double robot based on the user's personal information and to handle business operations. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a response unit, and an adaptation unit. The collection unit collects personal information about the user. The analysis unit analyzes the information collected by the collection unit. The generation unit creates a body double robot based on the analysis results obtained by the analysis unit. The response unit allows the body double robot created by the generation unit to handle business operations. The adaptation unit responds in accordance with the user's communication style.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately created body double robots based on users' personal information and have not been able to handle business operations, so there is room for improvement.

[0005] The system according to the embodiment aims to create a body double robot based on the user's personal information and to handle business operations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a response unit, and an adaptation unit. The collection unit collects personal information about the user. The analysis unit analyzes the information collected by the collection unit. The generation unit creates a body double robot based on the analysis results obtained by the analysis unit. The response unit allows the body double robot created by the generation unit to handle business operations. The adaptation unit adapts to the user's communication style. [Effects of the Invention]

[0007] The system according to the embodiment can create a body double robot based on the user's personal information and use it to handle business operations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A body double robot system according to an embodiment of the present invention collects and analyzes a user's personal information, creates a body double robot, and handles daily tasks. This body double robot system uses AI to learn personal information about the user, such as the user's PC, network drive, LINE (registered trademark) message history, and albums, to create a body double robot for the user. The body double robot then handles all simple communication and business tasks, such as daily tasks and email correspondence, thereby improving productivity. For example, the body double robot system collects personal information about the user, such as the user's PC, network drive, LINE message history, and albums. The body double robot system then analyzes the collected information to understand the user's behavioral patterns and communication style. The body double robot system then creates a body double robot based on the analyzed information. The body double robot handles daily tasks and email correspondence on behalf of the user. Furthermore, the body double robot adapts to the user's communication style. This allows the user to save time on daily tasks and email correspondence and improve productivity. The body double robot system collects and analyzes the user's personal information, creates a body double robot, and handles daily tasks, thereby improving productivity. LINE is an example of a messaging app.

[0029] A body double robot system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a correspondence unit, and an adaptation unit. The collection unit collects personal information about a user. Examples of the user's personal information include, but are not limited to, information stored on a PC, a network drive, a LINE message history, and an album. The collection unit, for example, scans files and folders stored on the user's PC or a network drive to collect information. The collection unit can also collect LINE message history and photo data from albums. For example, the collection unit collects metadata about files and folders using software installed on the user's PC. The collection unit can also access network drives and collect stored data. The collection unit can also obtain message history using the LINE API. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's behavioral patterns and communication style using, for example, data mining technology or machine learning algorithms. For example, the analysis unit can analyze the user's email sending and receiving history to identify frequent contacts and frequently used phrases. The analysis unit can also analyze the user's file access history and identify frequently used files and folders. Furthermore, the analysis unit can analyze the LINE message history and understand the user's communication style. The generation unit creates a body double robot based on the analysis results obtained by the analysis unit. The generation unit generates, for example, a software agent or a virtual assistant. For example, the generation unit sets the body double robot's actions and responses based on the user's behavior patterns and communication style. The generation unit can also generate a body double robot with the function of managing the user's schedule and tasks. Furthermore, the generation unit can customize the body double robot's appearance and voice to suit the user's preferences. The response unit causes the body double robot created by the generation unit to handle business operations. For example, the response unit can send and receive emails on behalf of the user. The response unit can also handle phone calls on behalf of the user.For example, the response unit can access the user's email account and automatically reply to received emails. The response unit can also receive and answer calls forwarded to the user's phone number. Furthermore, the response unit can schedule meetings and send reminders based on the user's schedule. The adaptation unit adapts to the user's communication style. For example, the adaptation unit adjusts the body double's responses to match the user's email writing style and speaking style. For example, if the user sends a formal email, the adaptation unit can also make the body double reply in a formal style. Furthermore, if the user sends an informal email, the adaptation unit can also make the body double reply in an informal style. Furthermore, the adaptation unit can adjust the voice responses of the body double to match the user's speaking style. As a result, the body double robot system according to the embodiment can collect and analyze the user's personal information, create a body double, and handle business operations, thereby improving productivity.

[0030] The collection unit can collect personal information from at least one of the user's PC or network drive, LINE message history, and album. The collection unit, for example, scans files and folders stored on the user's PC to collect the information. For example, the collection unit can collect metadata about files and folders using software installed on the user's PC. The collection unit can also access a network drive and collect stored data. For example, the collection unit can obtain file and folder information using a network drive's API. The collection unit can also collect LINE message history and photo data from albums. For example, the collection unit can obtain message history using a LINE API. The collection unit can also download photo data from albums from cloud storage. This improves the accuracy of the body double robot by collecting the user's personal information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information about files and folders stored on the user's PC into a generation AI, causing the generation AI to collect the information.

[0031] The analysis unit analyzes the information collected by the collection unit to understand the user's behavioral patterns and communication style. The analysis unit analyzes the user's behavioral patterns and communication style, for example, using data mining technology or machine learning algorithms. For example, the analysis unit analyzes the user's email sending and receiving history to identify frequent contacts and frequently used phrases. The analysis unit can also analyze the user's file access history to identify frequently used files and folders. For example, the analysis unit analyzes the access log of files stored on the user's PC to identify frequently accessed files. The analysis unit can also analyze the LINE message history to understand the user's communication style. For example, the analysis unit analyzes the content of LINE messages to identify frequently used expressions and patterns of interaction with specific contacts. By understanding the user's behavioral patterns and communication style, the body double robot can respond more appropriately. Some or all of the above-mentioned processing by the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the collected information into the generation AI, which can then analyze behavioral patterns and communication styles.

[0032] The generation unit can create a body double robot based on the analysis results obtained by the analysis unit. The generation unit generates, for example, a software agent or a virtual assistant. For example, the generation unit sets the body double robot's behavior and responses based on the user's behavioral patterns and communication style. The generation unit can also generate a body double robot with a function for managing the user's schedule and tasks. For example, the generation unit can add a schedule management function to the body double robot based on the user's calendar information. The generation unit can also customize the body double robot's appearance and voice to suit the user's preferences. For example, the generation unit applies an avatar and voice selected by the user to the body double robot. This allows the body double robot to be created based on the analysis results and perform tasks on behalf of the user. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate a body double robot.

[0033] The response unit allows the body double robot to handle daily business or email correspondence on behalf of the user. The response unit, for example, sends and receives emails on behalf of the user. For example, the response unit accesses the user's email account and automatically replies to received emails. The response unit can also handle phone calls on behalf of the user. For example, the response unit can receive and answer calls forwarded to the user's phone number. Furthermore, the response unit can set up meetings and send reminders based on the user's schedule. For example, the response unit can send meeting invitations and set reminders based on the user's calendar information. This reduces the burden on the user by having the body double robot handle business tasks. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without AI. For example, the response unit can input the content of an email into a generation AI and have the generation AI generate a reply.

[0034] The adaptation unit enables the body double robot to adapt to the user's communication style. The adaptation unit adjusts the body double's responses to, for example, the user's email writing style or speaking style. For example, if the user sends a formal email, the adaptation unit causes the body double to reply in a formal style. Furthermore, if the user sends an informal email, the adaptation unit causes the body double to reply in an informal style. Furthermore, the adaptation unit can adjust the body double's voice responses to adapt to the user's speaking style. For example, if the user speaks in a calm tone, the adaptation unit causes the body double to reply in a similar tone. Furthermore, if the user speaks quickly, the adaptation unit causes the body double to reply in a fast-paced manner. This allows the body double to adapt to the user's communication style, enabling more natural communication. Some or all of the above-described processing in the adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the adaptation unit can input the user's email or voice data into the generation AI and have the generation AI adjust the response content.

[0035] The body double robot system includes a management unit in which the body double robot manages the user's schedule. The management unit manages the user's schedule. The management unit adds, changes, and deletes schedules based on the user's calendar information, for example. For example, the management unit adds new events to the user's calendar, changes existing events, and deletes unnecessary events. The management unit can also set reminders and prioritize tasks. For example, the management unit can set reminders and prioritize tasks based on the user's schedule. The management unit can also synchronize the user's schedule with other devices. For example, the management unit can synchronize the schedule with the user's smartphone or tablet, allowing the schedule to be viewed from any device. This allows the body double robot to manage the schedule, thereby improving the efficiency of the user's time management. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input the user's calendar information into a generation AI and have the generation AI perform schedule management.

[0036] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit prioritizes collection of files and folders that the user frequently accessed in the past. For example, the collection unit analyzes the access log of files stored on the user's PC and identifies frequently accessed files. The collection unit can also suggest the optimal collection method based on the collection method (manual, scheduled, etc.) used by the user in the past. For example, the collection unit analyzes the user's past collection methods and selects the most efficient method. The collection unit can also select a collection method for a specific time period based on the user's past information collection history. For example, the collection unit analyzes the user's activity log and identifies the optimal collection timing. This allows the optimal collection method to be selected by analyzing the past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the past information collection history into a generation AI and have the generation AI select the optimal collection method.

[0037] When collecting information, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting information related to the user's ongoing project. For example, the collection unit can analyze data from the user's project management tool to identify relevant information. The collection unit can also collect only relevant information based on the user's area of ​​interest. For example, the collection unit can analyze the user's browsing history or search history to identify the area of ​​interest. The collection unit can also filter and collect information based on keywords set by the user. For example, the collection unit collects relevant information based on keywords specified by the user. This makes it possible to collect highly relevant information by filtering the information based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's project data into a generation AI and have the generation AI filter the information.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit analyzes the user's GPS data and identifies information related to the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting information related to the travel destination. For example, the collection unit analyzes the user's travel plans and identifies information related to the travel destination. Furthermore, if the user is participating in a specific event, the collection unit can prioritize collecting information related to the event. For example, the collection unit analyzes the user's calendar information and identifies information related to the event. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's GPS data into a generation AI and cause the generation AI to collect related information.

[0039] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit collects related information based on, for example, information shared by the user on social media. For example, the collection unit accesses the user's social media account and analyzes the posted content. The collection unit can also analyze the posted content of accounts the user follows and collect related information. For example, the collection unit analyzes the posted content of accounts the user follows and identifies related information. The collection unit can also collect related information based on the activities of groups and communities in which the user participates. For example, the collection unit analyzes the posted content of groups the user participates in and identifies related information. In this way, related information can be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's social media data into a generation AI and causes the generation AI to collect related information.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. For example, the analysis unit analyzes the content of a user's emails to identify emails with high importance. The analysis unit can also perform a simplified analysis of information with low importance. For example, the analysis unit analyzes the user's file access history to identify files with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the information. For example, the analysis unit analyzes a user's activity log and prioritizes analysis of information with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the importance of the information to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit analyzes the content of a user's email or message and extracts information using natural language processing technology. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit analyzes photos stored in a user's album and extracts information using image recognition technology. The analysis unit can also apply a voice recognition algorithm to audio data. For example, the analysis unit analyzes a user's voice message and extracts information using voice recognition technology. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of information to a generation AI and have the generation AI apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on when the information was collected. The analysis unit, for example, prioritizes analyzing the most recent information. For example, the analysis unit analyzes the creation date and update date of files stored on the user's PC to identify the most recent files. The analysis unit can also analyze older information as needed. For example, the analysis unit analyzes the user's file access history to identify older files. The analysis unit can also adjust the analysis priority based on when the information was collected. For example, the analysis unit analyzes the user's activity log and determines the analysis priority based on when the information was collected. This allows the most recent information to be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the information collection date and time to the generation AI, causing the generation AI to determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, the analysis unit analyzes the contents of a user's emails or messages to identify highly relevant information. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit analyzes a user's file access history to identify less relevant files. The analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit analyzes a user's activity log and determines the order of analysis based on the relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the information to a generation AI and have the generation AI adjust the order of analysis.

[0044] The generation unit can adjust the level of detail of the body double robot based on the importance of the analysis results during generation. The generation unit generates a detailed body double robot, for example, based on an analysis result with a high level of importance. For example, the generation unit analyzes the content of a user's emails or messages and generates a body double robot based on information with a high level of importance. The generation unit can also generate a simplified body double robot based on an analysis result with a low level of importance. For example, the generation unit analyzes a user's file access history and generates a body double robot based on information with a low level of importance. The generation unit can also adjust the level of detail of the body double robot according to the importance of the analysis result. For example, the generation unit analyzes a user's activity log and determines the level of detail of the body double robot based on the importance. This enables efficient generation of a body double robot by adjusting the level of detail of the body double robot based on the importance of the analysis result. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis results into the generation AI, and have the generation AI adjust the level of detail of the body double robot.

[0045] The generation unit can apply different generation algorithms depending on the user's behavioral patterns during generation. The generation unit generates a body double robot corresponding to, for example, tasks frequently performed by the user. For example, the generation unit analyzes the user's behavioral patterns and identifies the tasks frequently performed. The generation unit can also generate a body double robot that matches the user's communication style. For example, the generation unit analyzes the content of the user's emails and messages and identifies the communication style. The generation unit can also apply an optimal generation algorithm based on the user's behavioral patterns. For example, the generation unit analyzes the user's activity log and selects a generation algorithm based on the behavioral patterns. This improves the accuracy of generating a body double robot by applying the optimal generation algorithm depending on the user's behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the behavioral patterns into a generation AI and have the generation AI apply the optimal generation algorithm.

[0046] The generation unit can determine the priority of the body double robots based on the time when the analysis results were collected during generation. The generation unit determines the priority of the body double robots based on, for example, the most recent analysis results. For example, the generation unit analyzes the creation and update dates and times of files stored on the user's PC to identify the most recent files. The generation unit can also adjust the priority of body double robots for older analysis results as needed. For example, the generation unit analyzes the user's file access history to identify older files. The generation unit can also determine the priority of the body double robots based on the time when the analysis results were collected. For example, the generation unit analyzes the user's activity log and determines the priority of the body double robots based on the time when the analysis results were collected. In this way, body double robots based on the most recent information can be generated by determining the priority of the body double robots based on the time when the analysis results were collected. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and cause the generation AI to determine the priority of the body double robots.

[0047] The generation unit can adjust the order in which double robots are generated based on the relevance of the analysis results during generation. The generation unit determines the order in which double robots are generated based on, for example, highly relevant analysis results. For example, the generation unit analyzes the content of a user's emails or messages and generates double robots based on highly relevant information. The generation unit can also postpone analysis results with low relevance. For example, the generation unit analyzes a user's file access history and generates double robots based on less relevant information. The generation unit can also adjust the order in which double robots are generated based on the relevance of the analysis results. For example, the generation unit analyzes a user's activity log and determines the order in which double robots are generated based on the relevance. This enables efficient generation of double robots by adjusting the order in which double robots are generated based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI adjust the order in which double robots are generated.

[0048] When responding to a task, the response unit can select the optimal response method by referring to the user's past response history. The response unit selects the optimal response method, for example, based on the user's past response methods. For example, the response unit analyzes the content of the user's emails and messages to identify the user's past response history. The response unit can also select an effective response method from the user's past response history. For example, the response unit analyzes the user's file access history to identify the user's past response history. The response unit can also analyze the user's past response history to select the most efficient response method. For example, the response unit analyzes the user's activity log and determines the optimal response method based on the past response history. In this way, the optimal response method can be selected by referring to the past response history. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the past response history into a generation AI and have the generation AI select the optimal response method.

[0049] The response unit can customize a response measure based on the user's current work situation when responding to a task. For example, the response unit prioritizes tasks related to a project currently in progress. For example, the response unit analyzes data from the user's project management tool to identify related tasks. The response unit can also adjust the response measure based on the user's current workload. For example, the response unit analyzes the user's work log to identify the workload. The response unit can also propose an optimal response measure based on the user's current work situation. For example, the response unit analyzes the user's activity log and determines a response measure based on the work situation. This enables efficient work response by customizing the response measure based on the current work situation. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit can input the current work situation into a generation AI and have the generation AI select an optimal response measure.

[0050] When responding to a task, the response unit can select the optimal response method by taking into account the user's geographical location information. For example, if the user is in a specific area, the response unit prioritizes tasks related to that area. For example, the response unit analyzes the user's GPS data and identifies tasks related to the user's current location. Furthermore, if the user is on a business trip, the response unit can prioritize tasks related to the business trip destination. For example, the response unit analyzes the user's travel plans and identifies tasks related to the business trip destination. Furthermore, if the user is participating in a specific event, the response unit can prioritize tasks related to the event. For example, the response unit analyzes the user's calendar information and identifies tasks related to the event. This allows the optimal response method to be selected by taking the geographical location information into account. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit may input the user's GPS data into a generation AI and cause the generation AI to select the optimal response method.

[0051] The response unit can analyze the user's social media activity and suggest response measures when responding to a business. The response unit can suggest related business responses based on, for example, information shared by the user on social media. For example, the response unit accesses the user's social media account and analyzes the posted content. The response unit can also analyze the posted content of accounts followed by the user and suggest related business responses. For example, the response unit analyzes the posted content of accounts followed by the user and identifies related business. The response unit can also suggest related business responses based on the activity of groups or communities in which the user participates. For example, the response unit analyzes the posted content of groups in which the user participates and identifies related business. In this way, related business responses can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's social media data into a generation AI and have the generation AI suggest related business responses.

[0052] The adaptation unit can select an optimal style by referring to the user's past communication history during communication. The adaptation unit selects the optimal style based on, for example, the user's past communication styles. For example, the adaptation unit analyzes the content of the user's emails and messages to identify the user's past communication history. The adaptation unit can also select an effective style from the user's past communication history. For example, the adaptation unit analyzes the user's file access history to identify the user's past communication history. The adaptation unit can also analyze the user's past communication history to select the most efficient style. For example, the adaptation unit analyzes the user's activity log and determines the optimal style based on the past communication history. In this way, the optimal style can be selected by referring to the past communication history. Some or all of the above-described processing in the adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the adaptation unit can input the past communication history into a generation AI and cause the generation AI to select the optimal style.

[0053] The adaptation unit can customize the style based on the user's current situation during communication. For example, the adaptation unit prioritizes a communication style related to a project currently underway by the user. For example, the adaptation unit analyzes data from the user's project management tool to identify a related communication style. The adaptation unit can also adjust the communication style according to the user's current workload. For example, the adaptation unit analyzes the user's work log to identify the workload. The adaptation unit can also suggest an optimal communication style based on the user's current situation. For example, the adaptation unit analyzes the user's activity log and determines a communication style based on the situation. This enables efficient communication by customizing the style based on the current situation. Some or all of the above-described processing in the adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the adaptation unit can input the current situation into a generation AI and cause the generation AI to select an optimal style.

[0054] The adaptation unit can select an optimal style during communication by taking into account the user's geographical location information. For example, when the user is in a specific area, the adaptation unit prioritizes a communication style related to that area. For example, the adaptation unit analyzes the user's GPS data and identifies a communication style related to the user's current location. Furthermore, when the user is on a business trip, the adaptation unit can prioritize a communication style related to the business trip destination. For example, the adaptation unit analyzes the user's travel plans and identifies a communication style related to the business trip destination. Furthermore, when the user is participating in a specific event, the adaptation unit can prioritize a communication style related to the event. For example, the adaptation unit analyzes the user's calendar information and identifies a communication style related to the event. This allows the optimal communication style to be selected by taking into account the geographical location information. Some or all of the above-described processing in the adaptation unit may be performed using, or without, AI. For example, the adaptation unit can input the user's GPS data into a generation AI and cause the generation AI to select the optimal style.

[0055] The adaptation unit can analyze the user's social media activity during communication to suggest a style. The adaptation unit can suggest a relevant communication style based on, for example, information shared by the user on social media. For example, the adaptation unit accesses the user's social media account and analyzes the posted content. The adaptation unit can also analyze the posted content of accounts the user follows and suggest a relevant communication style. For example, the adaptation unit can analyze the posted content of accounts the user follows and identify a relevant style. The adaptation unit can also suggest a relevant communication style based on the activity of groups or communities the user participates in. For example, the adaptation unit can analyze the posted content of groups the user participates in and identify a relevant style. In this way, a relevant communication style can be suggested by analyzing the social media activity. Some or all of the above-described processing in the adaptation unit can be performed using, for example, AI, or can be performed without using AI. For example, the adaptation unit can input the user's social media data into a generation AI and cause the generation AI to suggest a relevant style.

[0056] When managing a schedule, the management unit can select an optimal management method by referring to the user's past schedule history. The management unit selects the optimal management method, for example, based on schedule management methods used by the user in the past. For example, the management unit analyzes the user's calendar information to identify the user's past schedule history. The management unit can also select an effective management method from the user's past schedule history. For example, the management unit analyzes the user's activity log to identify the past schedule history. The management unit can also analyze the user's past schedule history to select the most efficient management method. For example, the management unit analyzes the user's activity log and determines the optimal management method based on the past schedule history. In this way, the optimal management method can be selected by referring to the past schedule history. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the past schedule history into a generation AI and have the generation AI select the optimal management method.

[0057] The management unit can select the optimal schedule management method by taking into account the user's device information when managing the schedule. For example, if the user is using a smartphone, the management unit provides a schedule management method tailored to the screen size. For example, the management unit analyzes the user's device information and identifies a schedule management method optimized for smartphones. Furthermore, if the user is using a tablet, the management unit can also provide a schedule management method optimized for large screens. For example, the management unit analyzes the user's device information and identifies a schedule management method optimized for tablets. Furthermore, if the user is using a smartwatch, the management unit can also provide a simple and highly visible schedule management method. For example, the management unit analyzes the user's device information and identifies a schedule management method optimized for smartwatches. This allows the optimal schedule management method to be selected by taking the device information into account. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit inputs the user's device information into a generation AI, causing the generation AI to select the optimal management method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The body double robot system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's biometric data and analyzes the user's health condition. For example, the health management unit may monitor the user's heart rate, blood pressure, and sleep patterns and issue an alert if an abnormality is detected. The health management unit may also adjust the body double robot's work response based on the user's health condition. For example, if the user is tired, the body double robot may be allowed to take on more work. This makes it possible to respond to work taking the user's health condition into consideration, thereby contributing to maintaining the user's health.

[0060] The body double robot system may further include a smart home management unit that manages smart devices in the user's home. The smart home management unit controls the smart devices in the home based on the user's instructions. For example, the smart home management unit can instruct the user to turn on the air conditioner while the user is out. The smart home management unit can also learn the user's lifestyle patterns and automatically control devices. For example, if the user wakes up at a specific time every morning, the smart home management unit can turn on the lights at that time. This can make the user's life more comfortable.

[0061] The body double robot system may further include a financial management unit that assists the user in managing his or her finances. The financial management unit monitors the user's income and expenses and provides appropriate financial advice. For example, the financial management unit may analyze the user's spending patterns and provide advice on saving. The financial management unit may also create a savings plan based on the user's goals. Furthermore, the financial management unit may manage the user's investment portfolio and suggest optimal investment strategies. This may improve the user's financial situation.

[0062] The body double robot system can also be equipped with a security management unit to ensure user safety. The security management unit monitors the security of the user's device and network and issues an alert if an abnormality is detected. For example, it can immediately notify the user if an unauthorized access attempt is made to the user's PC. The security management unit can also encrypt user data to enhance security. Furthermore, the security management unit can monitor the user's online activity and protect against phishing scams and malware. This helps keep the user's digital life safe.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects the user's personal information. The user's personal information includes information on the user's PC, network drives, LINE, and albums. The collection unit scans files and folders stored on the user's PC or network drives to collect information. It can also collect LINE message history and photo data from albums. For example, the collection unit uses software installed on the user's PC to collect metadata about files and folders. It can also access network drives and collect stored data. It can also obtain message history using the LINE API. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses data mining technology and machine learning algorithms to analyze the user's behavioral patterns and communication style. For example, it can analyze the user's email sending and receiving history to identify the people with whom they frequently communicate and the phrases they frequently use. It can also analyze the user's file access history to identify frequently used files and folders. It can also analyze LINE message history to understand the user's communication style. Step 3: The generation unit creates a body double robot based on the analysis results obtained by the analysis unit. The generation unit generates a software agent or virtual assistant. For example, the generation unit sets the body double robot's behavior and responses based on the user's behavioral patterns and communication style. It can also generate a body double robot with the function of managing the user's schedule and tasks. Furthermore, the appearance and voice of the body double robot can be customized to suit the user's preferences. Step 4: The response unit uses the body double robot created by the generation unit to handle business operations. The response unit sends and receives emails on behalf of the user. It can also handle phone calls on behalf of the user. For example, the response unit can access the user's email account and automatically reply to received emails. It can also receive and answer calls forwarded to the user's phone number. It can also set up meetings and send reminders based on the user's schedule. Step 5: The adaptation unit adapts to the user's communication style. The adaptation unit adjusts the body double's responses to match the user's email writing style and speaking style. For example, if the user sends a formal email, the body double will also reply in a formal style. On the other hand, if the user sends an informal email, the body double will also reply in an informal style. Furthermore, the body double's voice responses can be adjusted to match the user's speaking style.

[0065] (Example 2) A body double robot system according to an embodiment of the present invention collects and analyzes a user's personal information, creates a body double robot, and handles daily tasks. This body double robot system uses AI to learn personal information about the user, such as the user's PC, network drive, LINE message history, and albums, to create a body double robot for the user. The body double robot then handles all simple communication and business tasks, such as daily tasks and email correspondence, thereby improving productivity. For example, the body double robot system collects personal information about the user, such as the user's PC, network drive, LINE message history, and albums. The body double robot system then analyzes the collected information to understand the user's behavioral patterns and communication style. The body double robot system then creates a body double robot based on the analyzed information. The body double robot handles daily tasks and email correspondence on behalf of the user. Furthermore, the body double robot adapts to the user's communication style. This allows the user to save time on daily tasks and email correspondence and improve productivity. The body double robot system collects and analyzes the user's personal information, creates a body double robot, and handles daily tasks, thereby improving productivity. LINE is an example of a messaging app.

[0066] A body double robot system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a correspondence unit, and an adaptation unit. The collection unit collects personal information about a user. Examples of the user's personal information include, but are not limited to, information stored on a PC, a network drive, a LINE message history, and an album. The collection unit, for example, scans files and folders stored on the user's PC or a network drive to collect information. The collection unit can also collect LINE message history and photo data from albums. For example, the collection unit collects metadata about files and folders using software installed on the user's PC. The collection unit can also access network drives and collect stored data. The collection unit can also obtain message history using the LINE API. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's behavioral patterns and communication style using, for example, data mining technology or machine learning algorithms. For example, the analysis unit can analyze the user's email sending and receiving history to identify frequent contacts and frequently used phrases. The analysis unit can also analyze the user's file access history and identify frequently used files and folders. Furthermore, the analysis unit can analyze the LINE message history and understand the user's communication style. The generation unit creates a body double robot based on the analysis results obtained by the analysis unit. The generation unit generates, for example, a software agent or a virtual assistant. For example, the generation unit sets the body double robot's actions and responses based on the user's behavior patterns and communication style. The generation unit can also generate a body double robot with the function of managing the user's schedule and tasks. Furthermore, the generation unit can customize the body double robot's appearance and voice to suit the user's preferences. The response unit causes the body double robot created by the generation unit to handle business operations. For example, the response unit can send and receive emails on behalf of the user. The response unit can also handle phone calls on behalf of the user.For example, the response unit can access the user's email account and automatically reply to received emails. The response unit can also receive and answer calls forwarded to the user's phone number. Furthermore, the response unit can schedule meetings and send reminders based on the user's schedule. The adaptation unit adapts to the user's communication style. For example, the adaptation unit adjusts the body double's responses to match the user's email writing style and speaking style. For example, if the user sends a formal email, the adaptation unit can also make the body double reply in a formal style. Furthermore, if the user sends an informal email, the adaptation unit can also make the body double reply in an informal style. Furthermore, the adaptation unit can adjust the voice responses of the body double to match the user's speaking style. As a result, the body double robot system according to the embodiment can collect and analyze the user's personal information, create a body double, and handle business operations, thereby improving productivity.

[0067] The collection unit can collect personal information from at least one of the user's PC or network drive, LINE message history, and album. The collection unit, for example, scans files and folders stored on the user's PC to collect the information. For example, the collection unit can collect metadata about files and folders using software installed on the user's PC. The collection unit can also access a network drive and collect stored data. For example, the collection unit can obtain file and folder information using a network drive's API. The collection unit can also collect LINE message history and photo data from albums. For example, the collection unit can obtain message history using a LINE API. The collection unit can also download photo data from albums from cloud storage. This improves the accuracy of the body double robot by collecting the user's personal information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information about files and folders stored on the user's PC into a generation AI, causing the generation AI to collect the information.

[0068] The analysis unit analyzes the information collected by the collection unit to understand the user's behavioral patterns and communication style. The analysis unit analyzes the user's behavioral patterns and communication style, for example, using data mining technology or machine learning algorithms. For example, the analysis unit analyzes the user's email sending and receiving history to identify frequent contacts and frequently used phrases. The analysis unit can also analyze the user's file access history to identify frequently used files and folders. For example, the analysis unit analyzes the access log of files stored on the user's PC to identify frequently accessed files. The analysis unit can also analyze the LINE message history to understand the user's communication style. For example, the analysis unit analyzes the content of LINE messages to identify frequently used expressions and patterns of interaction with specific contacts. By understanding the user's behavioral patterns and communication style, the body double robot can respond more appropriately. Some or all of the above-mentioned processing by the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the collected information into the generation AI, which can then analyze behavioral patterns and communication styles.

[0069] The generation unit can create a body double robot based on the analysis results obtained by the analysis unit. The generation unit generates, for example, a software agent or a virtual assistant. For example, the generation unit sets the body double robot's behavior and responses based on the user's behavioral patterns and communication style. The generation unit can also generate a body double robot with a function for managing the user's schedule and tasks. For example, the generation unit can add a schedule management function to the body double robot based on the user's calendar information. The generation unit can also customize the body double robot's appearance and voice to suit the user's preferences. For example, the generation unit applies an avatar and voice selected by the user to the body double robot. This allows the body double robot to be created based on the analysis results and perform tasks on behalf of the user. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate a body double robot.

[0070] The response unit allows the body double robot to handle daily business or email correspondence on behalf of the user. The response unit, for example, sends and receives emails on behalf of the user. For example, the response unit accesses the user's email account and automatically replies to received emails. The response unit can also handle phone calls on behalf of the user. For example, the response unit can receive and answer calls forwarded to the user's phone number. Furthermore, the response unit can set up meetings and send reminders based on the user's schedule. For example, the response unit can send meeting invitations and set reminders based on the user's calendar information. This reduces the burden on the user by having the body double robot handle business tasks. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without AI. For example, the response unit can input the content of an email into a generation AI and have the generation AI generate a reply.

[0071] The adaptation unit enables the body double robot to adapt to the user's communication style. The adaptation unit adjusts the body double's responses to, for example, the user's email writing style or speaking style. For example, if the user sends a formal email, the adaptation unit causes the body double to reply in a formal style. Furthermore, if the user sends an informal email, the adaptation unit causes the body double to reply in an informal style. Furthermore, the adaptation unit can adjust the body double's voice responses to adapt to the user's speaking style. For example, if the user speaks in a calm tone, the adaptation unit causes the body double to reply in a similar tone. Furthermore, if the user speaks quickly, the adaptation unit causes the body double to reply in a fast-paced manner. This allows the body double to adapt to the user's communication style, enabling more natural communication. Some or all of the above-described processing in the adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the adaptation unit can input the user's email or voice data into the generation AI and have the generation AI adjust the response content.

[0072] The body double robot system includes a management unit in which the body double robot manages the user's schedule. The management unit manages the user's schedule. The management unit adds, changes, and deletes schedules based on the user's calendar information, for example. For example, the management unit adds new events to the user's calendar, changes existing events, and deletes unnecessary events. The management unit can also set reminders and prioritize tasks. For example, the management unit can set reminders and prioritize tasks based on the user's schedule. The management unit can also synchronize the user's schedule with other devices. For example, the management unit can synchronize the schedule with the user's smartphone or tablet, allowing the schedule to be viewed from any device. This allows the body double robot to manage the schedule, thereby improving the efficiency of the user's time management. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input the user's calendar information into a generation AI and have the generation AI perform schedule management.

[0073] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the user is relaxed. For example, the collection unit analyzes the user's facial expressions and voice to estimate the stress level. Furthermore, if the user is concentrating, the collection unit can temporarily stop information collection and resume collection when the user's concentration is broken. For example, the collection unit analyzes the user's work log to identify the time period when the user is concentrating. Furthermore, if the user is tired, the collection unit can collect information at night and complete the collection while the user is resting. For example, the collection unit analyzes the user's biometric data to estimate the user's fatigue level. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI, and have the generation AI estimate the emotion.

[0074] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit prioritizes collection of files and folders that the user frequently accessed in the past. For example, the collection unit analyzes the access log of files stored on the user's PC and identifies frequently accessed files. The collection unit can also suggest the optimal collection method based on the collection method (manual, scheduled, etc.) used by the user in the past. For example, the collection unit analyzes the user's past collection methods and selects the most efficient method. The collection unit can also select a collection method for a specific time period based on the user's past information collection history. For example, the collection unit analyzes the user's activity log and identifies the optimal collection timing. This allows the optimal collection method to be selected by analyzing the past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the past information collection history into a generation AI and have the generation AI select the optimal collection method.

[0075] When collecting information, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting information related to the user's ongoing project. For example, the collection unit can analyze data from the user's project management tool to identify relevant information. The collection unit can also collect only relevant information based on the user's area of ​​interest. For example, the collection unit can analyze the user's browsing history or search history to identify the area of ​​interest. The collection unit can also filter and collect information based on keywords set by the user. For example, the collection unit collects relevant information based on keywords specified by the user. This makes it possible to collect highly relevant information by filtering the information based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's project data into a generation AI and have the generation AI filter the information.

[0076] The collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important information and prioritizes collecting more important information. For example, the collection unit analyzes the user's facial expressions and voice to estimate the stress level. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. For example, the collection unit analyzes the user's biometric data to estimate the user's relaxation level. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. For example, the collection unit analyzes the user's work log and identifies time periods when the user is in a hurry. Thus, by prioritizing information according to the user's emotions, important information can be prioritized and collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0077] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit analyzes the user's GPS data and identifies information related to the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting information related to the travel destination. For example, the collection unit analyzes the user's travel plans and identifies information related to the travel destination. Furthermore, if the user is participating in a specific event, the collection unit can prioritize collecting information related to the event. For example, the collection unit analyzes the user's calendar information and identifies information related to the event. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's GPS data into a generation AI and cause the generation AI to collect related information.

[0078] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit collects related information based on, for example, information shared by the user on social media. For example, the collection unit accesses the user's social media account and analyzes the posted content. The collection unit can also analyze the posted content of accounts the user follows and collect related information. For example, the collection unit analyzes the posted content of accounts the user follows and identifies related information. The collection unit can also collect related information based on the activities of groups and communities in which the user participates. For example, the collection unit analyzes the posted content of groups the user participates in and identifies related information. In this way, related information can be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's social media data into a generation AI and causes the generation AI to collect related information.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit analyzes the user's facial expressions and voice to estimate the tension level. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit analyzes the user's biometric data to estimate the relaxation level. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the key points. For example, the analysis unit analyzes the user's work log to identify time periods when the user is in a hurry. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. For example, the analysis unit analyzes the content of a user's emails to identify emails with high importance. The analysis unit can also perform a simplified analysis of information with low importance. For example, the analysis unit analyzes the user's file access history to identify files with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the information. For example, the analysis unit analyzes a user's activity log and prioritizes analysis of information with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the importance of the information to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit analyzes the content of a user's email or message and extracts information using natural language processing technology. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit analyzes photos stored in a user's album and extracts information using image recognition technology. The analysis unit can also apply a voice recognition algorithm to audio data. For example, the analysis unit analyzes a user's voice message and extracts information using voice recognition technology. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of information to a generation AI and have the generation AI apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit analyzes the user's facial expressions and voice to estimate that the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit analyzes the user's biometric data to estimate that the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, the analysis unit analyzes the user's work log to estimate that the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0083] During analysis, the analysis unit can determine the analysis priority based on when the information was collected. The analysis unit, for example, prioritizes analyzing the most recent information. For example, the analysis unit analyzes the creation date and update date of files stored on the user's PC to identify the most recent files. The analysis unit can also analyze older information as needed. For example, the analysis unit analyzes the user's file access history to identify older files. The analysis unit can also adjust the analysis priority based on when the information was collected. For example, the analysis unit analyzes the user's activity log and determines the analysis priority based on when the information was collected. This allows the most recent information to be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the information collection date and time to the generation AI, causing the generation AI to determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, the analysis unit analyzes the contents of a user's emails or messages to identify highly relevant information. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit analyzes a user's file access history to identify less relevant files. The analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit analyzes a user's activity log and determines the order of analysis based on the relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the information to a generation AI and have the generation AI adjust the order of analysis.

[0085] The generation unit can estimate the user's emotions and adjust the generation method of the body double robot based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a body double robot that moves at a leisurely pace. For example, the generation unit analyzes the user's facial expressions and voice to estimate the user's relaxation level. Furthermore, if the user is in a hurry, the generation unit can generate a body double robot that can respond quickly. For example, the generation unit analyzes the user's work log to estimate the user's hurry. Furthermore, if the user is excited, the generation unit can generate a body double robot with visually stimulating effects. For example, the generation unit analyzes the user's biometric data to estimate the user's excitement level. By adjusting the generation method of the body double robot according to the user's emotions, a more appropriate body double robot can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0086] The generation unit can adjust the level of detail of the body double robot based on the importance of the analysis results during generation. The generation unit generates a detailed body double robot, for example, based on an analysis result with a high level of importance. For example, the generation unit analyzes the content of a user's emails or messages and generates a body double robot based on information with a high level of importance. The generation unit can also generate a simplified body double robot based on an analysis result with a low level of importance. For example, the generation unit analyzes a user's file access history and generates a body double robot based on information with a low level of importance. The generation unit can also adjust the level of detail of the body double robot according to the importance of the analysis result. For example, the generation unit analyzes a user's activity log and determines the level of detail of the body double robot based on the importance. This enables efficient generation of a body double robot by adjusting the level of detail of the body double robot based on the importance of the analysis result. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis results into the generation AI, and have the generation AI adjust the level of detail of the body double robot.

[0087] The generation unit can apply different generation algorithms depending on the user's behavioral patterns during generation. The generation unit generates a body double robot corresponding to, for example, tasks frequently performed by the user. For example, the generation unit analyzes the user's behavioral patterns and identifies the tasks frequently performed. The generation unit can also generate a body double robot that matches the user's communication style. For example, the generation unit analyzes the content of the user's emails and messages and identifies the communication style. The generation unit can also apply an optimal generation algorithm based on the user's behavioral patterns. For example, the generation unit analyzes the user's activity log and selects a generation algorithm based on the behavioral patterns. This improves the accuracy of generating a body double robot by applying the optimal generation algorithm depending on the user's behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the behavioral patterns into a generation AI and have the generation AI apply the optimal generation algorithm.

[0088] The generation unit can estimate the user's emotions and adjust the order in which the body double robots are generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit prioritizes generating a body double robot that can be generated quickly. For example, the generation unit analyzes the user's facial expressions and voice to estimate that the user is in a hurry. Furthermore, if the user is relaxed, the generation unit can prioritize generating a detailed body double robot. For example, the generation unit analyzes the user's biometric data to estimate that the user is relaxed. Furthermore, if the user is excited, the generation unit can prioritize generating a visually stimulating body double robot. For example, the generation unit analyzes the user's work log to estimate that the user is excited. By adjusting the order in which the body double robots are generated based on the user's emotions, more appropriate body double robots can be generated. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0089] The generation unit can determine the priority of the body double robots based on the time when the analysis results were collected during generation. The generation unit determines the priority of the body double robots based on, for example, the most recent analysis results. For example, the generation unit analyzes the creation and update dates and times of files stored on the user's PC to identify the most recent files. The generation unit can also adjust the priority of body double robots for older analysis results as needed. For example, the generation unit analyzes the user's file access history to identify older files. The generation unit can also determine the priority of the body double robots based on the time when the analysis results were collected. For example, the generation unit analyzes the user's activity log and determines the priority of the body double robots based on the time when the analysis results were collected. In this way, body double robots based on the most recent information can be generated by determining the priority of the body double robots based on the time when the analysis results were collected. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and cause the generation AI to determine the priority of the body double robots.

[0090] The generation unit can adjust the order in which double robots are generated based on the relevance of the analysis results during generation. The generation unit determines the order in which double robots are generated based on, for example, highly relevant analysis results. For example, the generation unit analyzes the content of a user's emails or messages and generates double robots based on highly relevant information. The generation unit can also postpone analysis results with low relevance. For example, the generation unit analyzes a user's file access history and generates double robots based on less relevant information. The generation unit can also adjust the order in which double robots are generated based on the relevance of the analysis results. For example, the generation unit analyzes a user's activity log and determines the order in which double robots are generated based on the relevance. This enables efficient generation of double robots by adjusting the order in which double robots are generated based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI adjust the order in which double robots are generated.

[0091] The response unit can estimate the user's emotions and adjust the task response method based on the estimated user emotions. For example, if the user is feeling stressed, the response unit prioritizes simple task responses. For example, the response unit analyzes the user's facial expressions and voice to estimate the user's stress level. Furthermore, if the user is relaxed, the response unit can also perform detailed task responses. For example, the response unit analyzes the user's biometric data to estimate the user's relaxation level. Furthermore, if the user is in a hurry, the response unit can prioritize tasks that can be handled quickly. For example, the response unit analyzes the user's work log to estimate the user's hurry. This allows for more appropriate task response by adjusting the task response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correspondence unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0092] When responding to a task, the response unit can select the optimal response method by referring to the user's past response history. The response unit selects the optimal response method, for example, based on the user's past response methods. For example, the response unit analyzes the content of the user's emails and messages to identify the user's past response history. The response unit can also select an effective response method from the user's past response history. For example, the response unit analyzes the user's file access history to identify the user's past response history. The response unit can also analyze the user's past response history to select the most efficient response method. For example, the response unit analyzes the user's activity log and determines the optimal response method based on the past response history. In this way, the optimal response method can be selected by referring to the past response history. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the past response history into a generation AI and have the generation AI select the optimal response method.

[0093] The response unit can customize a response measure based on the user's current work situation when responding to a task. For example, the response unit prioritizes tasks related to a project currently in progress. For example, the response unit analyzes data from the user's project management tool to identify related tasks. The response unit can also adjust the response measure based on the user's current workload. For example, the response unit analyzes the user's work log to identify the workload. The response unit can also propose an optimal response measure based on the user's current work situation. For example, the response unit analyzes the user's activity log and determines a response measure based on the work situation. This enables efficient work response by customizing the response measure based on the current work situation. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit can input the current work situation into a generation AI and have the generation AI select an optimal response measure.

[0094] The response unit can estimate the user's emotions and determine the priority of tasks based on the estimated user emotions. For example, if the user is feeling stressed, the response unit postpones less important tasks and prioritizes more important tasks. For example, the response unit analyzes the user's facial expressions and voice to estimate the user's stress level. Furthermore, if the user is relaxed, the response unit can prioritize detailed tasks. For example, the response unit analyzes the user's biometric data to estimate the user's relaxation level. Furthermore, if the user is in a hurry, the response unit can prioritize tasks that can be handled quickly. For example, the response unit analyzes the user's work log to estimate the user's hurry. Thus, by determining the priority of tasks based on the user's emotions, important tasks can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the correspondence unit may be performed using, for example, AI, or may be performed without using AI. For example, the correspondence unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0095] When responding to a task, the response unit can select the optimal response method by taking into account the user's geographical location information. For example, if the user is in a specific area, the response unit prioritizes tasks related to that area. For example, the response unit analyzes the user's GPS data and identifies tasks related to the user's current location. Furthermore, if the user is on a business trip, the response unit can prioritize tasks related to the business trip destination. For example, the response unit analyzes the user's travel plans and identifies tasks related to the business trip destination. Furthermore, if the user is participating in a specific event, the response unit can prioritize tasks related to the event. For example, the response unit analyzes the user's calendar information and identifies tasks related to the event. This allows the optimal response method to be selected by taking the geographical location information into account. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit may input the user's GPS data into a generation AI and cause the generation AI to select the optimal response method.

[0096] The response unit can analyze the user's social media activity and suggest response measures when responding to a business. The response unit can suggest related business responses based on, for example, information shared by the user on social media. For example, the response unit accesses the user's social media account and analyzes the posted content. The response unit can also analyze the posted content of accounts followed by the user and suggest related business responses. For example, the response unit analyzes the posted content of accounts followed by the user and identifies related business. The response unit can also suggest related business responses based on the activity of groups or communities in which the user participates. For example, the response unit analyzes the posted content of groups in which the user participates and identifies related business. In this way, related business responses can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's social media data into a generation AI and have the generation AI suggest related business responses.

[0097] The adaptation unit can estimate the user's emotions and adjust the communication style based on the estimated user emotions. For example, if the user is nervous, the adaptation unit communicates in a calm tone. For example, the adaptation unit analyzes the user's facial expressions and voice to estimate the user's level of nervousness. Furthermore, if the user is relaxed, the adaptation unit can communicate in a friendly tone. For example, the adaptation unit analyzes the user's biometric data to estimate the user's level of relaxation. Furthermore, if the user is in a hurry, the adaptation unit can communicate quickly and concisely. For example, the adaptation unit analyzes the user's work log to estimate that the user is in a hurry. This enables more appropriate communication by adjusting the communication style according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adaptation unit can be performed using, for example, AI, or without AI. For example, the adaptation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] The adaptation unit can select an optimal style by referring to the user's past communication history during communication. The adaptation unit selects the optimal style based on, for example, the user's past communication styles. For example, the adaptation unit analyzes the content of the user's emails and messages to identify the user's past communication history. The adaptation unit can also select an effective style from the user's past communication history. For example, the adaptation unit analyzes the user's file access history to identify the user's past communication history. The adaptation unit can also analyze the user's past communication history to select the most efficient style. For example, the adaptation unit analyzes the user's activity log and determines the optimal style based on the past communication history. In this way, the optimal style can be selected by referring to the past communication history. Some or all of the above-described processing in the adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the adaptation unit can input the past communication history into a generation AI and cause the generation AI to select the optimal style.

[0099] The adaptation unit can customize the style based on the user's current situation during communication. For example, the adaptation unit prioritizes a communication style related to a project currently underway by the user. For example, the adaptation unit analyzes data from the user's project management tool to identify a related communication style. The adaptation unit can also adjust the communication style according to the user's current workload. For example, the adaptation unit analyzes the user's work log to identify the workload. The adaptation unit can also suggest an optimal communication style based on the user's current situation. For example, the adaptation unit analyzes the user's activity log and determines a communication style based on the situation. This enables efficient communication by customizing the style based on the current situation. Some or all of the above-described processing in the adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the adaptation unit can input the current situation into a generation AI and cause the generation AI to select an optimal style.

[0100] The adaptation unit can estimate the user's emotions and determine communication priorities based on the estimated user emotions. For example, if the user is feeling stressed, the adaptation unit postpones less important communications and prioritizes more important communications. For example, the adaptation unit analyzes the user's facial expressions and voice to estimate the stress level. Furthermore, if the user is relaxed, the adaptation unit can prioritize detailed communications. For example, the adaptation unit analyzes the user's biometric data to estimate the user's relaxation level. Furthermore, if the user is in a hurry, the adaptation unit can prioritize communications that can be responded to quickly. For example, the adaptation unit analyzes the user's work log to estimate that the user is in a hurry. Thus, by determining communication priorities according to the user's emotions, important communications can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adaptation unit may be performed using, for example, AI, or may be performed without using AI. For example, the adaptation unit may input the user's facial expression data to the generation AI and have the generation AI estimate the emotion.

[0101] The adaptation unit can select an optimal style during communication by taking into account the user's geographical location information. For example, when the user is in a specific area, the adaptation unit prioritizes a communication style related to that area. For example, the adaptation unit analyzes the user's GPS data and identifies a communication style related to the user's current location. Furthermore, when the user is on a business trip, the adaptation unit can prioritize a communication style related to the business trip destination. For example, the adaptation unit analyzes the user's travel plans and identifies a communication style related to the business trip destination. Furthermore, when the user is participating in a specific event, the adaptation unit can prioritize a communication style related to the event. For example, the adaptation unit analyzes the user's calendar information and identifies a communication style related to the event. This allows the optimal communication style to be selected by taking into account the geographical location information. Some or all of the above-described processing in the adaptation unit may be performed using, or without, AI. For example, the adaptation unit can input the user's GPS data into a generation AI and cause the generation AI to select the optimal style.

[0102] The adaptation unit can analyze the user's social media activity during communication to suggest a style. The adaptation unit can suggest a relevant communication style based on, for example, information shared by the user on social media. For example, the adaptation unit accesses the user's social media account and analyzes the posted content. The adaptation unit can also analyze the posted content of accounts the user follows and suggest a relevant communication style. For example, the adaptation unit can analyze the posted content of accounts the user follows and identify a relevant style. The adaptation unit can also suggest a relevant communication style based on the activity of groups or communities the user participates in. For example, the adaptation unit can analyze the posted content of groups the user participates in and identify a relevant style. In this way, a relevant communication style can be suggested by analyzing the social media activity. Some or all of the above-described processing in the adaptation unit can be performed using, for example, AI, or can be performed without using AI. For example, the adaptation unit can input the user's social media data into a generation AI and cause the generation AI to suggest a relevant style.

[0103] The management unit can estimate the user's emotions and adjust the schedule management method based on the estimated user emotions. For example, if the user is feeling stressed, the management unit adjusts the schedule more gently. For example, the management unit analyzes the user's facial expressions and voice to estimate the stress level. The management unit can also provide a detailed schedule if the user is relaxed. For example, the management unit analyzes the user's biometric data to estimate the user's relaxation level. The management unit can also provide a schedule that allows for quick responses if the user is in a hurry. For example, the management unit analyzes the user's work log to estimate the user's hurry. This enables more appropriate schedule management by adjusting the schedule management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the management unit can be performed using, for example, AI, or without AI. For example, the management unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0104] When managing a schedule, the management unit can select an optimal management method by referring to the user's past schedule history. The management unit selects the optimal management method, for example, based on schedule management methods used by the user in the past. For example, the management unit analyzes the user's calendar information to identify the user's past schedule history. The management unit can also select an effective management method from the user's past schedule history. For example, the management unit analyzes the user's activity log to identify the past schedule history. The management unit can also analyze the user's past schedule history to select the most efficient management method. For example, the management unit analyzes the user's activity log and determines the optimal management method based on the past schedule history. In this way, the optimal management method can be selected by referring to the past schedule history. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the past schedule history into a generation AI and have the generation AI select the optimal management method.

[0105] The management unit can estimate the user's emotions and determine the priority of schedule management based on the estimated user emotions. For example, when the user is feeling stressed, the management unit postpones less important schedules and prioritizes more important schedules. For example, the management unit analyzes the user's facial expressions and voice to estimate the stress level. Furthermore, when the user is relaxed, the management unit can prioritize detailed schedule management. For example, the management unit analyzes the user's biometric data to estimate the user's relaxation level. Furthermore, when the user is in a hurry, the management unit can prioritize schedules that can be handled quickly. For example, the management unit analyzes the user's work log to estimate the user's hurry. Thus, by determining the priority of schedule management according to the user's emotions, important schedules can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0106] The management unit can select the optimal schedule management method by taking into account the user's device information when managing the schedule. For example, if the user is using a smartphone, the management unit provides a schedule management method tailored to the screen size. For example, the management unit analyzes the user's device information and identifies a schedule management method optimized for smartphones. Furthermore, if the user is using a tablet, the management unit can also provide a schedule management method optimized for large screens. For example, the management unit analyzes the user's device information and identifies a schedule management method optimized for tablets. Furthermore, if the user is using a smartwatch, the management unit can also provide a simple and highly visible schedule management method. For example, the management unit analyzes the user's device information and identifies a schedule management method optimized for smartwatches. This allows the optimal schedule management method to be selected by taking the device information into account. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit inputs the user's device information into a generation AI, causing the generation AI to select the optimal management method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, response unit, adaptation unit, management unit, and emotion estimation function 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 personal information of the user using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing device 12. The generation unit creates a body double robot by the specific processing unit 290 of the data processing device 12. The response unit handles business operations by the control unit 46A of the smart device 14. The adaptation unit adapts to the user's communication style by the control unit 46A of the smart device 14. The management unit manages the user's schedule by the specific processing unit 290 of the data processing device 12. The emotion estimation function estimates the user's emotion by the specific processing unit 290 of the data processing device 12 and adjusts the information collection timing of the collection unit. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, response unit, adaptation unit, management unit, and emotion estimation function 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 personal information of the user using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing device 12. The generation unit creates a body double robot by the specific processing unit 290 of the data processing device 12. The response unit handles business operations by the control unit 46A of the smart glasses 214. The adaptation unit adapts to the user's communication style by the control unit 46A of the smart glasses 214. The management unit manages the user's schedule by the specific processing unit 290 of the data processing device 12. The emotion estimation function estimates the user's emotion by the specific processing unit 290 of the data processing device 12 and adjusts the information collection timing of the collection unit. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, response unit, adaptation unit, management unit, and emotion estimation function described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects personal information about the user using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing device 12. The generation unit creates a body double robot by the specific processing unit 290 of the data processing device 12. The response unit handles business operations by the control unit 46A of the headset-type terminal 314. The adaptation unit adapts to the user's communication style by the control unit 46A of the headset-type terminal 314. The management unit manages the user's schedule by the specific processing unit 290 of the data processing device 12. The emotion estimation function estimates the user's emotion by the specific processing unit 290 of the data processing device 12 and adjusts the information collection timing of the collection unit. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, response unit, adaptation unit, management unit, and emotion estimation function 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 personal information about the user using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing device 12. The generation unit creates a body double robot by the specific processing unit 290 of the data processing device 12. The response unit handles business operations by the control unit 46A of the robot 414. The adaptation unit adapts to the user's communication style by the control unit 46A of the robot 414. The management unit manages the user's schedule by the specific processing unit 290 of the data processing device 12. The emotion estimation function estimates the user's emotions by the specific processing unit 290 of the data processing device 12 and adjusts the timing of information collection by the collection unit.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The body double robot system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's biometric data and analyzes the user's health condition. For example, the health management unit may monitor the user's heart rate, blood pressure, and sleep patterns and issue an alert if an abnormality is detected. The health management unit may also adjust the body double robot's work response based on the user's health condition. For example, if the user is tired, the body double robot may be allowed to take on more work. This makes it possible to respond to work taking the user's health condition into consideration, thereby contributing to maintaining the user's health.

[0109] The body double robot system may further include an entertainment provider customized based on the user's hobbies and interests. The entertainment provider collects information about the user's hobbies and interests and provides appropriate entertainment content. For example, if the user is a movie lover, the entertainment provider can provide the latest movie information and recommended movies. If the user is a music lover, the entertainment provider can create and provide a playlist that matches the user's preferences. Furthermore, the entertainment provider can provide relaxing content or uplifting content according to the user's emotional state. This makes it possible to provide an entertainment experience based on the user's hobbies and interests.

[0110] The body double robot system may further include a learning support unit that supports the user's learning. The learning support unit provides appropriate learning content based on the user's learning history and interests. For example, if the user is interested in a particular field, it can provide learning materials and online courses related to that field. The learning support unit can also monitor the user's learning progress and adjust the learning plan as needed. Furthermore, the learning support unit can provide feedback and advice to increase the user's motivation according to the user's emotional state. This can improve the user's learning efficiency.

[0111] The body double robot system may further include a communication support unit that supports the user's communication with family and friends. The communication support unit monitors the user's interactions with family and friends and promotes communication at appropriate times. For example, it can remind the user to send a message to family and friends with whom the user has not been in contact for a long time. The communication support unit can also suggest appropriate communication methods based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing conversation topics. This helps maintain smooth interpersonal relationships between the user.

[0112] The body double robot system may further include a fitness support unit that supports the user's fitness activities. The fitness support unit provides an appropriate fitness plan based on the user's exercise history and goals. For example, if the user is trying to lose weight, the fitness support unit may provide an exercise plan to maximize calorie consumption. The fitness support unit may also monitor the user's exercise data and track progress. Furthermore, the fitness support unit may provide feedback and advice to increase motivation according to the user's emotional state. This may help the user achieve their fitness goals.

[0113] The body double robot system may further include a purchasing support unit that supports the user's purchasing activities. The purchasing support unit suggests appropriate products and services based on the user's purchasing history and preferences. For example, it may suggest related products based on products the user has previously purchased. The purchasing support unit may also analyze the user's purchasing patterns and suggest optimal timing for purchases. Furthermore, the purchasing support unit may suggest relaxation products and services to reduce stress based on the user's emotional state. This may improve the user's purchasing experience.

[0114] The body double robot system may further include a travel support unit that assists the user in planning a trip. The travel support unit suggests an appropriate travel plan based on the user's travel history and interests. For example, it can suggest new travel destinations based on places the user has visited in the past. The travel support unit can also create an optimal travel plan based on the user's schedule and budget. Furthermore, the travel support unit can suggest relaxing travel destinations and activities based on the user's emotional state. This can improve the user's travel experience.

[0115] The body double robot system may further include a smart home management unit that manages smart devices in the user's home. The smart home management unit controls the smart devices in the home based on the user's instructions. For example, the smart home management unit can instruct the user to turn on the air conditioner while the user is out. The smart home management unit can also learn the user's lifestyle patterns and automatically control devices. For example, if the user wakes up at a specific time every morning, the smart home management unit can turn on the lights at that time. This can make the user's life more comfortable.

[0116] The body double robot system may further include a financial management unit that assists the user in managing his or her finances. The financial management unit monitors the user's income and expenses and provides appropriate financial advice. For example, the financial management unit may analyze the user's spending patterns and provide advice on saving. The financial management unit may also create a savings plan based on the user's goals. Furthermore, the financial management unit may manage the user's investment portfolio and suggest optimal investment strategies. This may improve the user's financial situation.

[0117] The body double robot system can also be equipped with a security management unit to ensure user safety. The security management unit monitors the security of the user's device and network and issues an alert if an abnormality is detected. For example, it can immediately notify the user if an unauthorized access attempt is made to the user's PC. The security management unit can also encrypt user data to enhance security. Furthermore, the security management unit can monitor the user's online activity and protect against phishing scams and malware. This helps keep the user's digital life safe.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects the user's personal information. The user's personal information includes information on the user's PC, network drives, LINE, and albums. The collection unit scans files and folders stored on the user's PC or network drives to collect information. It can also collect LINE message history and photo data from albums. For example, the collection unit uses software installed on the user's PC to collect metadata about files and folders. It can also access network drives and collect stored data. It can also obtain message history using the LINE API. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses data mining technology and machine learning algorithms to analyze the user's behavioral patterns and communication style. For example, it can analyze the user's email sending and receiving history to identify the people with whom they frequently communicate and the phrases they frequently use. It can also analyze the user's file access history to identify frequently used files and folders. It can also analyze LINE message history to understand the user's communication style. Step 3: The generation unit creates a body double robot based on the analysis results obtained by the analysis unit. The generation unit generates a software agent or virtual assistant. For example, the generation unit sets the body double robot's behavior and responses based on the user's behavioral patterns and communication style. It can also generate a body double robot with the function of managing the user's schedule and tasks. Furthermore, the appearance and voice of the body double robot can be customized to suit the user's preferences. Step 4: The response unit uses the body double robot created by the generation unit to handle business operations. The response unit sends and receives emails on behalf of the user. It can also handle phone calls on behalf of the user. For example, the response unit can access the user's email account and automatically reply to received emails. It can also receive and answer calls forwarded to the user's phone number. It can also set up meetings and send reminders based on the user's schedule. Step 5: The adaptation unit adapts to the user's communication style. The adaptation unit adjusts the body double's responses to match the user's email writing style and speaking style. For example, if the user sends a formal email, the body double will also reply in a formal style. On the other hand, if the user sends an informal email, the body double will also reply in an informal style. Furthermore, the body double's voice responses can be adjusted to match the user's speaking style.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0127] The 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.

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

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

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

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

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

[0143] The 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.

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

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

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

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 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 for collecting personal information of users; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a body double robot based on the analysis results obtained by the analysis unit; a response unit in which the body double robot created by the generation unit responds to business operations; an adaptation unit that adapts to the user's communication style; A system characterized by:

2. The body double robot will have a management section that manages the user's schedule. The system of claim 1 .

3. The collecting unit Collect personal information from at least one of the following: the user's PC or network drive, message history, or album. The system of claim 1 .

4. The analysis unit Analyzing the information collected by the collection unit to understand the user's behavioral patterns and communication styles The system of claim 1 .

5. The generation unit A body double robot is created based on the analysis results obtained by the analysis unit. The system of claim 1 .

6. The corresponding part is A robot double handles daily tasks or emails on behalf of the user The system of claim 1 .

7. The adaptation unit A double robot responds to the user's communication style The system of claim 1 .

8. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The system of claim 1 .

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

10. The collecting unit As information is collected, it is filtered based on the user's current projects and areas of interest. The system of claim 1 .

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