System

Abstract

The system according to the embodiment is intended to support future design of a user and support execution of the plan.SOLUTION: A system according to an embodiment includes a hearing unit, an analysis unit, a report generation unit, a plan proposal unit, an execution support unit, and a progress management unit. The hearing unit collects input information of the user. The analysis unit analyzes the information collected by the hearing unit. The report generation unit generates a result analyzed by the analysis unit as a report. The plan proposal unit proposes a plan based on the report generated by the report generation unit. The execution support unit supports execution of the plan proposed by the plan proposal unit. The progress management unit manages progress of the plan executed by the execution support unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide a system that consistently supports users in planning for the future and executing their plans, and there is room for improvement.

[0005] The system according to the embodiment aims to support the user in planning for the future and to assist in carrying out the plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing unit, an analysis unit, a report generation unit, a plan proposal unit, an execution support unit, and a progress management unit. The hearing unit collects user input information. The analysis unit analyzes the information collected by the hearing unit. The report generation unit generates a report based on the results of the analysis by the analysis unit. The plan proposal unit proposes a plan based on the report generated by the report generation unit. The execution support unit provides support for executing the plan proposed by the plan proposal unit. The progress management unit manages the progress of the plan executed by the execution support unit. [Effects of the Invention]

[0007] The system according to the embodiment can support the user in planning for the future and assist in carrying out the plan. [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 life navigation system according to an embodiment of the present invention utilizes a generative AI to support a user's future planning and assist in its realization. The life navigation system collects information input by the user, which the generative AI analyzes, generates a report, proposes a plan, supports its implementation, and manages progress. For example, the life navigation system listens to the user's envisioned future and current situation, and the generative AI analyzes and provides a report. Next, the generative AI proposes a specific plan for the user to become their ideal self. Furthermore, the life navigation system provides support for executing the proposed plan and manages progress. This allows the life navigation system to efficiently support the user's future planning and its realization. For example, the user can have their own personal life navigator, allowing them to efficiently plan for their future and its realization.

[0029] The life navigation system according to the embodiment includes a hearing unit, an analysis unit, a report generation unit, a plan proposal unit, an execution support unit, and a progress management unit. The hearing unit collects user input information. The user input information includes, but is not limited to, text, audio, and images. The hearing unit collects user information using, for example, a questionnaire. The hearing unit can also collect user biometric data using a sensor. The hearing unit can also collect log data. For example, the hearing unit collects user input information and inputs it to the generation AI. The analysis unit analyzes the information collected by the hearing unit. The analysis can be performed using, for example, statistical analysis, machine learning, natural language processing, or other methods, but is not limited to these examples. For example, the analysis unit statistically analyzes the collected information to understand user trends. The analysis unit can also analyze user behavior patterns using machine learning. The analysis unit can also analyze user text data using natural language processing. The report generation unit generates a report based on the results of the analysis by the analysis unit. The report may be generated in, for example, a PDF format, an HTML format, a graph, or a text format, but is not limited to these examples. For example, the report generation unit generates a report of the analysis results in PDF format. The report generation unit can also generate a report of the analysis results in HTML format. The report generation unit can also include the analysis results in the report as a graph or text. The plan proposal unit proposes a plan based on the report generated by the report generation unit. The plan may be proposed in the form of, for example, a short-term plan, a long-term plan, or resource allocation, but is not limited to these examples. For example, the plan proposal unit proposes a plan based on the user's career goals. The plan proposal unit can also propose a plan based on improvements to the user's lifestyle. The plan proposal unit can also propose a plan based on the user's financial plan. The execution support unit provides support for executing the plan proposed by the plan proposal unit. The execution support may be performed using, for example, task management, project management, resource management, or other methods, but is not limited to these examples.For example, the execution support unit manages the user's tasks and supports the execution of the plan. The execution support unit can also perform project management and support the execution of the plan. Furthermore, the execution support unit can also perform resource management and support the execution of the plan. The progress management unit manages the progress of the plan executed by the execution support unit. Progress management is performed, for example, using methods such as progress rate, milestones, and task completion status, but is not limited to these examples. For example, the progress management unit manages the progress rate of the plan and provides feedback to the user. The progress management unit can also set milestones and manage the progress of the plan. Furthermore, the progress management unit can manage the completion status of tasks and manage the progress of the plan. As a result, the life navigation system according to the embodiment can efficiently support the user's future planning and its realization.

[0030] The hearing unit can hear about the user's envisioned future and current situation. The hearing unit, for example, hears about the user's envisioned future. For example, the hearing unit can hear about the user's career goals. The hearing unit can also hear about the user's goals for improving their lifestyle. Furthermore, the hearing unit can also hear about the user's financial plan. The hearing unit, for example, hears about the user's current situation. For example, the hearing unit can hear about the user's current occupation. The hearing unit can also hear about the user's income. Furthermore, the hearing unit can hear about the user's living situation. This makes it possible to accurately grasp the user's envisioned future and current situation.

[0031] The analysis unit can analyze the information collected by the hearing unit. The analysis unit, for example, statistically analyzes the collected information. For example, the analysis unit statistically analyzes information input by a user to understand the user's tendencies. The analysis unit can also analyze the collected information using machine learning. For example, the analysis unit analyzes a user's behavioral patterns using machine learning. Furthermore, the analysis unit can also analyze the collected information using natural language processing. For example, the analysis unit analyzes the user's text data using natural language processing. This allows the collected information to be analyzed accurately.

[0032] The report generation unit can generate the results of the analysis performed by the analysis unit as a report. The report generation unit generates the analysis results as a report in PDF format, for example. For example, the report generation unit generates the analysis results as a report in PDF format. The report generation unit can also generate the analysis results as a report in HTML format. For example, the report generation unit generates the analysis results as a report in HTML format. Furthermore, the report generation unit can include the analysis results in the report as a graph or text. For example, the report generation unit includes the analysis results in the report as a graph or text. This allows the analysis results to be provided as a report.

[0033] The plan proposal unit can propose a plan based on the report generated by the report generation unit. The plan proposal unit can propose a plan based on, for example, the user's career goals. For example, the plan proposal unit can propose a plan based on the user's career goals. The plan proposal unit can also propose a plan based on improvements to the user's lifestyle. For example, the plan proposal unit can propose a plan based on improvements to the user's lifestyle. Furthermore, the plan proposal unit can also propose a plan based on the user's financial plan. For example, the plan proposal unit can propose a plan based on the user's financial plan. This makes it possible to propose an optimal plan for the user.

[0034] The execution support unit can provide support for executing the plan proposed by the plan proposal unit. The execution support unit, for example, manages the user's tasks and supports the execution of the plan. For example, the execution support unit manages the user's tasks and supports the execution of the plan. The execution support unit can also perform project management and support the execution of the plan. For example, the execution support unit can perform project management and support the execution of the plan. Furthermore, the execution support unit can also perform resource management and support the execution of the plan. For example, the execution support unit can perform resource management and support the execution of the plan. This makes it possible to support the execution of the proposed plan.

[0035] The progress management unit can manage the progress of the plan executed by the execution support unit. The progress management unit, for example, manages the progress rate of the plan and provides feedback to the user. For example, the progress management unit manages the progress rate of the plan and provides feedback to the user. The progress management unit can also set milestones and manage the progress of the plan. For example, the progress management unit sets milestones and manages the progress of the plan. Furthermore, the progress management unit can also manage the completion status of tasks and manage the progress of the plan. For example, the progress management unit manages the completion status of tasks and manages the progress of the plan. In this way, the progress of the plan can be managed.

[0036] The hearing unit can analyze the user's past hearing history and select the optimal hearing method. For example, the hearing unit preferentially selects a hearing method that the user has preferred in the past. For example, the hearing unit preferentially selects a hearing method that the user has preferred in the past. The hearing unit can also eliminate a hearing method that the user has avoided in the past. For example, the hearing unit eliminates a hearing method that the user has avoided in the past. Furthermore, the hearing unit can also suggest the most effective hearing method from the user's past hearing history. For example, the hearing unit suggests the most effective hearing method from the user's past hearing history. This makes it possible to select the optimal hearing method based on the past hearing history.

[0037] The hearing unit can customize the content of questions based on the user's current living situation and areas of interest. For example, if the user is interested in their current job, the hearing unit prioritizes questions related to work. For example, if the user is interested in their current job, the hearing unit prioritizes questions related to work. Furthermore, if the user is interested in health, the hearing unit can prioritize questions related to health. For example, if the user is interested in health, the hearing unit prioritizes questions related to health. Furthermore, if the user is interested in hobbies, the hearing unit can prioritize questions related to hobbies. For example, if the user is interested in hobbies, the hearing unit prioritizes questions related to hobbies. This makes it possible to provide questions according to the user's areas of interest.

[0038] The hearing unit can select the optimal hearing means according to the user's input method. For example, if the user prefers voice input, the hearing unit prioritizes voice hearing. For example, if the user prefers voice input, the hearing unit prioritizes voice hearing. Furthermore, if the user prefers text input, the hearing unit can also prioritize text hearing. For example, if the user prefers text input, the hearing unit prioritizes text hearing. Furthermore, if the user prefers image input, the hearing unit can also prioritize image-based hearing. For example, if the user prefers image input, the hearing unit prioritizes image-based hearing. This makes it possible to provide the optimal hearing means according to the user's input method.

[0039] The hearing unit can prioritize relevant questions taking into account the user's geographical location information. For example, if the user lives in a specific area, the hearing unit prioritizes questions related to that area. For example, if the user lives in a specific area, the hearing unit prioritizes questions related to that area. Furthermore, if the user is traveling, the hearing unit can prioritize questions related to the travel destination. For example, if the user is traveling, the hearing unit prioritizes questions related to the travel destination. Furthermore, if the user is considering moving, the hearing unit can prioritize questions related to the destination. For example, if the user is considering moving, the hearing unit prioritizes questions related to the destination. This makes it possible to provide questions based on the user's geographical location information.

[0040] The hearing unit can analyze the user's social media activity and ask related questions. The hearing unit, for example, asks questions based on information shared by the user on social media. For example, the hearing unit asks questions based on information shared by the user on social media. The hearing unit can also analyze the user's social media activity and ask related questions. For example, the hearing unit can analyze the user's social media activity and ask related questions. Furthermore, the hearing unit can also ask questions with reference to the activity of the user's friends on social media. For example, the hearing unit asks questions with reference to the activity of the user's friends on social media. This makes it possible to provide questions based on the user's social media activity.

[0041] The hearing unit can customize the hearing method by reflecting the user's past feedback. For example, the hearing unit preferentially selects a hearing method that the user has previously preferred. For example, the hearing unit preferentially selects a hearing method that the user has previously preferred. The hearing unit can also eliminate a hearing method that the user has previously avoided. For example, the hearing unit eliminates a hearing method that the user has previously avoided. Furthermore, the hearing unit can also suggest the most effective hearing method based on the user's past feedback. For example, the hearing unit suggests the most effective hearing method based on the user's past feedback. This makes it possible to provide the optimal hearing method based on the user's past feedback.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. For example, the analysis unit performs a concise analysis on information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. For example, the analysis unit performs an analysis with an appropriate level of detail on information of medium importance. This makes it possible to provide an optimal analysis according to the importance of the information.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a career analysis algorithm to information about career. For example, the analysis unit applies a career analysis algorithm to information about career. The analysis unit can also apply a health analysis algorithm to information about health. For example, the analysis unit applies a health analysis algorithm to information about health. The analysis unit can also apply a lifestyle analysis algorithm to information about lifestyle. For example, the analysis unit applies a lifestyle analysis algorithm to information about lifestyle. This makes it possible to provide an optimal analysis depending on the category of information.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit analyzes the user's past analysis results to improve the accuracy of the analysis. This makes it possible to provide an optimal analysis based on the past analysis results.

[0045] The analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit gives priority to analyzing the latest information. For example, the analysis unit gives priority to analyzing the latest information. The analysis unit can also postpone information that was submitted earlier. For example, the analysis unit postpones information that was submitted earlier. Furthermore, the analysis unit can also moderately analyze information that was submitted at a medium time. For example, the analysis unit moderately analyzes information that was submitted at a medium time. This makes it possible to provide an optimal analysis according to the time of submission of the information.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone information with low relevance. For example, the analysis unit postpones information with low relevance. Furthermore, the analysis unit can also moderately analyze information with medium relevance. For example, the analysis unit moderately analyzes information with medium relevance. This makes it possible to provide an optimal analysis order according to the relevance of the information.

[0047] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. The analysis unit can also avoid technical terms if the user does not have technical expertise. For example, the analysis unit avoids technical terms if the user does not have technical expertise. Furthermore, the analysis unit can also use technical terms appropriately according to the user's level of expertise. For example, the analysis unit uses technical terms appropriately according to the user's level of expertise. This makes it possible to provide an optimal analysis according to the user's level of expertise.

[0048] The report generation unit can adjust the level of detail of the report based on the importance of the analysis result. For example, the report generation unit generates a detailed report for an analysis result with high importance. For example, the report generation unit generates a detailed report for an analysis result with high importance. The report generation unit can also generate a concise report for an analysis result with low importance. For example, the report generation unit generates a concise report for an analysis result with low importance. Furthermore, the report generation unit can also generate a report with a moderate level of detail for an analysis result with medium importance. For example, the report generation unit generates a report with a moderate level of detail for an analysis result with medium importance. This makes it possible to provide an optimal report according to the importance of the analysis result.

[0049] The report generation unit can apply different report generation algorithms depending on the category of the analysis results. For example, the report generation unit applies a career report generation algorithm to analysis results related to career. For example, the report generation unit applies a career report generation algorithm to analysis results related to career. The report generation unit can also apply a health report generation algorithm to analysis results related to health. For example, the report generation unit applies a health report generation algorithm to analysis results related to health. The report generation unit can also apply a lifestyle report generation algorithm to analysis results related to lifestyle. For example, the report generation unit applies a lifestyle report generation algorithm to analysis results related to lifestyle. This makes it possible to provide an optimal report depending on the category of the analysis results.

[0050] The report generation unit can improve the accuracy of the report by referring to the user's past report results. The report generation unit, for example, corrects the current report based on the user's past report results. For example, the report generation unit corrects the current report based on the user's past report results. The report generation unit can also adjust the report generation algorithm by referring to the user's past report results. For example, the report generation unit adjusts the report generation algorithm by referring to the user's past report results. Furthermore, the report generation unit can also analyze the user's past report results to improve the accuracy of the report. For example, the report generation unit analyzes the user's past report results to improve the accuracy of the report. This makes it possible to provide an optimal report based on the past report results.

[0051] The report generation unit can determine the priority of reports based on the submission timing of the analysis results. The report generation unit, for example, preferentially reflects the latest analysis results in the report. For example, the report generation unit preferentially reflects the latest analysis results in the report. The report generation unit can also postpone analysis results that were submitted earlier. For example, the report generation unit postpones analysis results that were submitted earlier. The report generation unit can also appropriately reflect analysis results that were submitted at a medium time in the report. For example, the report generation unit appropriately reflects analysis results that were submitted at a medium time in the report. This makes it possible to provide an optimal report according to the submission timing of the analysis results.

[0052] The report generation unit can adjust the order of reports based on the relevance of the analysis results. For example, the report generation unit preferentially reflects analysis results with high relevance in the report. For example, the report generation unit preferentially reflects analysis results with high relevance in the report. The report generation unit can also postpone analysis results with low relevance. For example, the report generation unit postpones analysis results with low relevance. Furthermore, the report generation unit can also moderately reflect analysis results with medium relevance in the report. For example, the report generation unit moderately reflects analysis results with medium relevance in the report. This makes it possible to provide an optimal report order according to the relevance of the analysis results.

[0053] The report generation unit can adjust the use of technical terms in the report according to the user's level of expertise. For example, the report generation unit uses a lot of technical terms when the user has technical expertise. For example, the report generation unit uses a lot of technical terms when the user has technical expertise. The report generation unit can also avoid technical terms when the user does not have technical expertise. For example, the report generation unit avoids technical terms when the user does not have technical expertise. Furthermore, the report generation unit can also use technical terms appropriately according to the user's level of expertise. For example, the report generation unit uses technical terms appropriately according to the user's level of expertise. This makes it possible to provide an optimal report according to the user's level of expertise.

[0054] The plan proposal unit can adjust the level of detail of the plan based on the importance of the report. For example, the plan proposal unit proposes a detailed plan for a report of high importance. For example, the plan proposal unit proposes a detailed plan for a report of high importance. The plan proposal unit can also propose a concise plan for a report of low importance. For example, the plan proposal unit proposes a concise plan for a report of low importance. Furthermore, the plan proposal unit can also propose a plan with a moderate level of detail for a report of medium importance. For example, the plan proposal unit proposes a plan with a moderate level of detail for a report of medium importance. This makes it possible to provide an optimal plan according to the importance of the report.

[0055] The plan proposal unit can apply different plan proposal algorithms depending on the category of the report. For example, the plan proposal unit applies a career plan proposal algorithm to a report related to career. For example, the plan proposal unit applies a career plan proposal algorithm to a report related to career. The plan proposal unit can also apply a health plan proposal algorithm to a report related to health. For example, the plan proposal unit applies a health plan proposal algorithm to a report related to health. Furthermore, the plan proposal unit can also apply a lifestyle plan proposal algorithm to a report related to lifestyle. For example, the plan proposal unit applies a lifestyle plan proposal algorithm to a report related to lifestyle. This makes it possible to provide an optimal plan according to the category of the report.

[0056] The plan proposal unit can improve the accuracy of the plan by referring to the user's past plan results. The plan proposal unit, for example, corrects the current plan based on the user's past plan results. For example, the plan proposal unit corrects the current plan based on the user's past plan results. The plan proposal unit can also adjust the plan proposal algorithm by referring to the user's past plan results. For example, the plan proposal unit adjusts the plan proposal algorithm by referring to the user's past plan results. Furthermore, the plan proposal unit can also analyze the user's past plan results and improve the accuracy of the plan. For example, the plan proposal unit analyzes the user's past plan results and improves the accuracy of the plan. This makes it possible to provide an optimal plan based on the past plan results.

[0057] The plan proposal unit can determine the priority of the plan based on the submission time of the report. For example, the plan proposal unit preferentially reflects the latest report in the plan. For example, the plan proposal unit preferentially reflects the latest report in the plan. The plan proposal unit can also postpone reports that have been submitted earlier. For example, the plan proposal unit postpones reports that have been submitted earlier. Furthermore, the plan proposal unit can also appropriately reflect reports that have been submitted at an intermediate time in the plan. For example, the plan proposal unit appropriately reflects reports that have been submitted at an intermediate time in the plan. This makes it possible to provide an optimal plan according to the submission time of the report.

[0058] The plan proposal unit can adjust the order of the plan based on the relevance of the reports. For example, the plan proposal unit preferentially reflects reports with high relevance in the plan. For example, the plan proposal unit preferentially reflects reports with high relevance in the plan. The plan proposal unit can also postpone reports with low relevance. For example, the plan proposal unit postpones reports with low relevance. Furthermore, the plan proposal unit can also moderately reflect reports with medium relevance in the plan. For example, the plan proposal unit moderately reflects reports with medium relevance in the plan. This makes it possible to provide an optimal plan order according to the relevance of the reports.

[0059] The plan proposal unit can adjust the use of technical terms in the plan depending on the user's level of expertise. For example, if the user has technical knowledge, the plan proposal unit uses a lot of technical terms. For example, if the user has technical knowledge, the plan proposal unit uses a lot of technical terms. Furthermore, the plan proposal unit can avoid technical terms if the user does not have technical knowledge. For example, if the user does not have technical knowledge, the plan proposal unit avoids technical terms. Furthermore, the plan proposal unit can also use technical terms appropriately depending on the user's level of expertise. For example, the plan proposal unit uses technical terms appropriately depending on the user's level of expertise. This makes it possible to provide an optimal plan depending on the user's level of expertise.

[0060] The execution support unit can select the optimal support method by analyzing the user's past execution history. For example, the execution support unit preferentially selects an execution support method that the user has preferred in the past. For example, the execution support unit preferentially selects an execution support method that the user has preferred in the past. The execution support unit can also eliminate an execution support method that the user has avoided in the past. For example, the execution support unit eliminates an execution support method that the user has avoided in the past. Furthermore, the execution support unit can also suggest the most effective execution support method from the user's past execution history. For example, the execution support unit suggests the most effective execution support method from the user's past execution history. This makes it possible to provide the optimal support method based on the past execution history.

[0061] The execution support unit can customize the assistance means based on the user's current living situation. For example, when the user is at work, the execution support unit provides assistance means that can be executed in a short time. For example, when the user is at work, the execution support unit provides assistance means that can be executed in a short time. Furthermore, the execution support unit can also provide detailed execution support when the user is on vacation. For example, when the user is on vacation, the execution support unit provides detailed execution support. Furthermore, when the user is engaged in an activity at home, the execution support unit can also provide assistance means that can be executed at home. For example, when the user is engaged in an activity at home, the execution support unit provides assistance means that can be executed at home. This makes it possible to provide optimal assistance means according to the user's living situation.

[0062] The execution support unit can improve the support method by reflecting the user's feedback. For example, the execution support unit preferentially selects a support method that the user has previously preferred. For example, the execution support unit preferentially selects a support method that the user has previously preferred. The execution support unit can also eliminate a support method that the user has previously avoided. For example, the execution support unit eliminates a support method that the user has previously avoided. Furthermore, the execution support unit can also suggest the most effective support method based on the user's past feedback. For example, the execution support unit suggests the most effective support method based on the user's past feedback. This makes it possible to provide the optimal support method based on the user's feedback.

[0063] The execution support unit can select the optimal support method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the execution support unit prioritizes support methods related to that area. For example, if the user lives in a specific area, the execution support unit prioritizes support methods related to that area. Furthermore, if the user is traveling, the execution support unit can also prioritize support methods related to the travel destination. For example, if the user is traveling, the execution support unit prioritizes support methods related to the travel destination. Furthermore, if the user is considering moving, the execution support unit can also prioritize support methods related to the moving destination. For example, if the user is considering moving, the execution support unit prioritizes support methods related to the moving destination. This makes it possible to provide the optimal support method based on the user's geographical location information.

[0064] The execution support unit can analyze the user's social media activities and suggest assistance means. The execution support unit can, for example, suggest assistance means based on information shared by the user on social media. For example, the execution support unit can suggest assistance means based on information shared by the user on social media. The execution support unit can also analyze the user's social media activities and suggest related assistance means. For example, the execution support unit can analyze the user's social media activities and suggest related assistance means. Furthermore, the execution support unit can also suggest assistance means by referring to the activities of the user's friends on social media. For example, the execution support unit can suggest assistance means by referring to the activities of the user's friends on social media. This makes it possible to provide optimal assistance means based on the user's social media activities.

[0065] The execution support unit can customize the support method by reflecting the user's past feedback. For example, the execution support unit preferentially selects support methods that the user has preferred in the past. For example, the execution support unit preferentially selects support methods that the user has preferred in the past. The execution support unit can also eliminate support methods that the user has avoided in the past. For example, the execution support unit eliminates support methods that the user has avoided in the past. Furthermore, the execution support unit can also suggest the most effective support method based on the user's past feedback. For example, the execution support unit suggests the most effective support method based on the user's past feedback. This makes it possible to provide the optimal support method based on the user's past feedback.

[0066] The progress management unit can analyze the user's past progress history and select the optimal management method. For example, the progress management unit preferentially selects a progress management method that the user has previously preferred. For example, the progress management unit preferentially selects a progress management method that the user has previously preferred. The progress management unit can also eliminate progress management methods that the user has previously avoided. For example, the progress management unit eliminates progress management methods that the user has previously avoided. Furthermore, the progress management unit can also suggest the most effective progress management method based on the user's past progress history. For example, the progress management unit suggests the most effective progress management method based on the user's past progress history. This makes it possible to provide the optimal management method based on the past progress history.

[0067] The progress management unit can customize the management means based on the user's current living situation. For example, when the user is at work, the progress management unit provides a management means that allows the user to check progress in a short time. For example, when the user is at work, the progress management unit provides a management means that allows the user to check progress in a short time. The progress management unit can also provide detailed progress management when the user is on vacation. For example, when the user is on vacation, the progress management unit provides detailed progress management. Furthermore, when the user is engaged in domestic activities, the progress management unit can also provide a management means that allows the user to check progress at home. For example, when the user is engaged in domestic activities, the progress management unit provides a management means that allows the user to check progress at home. This makes it possible to provide an optimal management means according to the user's living situation.

[0068] The progress management unit can improve the management method by reflecting user feedback. For example, the progress management unit preferentially selects management methods that the user has previously preferred. For example, the progress management unit preferentially selects management methods that the user has previously preferred. The progress management unit can also eliminate management methods that the user has previously avoided. For example, the progress management unit eliminates management methods that the user has previously avoided. Furthermore, the progress management unit can also suggest the most effective management method based on the user's past feedback. For example, the progress management unit suggests the most effective management method based on the user's past feedback. This makes it possible to provide an optimal management method based on the user's feedback.

[0069] The progress management unit can select the optimal management method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the progress management unit prioritizes a management method related to that area. For example, if the user lives in a specific area, the progress management unit prioritizes a management method related to that area. Furthermore, if the user is traveling, the progress management unit can also prioritize a management method related to the travel destination. For example, if the user is traveling, the progress management unit prioritizes a management method related to the travel destination. Furthermore, if the user is considering moving, the progress management unit can also prioritize a management method related to the new destination. For example, if the user is considering moving, the progress management unit prioritizes a management method related to the new destination. This makes it possible to provide the optimal management method based on the user's geographical location information.

[0070] The progress management unit can analyze the user's social media activities and propose management measures. The progress management unit can, for example, propose management measures based on information shared by the user on social media. For example, the progress management unit can propose management measures based on information shared by the user on social media. The progress management unit can also analyze the user's social media activities and propose related management measures. For example, the progress management unit can analyze the user's social media activities and propose related management measures. The progress management unit can also propose management measures by taking into account the activities of the user's friends on social media. For example, the progress management unit can propose management measures by taking into account the activities of the user's friends on social media. This makes it possible to provide optimal management measures based on the user's social media activities.

[0071] The progress management unit can customize the management method by reflecting the user's past feedback. For example, the progress management unit preferentially selects management methods that the user has previously preferred. For example, the progress management unit preferentially selects management methods that the user has previously preferred. The progress management unit can also eliminate management methods that the user has previously avoided. For example, the progress management unit eliminates management methods that the user has previously avoided. Furthermore, the progress management unit can also suggest the most effective management method based on the user's past feedback. For example, the progress management unit suggests the most effective management method based on the user's past feedback. This makes it possible to provide an optimal management method based on the user's past feedback.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The life navigation system may further include a health management unit that collects the user's health data and supports future planning based on the user's health status. For example, the health management unit may collect the user's fitness data and set health goals. The health management unit may also analyze the user's dietary data and suggest ways to improve nutritional balance. Furthermore, the health management unit may monitor the user's sleep data and provide advice on improving sleep quality. This allows for future planning that takes the user's health status into consideration.

[0074] The execution support unit can select the optimal support method by analyzing the user's past execution history. For example, it can prioritize the selection of execution support methods that the user has previously preferred. It can also eliminate execution support methods that the user has previously avoided. Furthermore, it can suggest the most effective execution support method based on the user's past execution history. This makes it possible to provide the optimal support method based on the user's past execution history.

[0075] The progress management unit can customize the management means based on the user's current living situation. For example, if the user is at work, a management means is provided that allows the user to check progress in a short time. If the user is on vacation, detailed progress management can be provided. Furthermore, if the user is engaged in activities at home, a management means is provided that allows the user to check progress at home. This makes it possible to provide the optimal management means according to the user's living situation.

[0076] The progress management unit can improve the management method by reflecting the user's feedback. For example, it can prioritize the selection of management methods that the user has previously preferred. It can also eliminate management methods that the user has previously avoided. Furthermore, it can suggest the most effective management method based on the user's past feedback. This makes it possible to provide the optimal management method based on the user's feedback.

[0077] The progress management unit can select the optimal management method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, a management method related to that area can be prioritized. Also, if the user is traveling, a management method related to the travel destination can be prioritized. Furthermore, if the user is considering moving, a management method related to the new destination can be prioritized. This makes it possible to provide the optimal management method based on the user's geographical location information.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The hearing unit collects user input information. User input information includes text, voice, images, etc. The hearing unit collects user information using questionnaires and sensors, and can also collect log data. For example, the hearing unit collects user input information and inputs it into the generation AI. Step 2: The analysis unit analyzes the information collected by the hearing unit. The analysis is performed using methods such as statistical analysis, machine learning, and natural language processing. For example, the analysis unit performs statistical analysis on the collected information to understand user trends. It can also analyze user behavior patterns using machine learning and analyze user text data using natural language processing. Step 3: The report generation unit generates a report based on the results of the analysis performed by the analysis unit. The report may be generated in PDF format, HTML format, graph format, text format, or other format. For example, the report generation unit may generate a report in PDF format or HTML format containing the analysis results, and may also include graphs or text in the report. Step 4: The plan suggestion unit proposes a plan based on the report generated by the report generation unit. The plan is proposed based on the content of a short-term plan, a long-term plan, resource allocation, etc. For example, the plan suggestion unit proposes a plan based on the user's career goals, lifestyle improvement, and financial plan. Step 5: The execution support unit provides support for executing the plan proposed by the plan proposal unit. Execution support is performed using methods such as task management, project management, and resource management. For example, the execution support unit manages the user's tasks, performs project management, and resource management, and supports the execution of the plan. Step 6: The progress management unit manages the progress of the plan executed by the execution support unit. Progress management is performed using methods such as progress rate, milestones, and task completion status. For example, the progress management unit manages the progress rate of the plan and provides feedback to the user. Milestones can also be set and task completion status can be managed.

[0080] (Example 2) A life navigation system according to an embodiment of the present invention utilizes a generative AI to support a user's future planning and assist in its realization. The life navigation system collects information input by the user, which the generative AI analyzes, generates a report, proposes a plan, supports its implementation, and manages progress. For example, the life navigation system listens to the user's envisioned future and current situation, and the generative AI analyzes and provides a report. Next, the generative AI proposes a specific plan for the user to become their ideal self. Furthermore, the life navigation system provides support for executing the proposed plan and manages progress. This allows the life navigation system to efficiently support the user's future planning and its realization. For example, the user can have their own personal life navigator, allowing them to efficiently plan for their future and its realization.

[0081] The life navigation system according to the embodiment includes a hearing unit, an analysis unit, a report generation unit, a plan proposal unit, an execution support unit, and a progress management unit. The hearing unit collects user input information. The user input information includes, but is not limited to, text, audio, and images. The hearing unit collects user information using, for example, a questionnaire. The hearing unit can also collect user biometric data using a sensor. The hearing unit can also collect log data. For example, the hearing unit collects user input information and inputs it to the generation AI. The analysis unit analyzes the information collected by the hearing unit. The analysis can be performed using, for example, statistical analysis, machine learning, natural language processing, or other methods, but is not limited to these examples. For example, the analysis unit statistically analyzes the collected information to understand user trends. The analysis unit can also analyze user behavior patterns using machine learning. The analysis unit can also analyze user text data using natural language processing. The report generation unit generates a report based on the results of the analysis by the analysis unit. The report may be generated in, for example, a PDF format, an HTML format, a graph, or a text format, but is not limited to these examples. For example, the report generation unit generates a report of the analysis results in PDF format. The report generation unit can also generate a report of the analysis results in HTML format. The report generation unit can also include the analysis results in the report as a graph or text. The plan proposal unit proposes a plan based on the report generated by the report generation unit. The plan may be proposed in the form of, for example, a short-term plan, a long-term plan, or resource allocation, but is not limited to these examples. For example, the plan proposal unit proposes a plan based on the user's career goals. The plan proposal unit can also propose a plan based on improvements to the user's lifestyle. The plan proposal unit can also propose a plan based on the user's financial plan. The execution support unit provides support for executing the plan proposed by the plan proposal unit. The execution support may be performed using, for example, task management, project management, resource management, or other methods, but is not limited to these examples.For example, the execution support unit manages the user's tasks and supports the execution of the plan. The execution support unit can also perform project management and support the execution of the plan. Furthermore, the execution support unit can also perform resource management and support the execution of the plan. The progress management unit manages the progress of the plan executed by the execution support unit. Progress management is performed, for example, using methods such as progress rate, milestones, and task completion status, but is not limited to these examples. For example, the progress management unit manages the progress rate of the plan and provides feedback to the user. The progress management unit can also set milestones and manage the progress of the plan. Furthermore, the progress management unit can manage the completion status of tasks and manage the progress of the plan. As a result, the life navigation system according to the embodiment can efficiently support the user's future planning and its realization.

[0082] The hearing unit can hear about the user's envisioned future and current situation. The hearing unit, for example, hears about the user's envisioned future. For example, the hearing unit can hear about the user's career goals. The hearing unit can also hear about the user's goals for improving their lifestyle. Furthermore, the hearing unit can also hear about the user's financial plan. The hearing unit, for example, hears about the user's current situation. For example, the hearing unit can hear about the user's current occupation. The hearing unit can also hear about the user's income. Furthermore, the hearing unit can hear about the user's living situation. This makes it possible to accurately grasp the user's envisioned future and current situation.

[0083] The analysis unit can analyze the information collected by the hearing unit. The analysis unit, for example, statistically analyzes the collected information. For example, the analysis unit statistically analyzes information input by a user to understand the user's tendencies. The analysis unit can also analyze the collected information using machine learning. For example, the analysis unit analyzes a user's behavioral patterns using machine learning. Furthermore, the analysis unit can also analyze the collected information using natural language processing. For example, the analysis unit analyzes the user's text data using natural language processing. This allows the collected information to be analyzed accurately.

[0084] The report generation unit can generate the results of the analysis performed by the analysis unit as a report. The report generation unit generates the analysis results as a report in PDF format, for example. For example, the report generation unit generates the analysis results as a report in PDF format. The report generation unit can also generate the analysis results as a report in HTML format. For example, the report generation unit generates the analysis results as a report in HTML format. Furthermore, the report generation unit can include the analysis results in the report as a graph or text. For example, the report generation unit includes the analysis results in the report as a graph or text. This allows the analysis results to be provided as a report.

[0085] The plan proposal unit can propose a plan based on the report generated by the report generation unit. The plan proposal unit can propose a plan based on, for example, the user's career goals. For example, the plan proposal unit can propose a plan based on the user's career goals. The plan proposal unit can also propose a plan based on improvements to the user's lifestyle. For example, the plan proposal unit can propose a plan based on improvements to the user's lifestyle. Furthermore, the plan proposal unit can also propose a plan based on the user's financial plan. For example, the plan proposal unit can propose a plan based on the user's financial plan. This makes it possible to propose an optimal plan for the user.

[0086] The execution support unit can provide support for executing the plan proposed by the plan proposal unit. The execution support unit, for example, manages the user's tasks and supports the execution of the plan. For example, the execution support unit manages the user's tasks and supports the execution of the plan. The execution support unit can also perform project management and support the execution of the plan. For example, the execution support unit can perform project management and support the execution of the plan. Furthermore, the execution support unit can also perform resource management and support the execution of the plan. For example, the execution support unit can perform resource management and support the execution of the plan. This makes it possible to support the execution of the proposed plan.

[0087] The progress management unit can manage the progress of the plan executed by the execution support unit. The progress management unit, for example, manages the progress rate of the plan and provides feedback to the user. For example, the progress management unit manages the progress rate of the plan and provides feedback to the user. The progress management unit can also set milestones and manage the progress of the plan. For example, the progress management unit sets milestones and manages the progress of the plan. Furthermore, the progress management unit can also manage the completion status of tasks and manage the progress of the plan. For example, the progress management unit manages the completion status of tasks and manages the progress of the plan. In this way, the progress of the plan can be managed.

[0088] The hearing unit can estimate the user's emotions and adjust the timing of the hearing based on the estimated user's emotions. For example, if the user is feeling stressed, the hearing unit performs the hearing at a time when the user can relax. For example, if the user is feeling stressed, the hearing unit performs the hearing at a time when the user can relax. Furthermore, if the user is relaxed, the hearing unit can perform a detailed hearing. For example, if the user is relaxed, the hearing unit performs a detailed hearing. Furthermore, if the user is in a hurry, the hearing unit can perform a brief hearing that focuses on the main points. For example, if the user is in a hurry, the hearing unit performs a brief hearing that focuses on the main points. This makes it possible to perform the hearing at an optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, 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.

[0089] The hearing unit can analyze the user's past hearing history and select the optimal hearing method. For example, the hearing unit preferentially selects a hearing method that the user has preferred in the past. For example, the hearing unit preferentially selects a hearing method that the user has preferred in the past. The hearing unit can also eliminate a hearing method that the user has avoided in the past. For example, the hearing unit eliminates a hearing method that the user has avoided in the past. Furthermore, the hearing unit can also suggest the most effective hearing method from the user's past hearing history. For example, the hearing unit suggests the most effective hearing method from the user's past hearing history. This makes it possible to select the optimal hearing method based on the past hearing history.

[0090] The hearing unit can customize the content of questions based on the user's current living situation and areas of interest. For example, if the user is interested in their current job, the hearing unit prioritizes questions related to work. For example, if the user is interested in their current job, the hearing unit prioritizes questions related to work. Furthermore, if the user is interested in health, the hearing unit can prioritize questions related to health. For example, if the user is interested in health, the hearing unit prioritizes questions related to health. Furthermore, if the user is interested in hobbies, the hearing unit can prioritize questions related to hobbies. For example, if the user is interested in hobbies, the hearing unit prioritizes questions related to hobbies. This makes it possible to provide questions according to the user's areas of interest.

[0091] The hearing unit can select the optimal hearing means according to the user's input method. For example, if the user prefers voice input, the hearing unit prioritizes voice hearing. For example, if the user prefers voice input, the hearing unit prioritizes voice hearing. Furthermore, if the user prefers text input, the hearing unit can also prioritize text hearing. For example, if the user prefers text input, the hearing unit prioritizes text hearing. Furthermore, if the user prefers image input, the hearing unit can also prioritize image-based hearing. For example, if the user prefers image input, the hearing unit prioritizes image-based hearing. This makes it possible to provide the optimal hearing means according to the user's input method.

[0092] The hearing unit can estimate the user's emotions and determine the priority of questions to be asked based on the estimated user emotions. For example, if the user is feeling stressed, the hearing unit prioritizes questions that will help the user relax. For example, if the user is feeling stressed, the hearing unit prioritizes questions that will help the user relax. Furthermore, if the user is relaxed, the hearing unit can also prioritize detailed questions. For example, if the user is relaxed, the hearing unit prioritizes detailed questions. Furthermore, if the user is in a hurry, the hearing unit can also prioritize questions that get to the point quickly. For example, if the user is in a hurry, the hearing unit prioritizes questions that get to the point quickly. This makes it possible to determine the optimal priority of questions 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.

[0093] The hearing unit can prioritize relevant questions taking into account the user's geographical location information. For example, if the user lives in a specific area, the hearing unit prioritizes questions related to that area. For example, if the user lives in a specific area, the hearing unit prioritizes questions related to that area. Furthermore, if the user is traveling, the hearing unit can prioritize questions related to the travel destination. For example, if the user is traveling, the hearing unit prioritizes questions related to the travel destination. Furthermore, if the user is considering moving, the hearing unit can prioritize questions related to the destination. For example, if the user is considering moving, the hearing unit prioritizes questions related to the destination. This makes it possible to provide questions based on the user's geographical location information.

[0094] The hearing unit can analyze the user's social media activity and ask related questions. The hearing unit, for example, asks questions based on information shared by the user on social media. For example, the hearing unit asks questions based on information shared by the user on social media. The hearing unit can also analyze the user's social media activity and ask related questions. For example, the hearing unit can analyze the user's social media activity and ask related questions. Furthermore, the hearing unit can also ask questions with reference to the activity of the user's friends on social media. For example, the hearing unit asks questions with reference to the activity of the user's friends on social media. This makes it possible to provide questions based on the user's social media activity.

[0095] The hearing unit can customize the hearing method by reflecting the user's past feedback. For example, the hearing unit preferentially selects a hearing method that the user has previously preferred. For example, the hearing unit preferentially selects a hearing method that the user has previously preferred. The hearing unit can also eliminate a hearing method that the user has previously avoided. For example, the hearing unit eliminates a hearing method that the user has previously avoided. Furthermore, the hearing unit can also suggest the most effective hearing method based on the user's past feedback. For example, the hearing unit suggests the most effective hearing method based on the user's past feedback. This makes it possible to provide the optimal hearing method based on the user's past feedback.

[0096] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides detailed analysis results. For example, when the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, when the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, when the user is excited, the analysis unit can provide analysis results that add visually stimulating effects. For example, when the user is excited, the analysis unit provides analysis results that add visually stimulating effects. This makes it possible to provide an optimal analysis presentation according to the user's emotions. Emotion estimation is achieved 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.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. For example, the analysis unit performs a concise analysis on information of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. For example, the analysis unit performs an analysis with an appropriate level of detail on information of medium importance. This makes it possible to provide an optimal analysis according to the importance of the information.

[0098] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a career analysis algorithm to information about career. For example, the analysis unit applies a career analysis algorithm to information about career. The analysis unit can also apply a health analysis algorithm to information about health. For example, the analysis unit applies a health analysis algorithm to information about health. The analysis unit can also apply a lifestyle analysis algorithm to information about lifestyle. For example, the analysis unit applies a lifestyle analysis algorithm to information about lifestyle. This makes it possible to provide an optimal analysis depending on the category of information.

[0099] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit analyzes the user's past analysis results to improve the accuracy of the analysis. This makes it possible to provide an optimal analysis based on the past analysis results.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, if the user is relaxed, the analysis unit performs a detailed analysis. Furthermore, if the user is excited, the analysis unit can also perform an analysis that adds visually stimulating effects. For example, if the user is excited, the analysis unit performs an analysis that adds visually stimulating effects. This makes it possible to provide an optimal length of analysis according to the user's emotions. Emotion estimation is achieved 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.

[0101] The analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit gives priority to analyzing the latest information. For example, the analysis unit gives priority to analyzing the latest information. The analysis unit can also postpone information that was submitted earlier. For example, the analysis unit postpones information that was submitted earlier. Furthermore, the analysis unit can also moderately analyze information that was submitted at a medium time. For example, the analysis unit moderately analyzes information that was submitted at a medium time. This makes it possible to provide an optimal analysis according to the time of submission of the information.

[0102] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone information with low relevance. For example, the analysis unit postpones information with low relevance. Furthermore, the analysis unit can also moderately analyze information with medium relevance. For example, the analysis unit moderately analyzes information with medium relevance. This makes it possible to provide an optimal analysis order according to the relevance of the information.

[0103] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. The analysis unit can also avoid technical terms if the user does not have technical expertise. For example, the analysis unit avoids technical terms if the user does not have technical expertise. Furthermore, the analysis unit can also use technical terms appropriately according to the user's level of expertise. For example, the analysis unit uses technical terms appropriately according to the user's level of expertise. This makes it possible to provide an optimal analysis according to the user's level of expertise.

[0104] The report generation unit can estimate the user's emotions and adjust the way the report is presented based on the estimated user's emotions. For example, the report generation unit provides a detailed report when the user is relaxed. For example, the report generation unit provides a detailed report when the user is relaxed. Furthermore, the report generation unit can provide a concise report that focuses on the main points when the user is in a hurry. For example, the report generation unit provides a concise report that focuses on the main points when the user is in a hurry. Furthermore, the report generation unit can provide a report that adds visually stimulating effects when the user is excited. For example, the report generation unit provides a report that adds visually stimulating effects when the user is excited. This makes it possible to provide an optimal report presentation according to the user's emotions. 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.

[0105] The report generation unit can adjust the level of detail of the report based on the importance of the analysis result. For example, the report generation unit generates a detailed report for an analysis result with high importance. For example, the report generation unit generates a detailed report for an analysis result with high importance. The report generation unit can also generate a concise report for an analysis result with low importance. For example, the report generation unit generates a concise report for an analysis result with low importance. Furthermore, the report generation unit can also generate a report with a moderate level of detail for an analysis result with medium importance. For example, the report generation unit generates a report with a moderate level of detail for an analysis result with medium importance. This makes it possible to provide an optimal report according to the importance of the analysis result.

[0106] The report generation unit can apply different report generation algorithms depending on the category of the analysis results. For example, the report generation unit applies a career report generation algorithm to analysis results related to career. For example, the report generation unit applies a career report generation algorithm to analysis results related to career. The report generation unit can also apply a health report generation algorithm to analysis results related to health. For example, the report generation unit applies a health report generation algorithm to analysis results related to health. The report generation unit can also apply a lifestyle report generation algorithm to analysis results related to lifestyle. For example, the report generation unit applies a lifestyle report generation algorithm to analysis results related to lifestyle. This makes it possible to provide an optimal report depending on the category of the analysis results.

[0107] The report generation unit can improve the accuracy of the report by referring to the user's past report results. The report generation unit, for example, corrects the current report based on the user's past report results. For example, the report generation unit corrects the current report based on the user's past report results. The report generation unit can also adjust the report generation algorithm by referring to the user's past report results. For example, the report generation unit adjusts the report generation algorithm by referring to the user's past report results. Furthermore, the report generation unit can also analyze the user's past report results to improve the accuracy of the report. For example, the report generation unit analyzes the user's past report results to improve the accuracy of the report. This makes it possible to provide an optimal report based on the past report results.

[0108] The report generation unit can estimate the user's emotions and adjust the length of the report based on the estimated user's emotions. For example, when the user is in a hurry, the report generation unit generates a short and to-the-point report. For example, when the user is in a hurry, the report generation unit generates a short and to-the-point report. The report generation unit can also generate a detailed report when the user is relaxed. For example, when the user is relaxed, the report generation unit generates a detailed report. Furthermore, when the user is excited, the report generation unit can also generate a report with visually stimulating effects. For example, when the user is excited, the report generation unit generates a report with visually stimulating effects. This makes it possible to provide an optimal report length according to the user's emotions. 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.

[0109] The report generation unit can determine the priority of reports based on the submission timing of the analysis results. The report generation unit, for example, preferentially reflects the latest analysis results in the report. For example, the report generation unit preferentially reflects the latest analysis results in the report. The report generation unit can also postpone analysis results that were submitted earlier. For example, the report generation unit postpones analysis results that were submitted earlier. The report generation unit can also appropriately reflect analysis results that were submitted at a medium time in the report. For example, the report generation unit appropriately reflects analysis results that were submitted at a medium time in the report. This makes it possible to provide an optimal report according to the submission timing of the analysis results.

[0110] The report generation unit can adjust the order of reports based on the relevance of the analysis results. For example, the report generation unit preferentially reflects analysis results with high relevance in the report. For example, the report generation unit preferentially reflects analysis results with high relevance in the report. The report generation unit can also postpone analysis results with low relevance. For example, the report generation unit postpones analysis results with low relevance. Furthermore, the report generation unit can also moderately reflect analysis results with medium relevance in the report. For example, the report generation unit moderately reflects analysis results with medium relevance in the report. This makes it possible to provide an optimal report order according to the relevance of the analysis results.

[0111] The report generation unit can adjust the use of technical terms in the report according to the user's level of expertise. For example, the report generation unit uses a lot of technical terms when the user has technical expertise. For example, the report generation unit uses a lot of technical terms when the user has technical expertise. The report generation unit can also avoid technical terms when the user does not have technical expertise. For example, the report generation unit avoids technical terms when the user does not have technical expertise. Furthermore, the report generation unit can also use technical terms appropriately according to the user's level of expertise. For example, the report generation unit uses technical terms appropriately according to the user's level of expertise. This makes it possible to provide an optimal report according to the user's level of expertise.

[0112] The plan proposal unit can estimate the user's emotions and adjust the way the plan is presented based on the estimated user's emotions. For example, if the user is relaxed, the plan proposal unit provides a detailed plan. For example, if the user is relaxed, the plan proposal unit provides a detailed plan. Furthermore, if the user is in a hurry, the plan proposal unit can provide a concise plan that focuses on the main points. For example, if the user is in a hurry, the plan proposal unit can provide a concise plan that focuses on the main points. Furthermore, if the user is excited, the plan proposal unit can provide a plan that adds visually stimulating effects. For example, if the user is excited, the plan proposal unit provides a plan that adds visually stimulating effects. This makes it possible to provide an optimal plan presentation according to the user's emotions. 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.

[0113] The plan proposal unit can adjust the level of detail of the plan based on the importance of the report. For example, the plan proposal unit proposes a detailed plan for a report of high importance. For example, the plan proposal unit proposes a detailed plan for a report of high importance. The plan proposal unit can also propose a concise plan for a report of low importance. For example, the plan proposal unit proposes a concise plan for a report of low importance. Furthermore, the plan proposal unit can also propose a plan with a moderate level of detail for a report of medium importance. For example, the plan proposal unit proposes a plan with a moderate level of detail for a report of medium importance. This makes it possible to provide an optimal plan according to the importance of the report.

[0114] The plan proposal unit can apply different plan proposal algorithms depending on the category of the report. For example, the plan proposal unit applies a career plan proposal algorithm to a report related to career. For example, the plan proposal unit applies a career plan proposal algorithm to a report related to career. The plan proposal unit can also apply a health plan proposal algorithm to a report related to health. For example, the plan proposal unit applies a health plan proposal algorithm to a report related to health. Furthermore, the plan proposal unit can also apply a lifestyle plan proposal algorithm to a report related to lifestyle. For example, the plan proposal unit applies a lifestyle plan proposal algorithm to a report related to lifestyle. This makes it possible to provide an optimal plan according to the category of the report.

[0115] The plan proposal unit can improve the accuracy of the plan by referring to the user's past plan results. The plan proposal unit, for example, corrects the current plan based on the user's past plan results. For example, the plan proposal unit corrects the current plan based on the user's past plan results. The plan proposal unit can also adjust the plan proposal algorithm by referring to the user's past plan results. For example, the plan proposal unit adjusts the plan proposal algorithm by referring to the user's past plan results. Furthermore, the plan proposal unit can also analyze the user's past plan results and improve the accuracy of the plan. For example, the plan proposal unit analyzes the user's past plan results and improves the accuracy of the plan. This makes it possible to provide an optimal plan based on the past plan results.

[0116] The plan proposal unit can estimate the user's emotions and adjust the length of the plan based on the estimated user's emotions. For example, if the user is in a hurry, the plan proposal unit can propose a short and to-the-point plan. For example, if the user is in a hurry, the plan proposal unit can propose a short and to-the-point plan. The plan proposal unit can also propose a detailed plan if the user is relaxed. For example, if the user is relaxed, the plan proposal unit can propose a detailed plan. Furthermore, if the user is excited, the plan proposal unit can propose a plan with visually stimulating effects. For example, if the user is excited, the plan proposal unit can propose a plan with visually stimulating effects. This makes it possible to provide an optimal plan length according to the user's emotions. 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.

[0117] The plan proposal unit can determine the priority of the plan based on the submission time of the report. For example, the plan proposal unit preferentially reflects the latest report in the plan. For example, the plan proposal unit preferentially reflects the latest report in the plan. The plan proposal unit can also postpone reports that have been submitted earlier. For example, the plan proposal unit postpones reports that have been submitted earlier. Furthermore, the plan proposal unit can also appropriately reflect reports that have been submitted at an intermediate time in the plan. For example, the plan proposal unit appropriately reflects reports that have been submitted at an intermediate time in the plan. This makes it possible to provide an optimal plan according to the submission time of the report.

[0118] The plan proposal unit can adjust the order of the plan based on the relevance of the reports. For example, the plan proposal unit preferentially reflects reports with high relevance in the plan. For example, the plan proposal unit preferentially reflects reports with high relevance in the plan. The plan proposal unit can also postpone reports with low relevance. For example, the plan proposal unit postpones reports with low relevance. Furthermore, the plan proposal unit can also moderately reflect reports with medium relevance in the plan. For example, the plan proposal unit moderately reflects reports with medium relevance in the plan. This makes it possible to provide an optimal plan order according to the relevance of the reports.

[0119] The plan proposal unit can adjust the use of technical terms in the plan depending on the user's level of expertise. For example, if the user has technical knowledge, the plan proposal unit uses a lot of technical terms. For example, if the user has technical knowledge, the plan proposal unit uses a lot of technical terms. Furthermore, the plan proposal unit can avoid technical terms if the user does not have technical knowledge. For example, if the user does not have technical knowledge, the plan proposal unit avoids technical terms. Furthermore, the plan proposal unit can also use technical terms appropriately depending on the user's level of expertise. For example, the plan proposal unit uses technical terms appropriately depending on the user's level of expertise. This makes it possible to provide an optimal plan depending on the user's level of expertise.

[0120] The execution support unit can estimate the user's emotions and adjust the execution support method based on the estimated user's emotions. For example, when the user is relaxed, the execution support unit provides detailed execution support. For example, when the user is relaxed, the execution support unit provides detailed execution support. Furthermore, when the user is in a hurry, the execution support unit can provide concise execution support that focuses on the main points. For example, when the user is in a hurry, the execution support unit provides concise execution support that focuses on the main points. Furthermore, when the user is excited, the execution support unit can provide execution support that adds visually stimulating effects. For example, when the user is excited, the execution support unit provides execution support that adds visually stimulating effects. This makes it possible to provide optimal execution support 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.

[0121] The execution support unit can select the optimal support method by analyzing the user's past execution history. For example, the execution support unit preferentially selects an execution support method that the user has preferred in the past. For example, the execution support unit preferentially selects an execution support method that the user has preferred in the past. The execution support unit can also eliminate an execution support method that the user has avoided in the past. For example, the execution support unit eliminates an execution support method that the user has avoided in the past. Furthermore, the execution support unit can also suggest the most effective execution support method from the user's past execution history. For example, the execution support unit suggests the most effective execution support method from the user's past execution history. This makes it possible to provide the optimal support method based on the past execution history.

[0122] The execution support unit can customize the assistance means based on the user's current living situation. For example, when the user is at work, the execution support unit provides assistance means that can be executed in a short time. For example, when the user is at work, the execution support unit provides assistance means that can be executed in a short time. Furthermore, the execution support unit can also provide detailed execution support when the user is on vacation. For example, when the user is on vacation, the execution support unit provides detailed execution support. Furthermore, when the user is engaged in an activity at home, the execution support unit can also provide assistance means that can be executed at home. For example, when the user is engaged in an activity at home, the execution support unit provides assistance means that can be executed at home. This makes it possible to provide optimal assistance means according to the user's living situation.

[0123] The execution support unit can improve the support method by reflecting the user's feedback. For example, the execution support unit preferentially selects a support method that the user has previously preferred. For example, the execution support unit preferentially selects a support method that the user has previously preferred. The execution support unit can also eliminate a support method that the user has previously avoided. For example, the execution support unit eliminates a support method that the user has previously avoided. Furthermore, the execution support unit can also suggest the most effective support method based on the user's past feedback. For example, the execution support unit suggests the most effective support method based on the user's past feedback. This makes it possible to provide the optimal support method based on the user's feedback.

[0124] The execution support unit can estimate the user's emotions and determine the priority of execution support based on the estimated user's emotions. For example, if the user is feeling stressed, the execution support unit prioritizes assistance that helps the user relax. For example, if the user is feeling stressed, the execution support unit prioritizes assistance that helps the user relax. Furthermore, the execution support unit can prioritize detailed assistance if the user is relaxed. For example, if the user is relaxed, the execution support unit prioritizes detailed assistance. Furthermore, if the user is in a hurry, the execution support unit can prioritize assistance that covers the main points in a short amount of time. For example, if the user is in a hurry, the execution support unit prioritizes assistance that covers the main points in a short amount of time. This makes it possible to provide optimal assistance priorities 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.

[0125] The execution support unit can select the optimal support method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the execution support unit prioritizes support methods related to that area. For example, if the user lives in a specific area, the execution support unit prioritizes support methods related to that area. Furthermore, if the user is traveling, the execution support unit can also prioritize support methods related to the travel destination. For example, if the user is traveling, the execution support unit prioritizes support methods related to the travel destination. Furthermore, if the user is considering moving, the execution support unit can also prioritize support methods related to the moving destination. For example, if the user is considering moving, the execution support unit prioritizes support methods related to the moving destination. This makes it possible to provide the optimal support method based on the user's geographical location information.

[0126] The execution support unit can analyze the user's social media activities and suggest assistance means. The execution support unit can, for example, suggest assistance means based on information shared by the user on social media. For example, the execution support unit can suggest assistance means based on information shared by the user on social media. The execution support unit can also analyze the user's social media activities and suggest related assistance means. For example, the execution support unit can analyze the user's social media activities and suggest related assistance means. Furthermore, the execution support unit can also suggest assistance means by referring to the activities of the user's friends on social media. For example, the execution support unit can suggest assistance means by referring to the activities of the user's friends on social media. This makes it possible to provide optimal assistance means based on the user's social media activities.

[0127] The execution support unit can customize the support method by reflecting the user's past feedback. For example, the execution support unit preferentially selects support methods that the user has preferred in the past. For example, the execution support unit preferentially selects support methods that the user has preferred in the past. The execution support unit can also eliminate support methods that the user has avoided in the past. For example, the execution support unit eliminates support methods that the user has avoided in the past. Furthermore, the execution support unit can also suggest the most effective support method based on the user's past feedback. For example, the execution support unit suggests the most effective support method based on the user's past feedback. This makes it possible to provide the optimal support method based on the user's past feedback.

[0128] The progress management unit can estimate the user's emotions and adjust the progress management method based on the estimated user's emotions. For example, when the user is relaxed, the progress management unit provides detailed progress management. For example, when the user is relaxed, the progress management unit provides detailed progress management. Furthermore, when the user is in a hurry, the progress management unit can provide concise progress management that focuses on the main points. For example, when the user is in a hurry, the progress management unit provides concise progress management that focuses on the main points. Furthermore, when the user is excited, the progress management unit can provide progress management that adds visually stimulating effects. For example, when the user is excited, the progress management unit provides progress management that adds visually stimulating effects. This makes it possible to provide optimal progress management 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.

[0129] The progress management unit can analyze the user's past progress history and select the optimal management method. For example, the progress management unit preferentially selects a progress management method that the user has previously preferred. For example, the progress management unit preferentially selects a progress management method that the user has previously preferred. The progress management unit can also eliminate progress management methods that the user has previously avoided. For example, the progress management unit eliminates progress management methods that the user has previously avoided. Furthermore, the progress management unit can also suggest the most effective progress management method based on the user's past progress history. For example, the progress management unit suggests the most effective progress management method based on the user's past progress history. This makes it possible to provide the optimal management method based on the past progress history.

[0130] The progress management unit can customize the management means based on the user's current living situation. For example, when the user is at work, the progress management unit provides a management means that allows the user to check progress in a short time. For example, when the user is at work, the progress management unit provides a management means that allows the user to check progress in a short time. The progress management unit can also provide detailed progress management when the user is on vacation. For example, when the user is on vacation, the progress management unit provides detailed progress management. Furthermore, when the user is engaged in domestic activities, the progress management unit can also provide a management means that allows the user to check progress at home. For example, when the user is engaged in domestic activities, the progress management unit provides a management means that allows the user to check progress at home. This makes it possible to provide an optimal management means according to the user's living situation.

[0131] The progress management unit can improve the management method by reflecting user feedback. For example, the progress management unit preferentially selects management methods that the user has previously preferred. For example, the progress management unit preferentially selects management methods that the user has previously preferred. The progress management unit can also eliminate management methods that the user has previously avoided. For example, the progress management unit eliminates management methods that the user has previously avoided. Furthermore, the progress management unit can also suggest the most effective management method based on the user's past feedback. For example, the progress management unit suggests the most effective management method based on the user's past feedback. This makes it possible to provide an optimal management method based on the user's feedback.

[0132] The progress management unit can estimate the user's emotions and determine the priority of progress management based on the estimated user's emotions. For example, if the user is feeling stressed, the progress management unit prioritizes management that allows the user to relax. For example, if the user is feeling stressed, the progress management unit prioritizes management that allows the user to relax. The progress management unit can also prioritize detailed management when the user is relaxed. For example, if the user is relaxed, the progress management unit prioritizes detailed management. Furthermore, if the user is in a hurry, the progress management unit can also prioritize management that focuses on the main points in a short amount of time. For example, if the user is in a hurry, the progress management unit prioritizes management that focuses on the main points in a short amount of time. This makes it possible to provide optimal management priorities 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.

[0133] The progress management unit can select the optimal management method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the progress management unit prioritizes a management method related to that area. For example, if the user lives in a specific area, the progress management unit prioritizes a management method related to that area. Furthermore, if the user is traveling, the progress management unit can also prioritize a management method related to the travel destination. For example, if the user is traveling, the progress management unit prioritizes a management method related to the travel destination. Furthermore, if the user is considering moving, the progress management unit can also prioritize a management method related to the new destination. For example, if the user is considering moving, the progress management unit prioritizes a management method related to the new destination. This makes it possible to provide the optimal management method based on the user's geographical location information.

[0134] The progress management unit can analyze the user's social media activities and propose management measures. The progress management unit can, for example, propose management measures based on information shared by the user on social media. For example, the progress management unit can propose management measures based on information shared by the user on social media. The progress management unit can also analyze the user's social media activities and propose related management measures. For example, the progress management unit can analyze the user's social media activities and propose related management measures. The progress management unit can also propose management measures by taking into account the activities of the user's friends on social media. For example, the progress management unit can propose management measures by taking into account the activities of the user's friends on social media. This makes it possible to provide optimal management measures based on the user's social media activities.

[0135] The progress management unit can customize the management method by reflecting the user's past feedback. For example, the progress management unit preferentially selects management methods that the user has previously preferred. For example, the progress management unit preferentially selects management methods that the user has previously preferred. The progress management unit can also eliminate management methods that the user has previously avoided. For example, the progress management unit eliminates management methods that the user has previously avoided. Furthermore, the progress management unit can also suggest the most effective management method based on the user's past feedback. For example, the progress management unit suggests the most effective management method based on the user's past feedback. This makes it possible to provide an optimal management method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the hearing unit, analysis unit, report generation unit, plan proposal unit, execution support unit, and progress management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the hearing unit collects user input information using the reception device 38 or camera 42 of the smart device 14 and inputs the user information to the generation AI via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The report generation unit generates a report based on the analysis results via the specific processing unit 290 of the data processing device 12. The plan proposal unit proposes a plan based on the report generated by the specific processing unit 290 of the data processing device 12. The execution support unit supports the execution of the plan via the control unit 46A of the smart device 14. The progress management unit manages the progress of the plan via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the hearing unit, analysis unit, report generation unit, plan proposal unit, execution support unit, and progress management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the hearing unit collects user input information using the microphone 238 or camera 42 of the smart glasses 214 and inputs the user information to the generation AI via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The report generation unit generates the analysis results as a report via the specific processing unit 290 of the data processing device 12. The plan proposal unit proposes a plan based on the report generated by the specific processing unit 290 of the data processing device 12. The execution support unit supports the execution of the plan via the control unit 46A of the smart glasses 214. The progress management unit manages the progress of the plan via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the hearing unit, analysis unit, report generation unit, plan proposal unit, execution support unit, and progress management unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the hearing unit collects user input information using the microphone 238 or camera 42 of the headset-type terminal 314 and inputs the user information to the generation AI via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The report generation unit generates a report based on the analysis results via the specific processing unit 290 of the data processing device 12. The plan proposal unit proposes a plan based on the report generated by the specific processing unit 290 of the data processing device 12. The execution support unit supports the execution of the plan via the control unit 46A of the headset-type terminal 314. The progress management unit manages the progress of the plan via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the hearing unit, analysis unit, report generation unit, plan proposal unit, execution support unit, and progress management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the hearing unit collects user input information using the microphone 238 or camera 42 of the robot 414 and inputs the user information to the generation AI via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The report generation unit generates a report based on the analysis results via the specific processing unit 290 of the data processing device 12. The plan proposal unit proposes a plan based on the report generated by the specific processing unit 290 of the data processing device 12. The execution support unit supports the execution of the plan via the control unit 46A of the robot 414. The progress management unit manages the progress of the plan via the specific processing unit 290 of the data processing device 12.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The life navigation system may further include a health management unit that collects the user's health data and supports future planning based on the user's health status. For example, the health management unit may collect the user's fitness data and set health goals. The health management unit may also analyze the user's dietary data and suggest ways to improve nutritional balance. Furthermore, the health management unit may monitor the user's sleep data and provide advice on improving sleep quality. This allows for future planning that takes the user's health status into consideration.

[0138] The hearing unit can estimate the user's emotions and adjust the content of the hearing based on the estimated user emotions. For example, if the user is feeling stressed, it prioritizes questions that will help the user relax. Also, if the user is relaxed, it can ask detailed questions. Furthermore, if the user is in a hurry, it can ask questions that get to the point in a short amount of time. This allows for optimal hearing according to the user's emotions.

[0139] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, analysis results with added visually stimulating effects can be provided. This makes it possible to provide the optimal analysis presentation according to the user's emotions.

[0140] The report generation unit can estimate the user's emotions and adjust the presentation of the report based on the estimated user's emotions. For example, if the user is relaxed, a detailed report can be provided. If the user is in a hurry, a concise report that focuses on the main points can be provided. Furthermore, if the user is excited, a report with visually stimulating effects can be provided. In this way, it is possible to provide the optimal report presentation according to the user's emotions.

[0141] The plan suggestion unit can estimate the user's emotions and adjust the way the plan is presented based on the estimated user's emotions. For example, if the user is relaxed, a detailed plan can be presented. If the user is in a hurry, a concise plan that focuses on the main points can be presented. Furthermore, if the user is excited, a plan with visually stimulating effects can be presented. In this way, it is possible to present the optimal plan presentation according to the user's emotions.

[0142] The execution support unit can select the optimal support method by analyzing the user's past execution history. For example, it can prioritize the selection of execution support methods that the user has previously preferred. It can also eliminate execution support methods that the user has previously avoided. Furthermore, it can suggest the most effective execution support method based on the user's past execution history. This makes it possible to provide the optimal support method based on the user's past execution history.

[0143] The progress management unit can customize the management means based on the user's current living situation. For example, if the user is at work, a management means is provided that allows the user to check progress in a short time. If the user is on vacation, detailed progress management can be provided. Furthermore, if the user is engaged in activities at home, a management means is provided that allows the user to check progress at home. This makes it possible to provide the optimal management means according to the user's living situation.

[0144] The progress management unit can improve the management method by reflecting the user's feedback. For example, it can prioritize the selection of management methods that the user has previously preferred. It can also eliminate management methods that the user has previously avoided. Furthermore, it can suggest the most effective management method based on the user's past feedback. This makes it possible to provide the optimal management method based on the user's feedback.

[0145] The progress management unit can estimate the user's emotions and determine the priority of progress management based on the estimated user's emotions. For example, if the user is feeling stressed, priority can be given to management that allows the user to relax. Also, if the user is relaxed, priority can be given to detailed management. Furthermore, if the user is in a hurry, priority can be given to management that focuses on the main points in a short amount of time. This makes it possible to provide optimal management priorities according to the user's emotions.

[0146] The progress management unit can select the optimal management method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, a management method related to that area can be prioritized. Also, if the user is traveling, a management method related to the travel destination can be prioritized. Furthermore, if the user is considering moving, a management method related to the new destination can be prioritized. This makes it possible to provide the optimal management method based on the user's geographical location information.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The hearing unit collects user input information. User input information includes text, voice, images, etc. The hearing unit collects user information using questionnaires and sensors, and can also collect log data. For example, the hearing unit collects user input information and inputs it into the generation AI. Step 2: The analysis unit analyzes the information collected by the hearing unit. The analysis is performed using methods such as statistical analysis, machine learning, and natural language processing. For example, the analysis unit performs statistical analysis on the collected information to understand user trends. It can also analyze user behavior patterns using machine learning and analyze user text data using natural language processing. Step 3: The report generation unit generates a report based on the results of the analysis performed by the analysis unit. The report may be generated in PDF format, HTML format, graph format, text format, or other format. For example, the report generation unit may generate a report in PDF format or HTML format containing the analysis results, and may also include graphs or text in the report. Step 4: The plan suggestion unit proposes a plan based on the report generated by the report generation unit. The plan is proposed based on the content of a short-term plan, a long-term plan, resource allocation, etc. For example, the plan suggestion unit proposes a plan based on the user's career goals, lifestyle improvement, and financial plan. Step 5: The execution support unit provides support for executing the plan proposed by the plan proposal unit. Execution support is performed using methods such as task management, project management, and resource management. For example, the execution support unit manages the user's tasks, performs project management, and resource management, and supports the execution of the plan. Step 6: The progress management unit manages the progress of the plan executed by the execution support unit. Progress management is performed using methods such as progress rate, milestones, and task completion status. For example, the progress management unit manages the progress rate of the plan and provides feedback to the user. Milestones can also be set and task completion status can be managed.

[0149] 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.

[0150] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 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 hearing section for collecting user input information; an analysis unit that analyzes the information collected by the hearing unit; a report generation unit that generates a report based on the results of the analysis performed by the analysis unit; a plan proposal unit that proposes a plan based on the report generated by the report generation unit; an execution support unit for executing the plan proposed by the plan proposal unit; a progress management unit that manages the progress of the plan executed by the execution support unit. A system characterized by:

2. The hearing section Listen to users' visions of the future and their current situation 2. The system of claim 1.

3. The analysis unit Analyzing the information collected by the hearing section 2. The system of claim 1.

4. The report generation unit The analysis results obtained by the analysis unit are generated as a report.

2. The system of claim 1.

5. The plan proposal unit Proposing a plan based on the report generated by the report generating unit 2. The system of claim 1.

6. The execution support unit Providing support for executing the plan proposed by the plan proposal unit 2. The system of claim 1.

7. The progress management unit Manage the progress of the plan executed by the execution support unit 2. The system of claim 1.

8. The hearing section Estimate the user's emotions and adjust the timing of hearings based on the estimated user emotions 2. The system of claim 1.

9. The hearing section Analyze the user's past interview history and select the optimal interview method 2. The system of claim 1.

10. The hearing section Customize questions based on the user's current life situation and interests 2. The system of claim 1.

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