System

Abstract

To provide a system that supports individuals in the developmental disorder gray zone in understanding their traits and sharing them with those around them.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and an output unit. The reception unit accepts input of concerns from a user. The analysis unit analyzes the data received by the reception unit to examine the user's traits deeply. The output unit structures obtained by the analysis unit and outputs them as a report.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide people in the gray zone of developmental disorders with the means to understand their own characteristics and share them with those around them, and there is room for improvement.

[0005] The system according to the embodiment aims to enable people with developmental disabilities in the gray zone to understand their own characteristics and share them with those around them. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an output unit. The reception unit receives input of a problem from a user. The analysis unit analyzes the information received by the reception unit and delves into the characteristics of the user. The output unit organizes the characteristics obtained by the analysis unit and outputs the characteristics as a report. Effect of the Invention

[0007] The system according to the embodiment enables people with developmental disabilities in the gray zone to understand their own characteristics and share them with those around them. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The solution system according to the embodiment of the present invention is a solution system for people who find it difficult to live in society due to their own characteristics. This solution system allows people in the "gray zone of developmental disorders" who cannot receive official diagnosis or support to present their daily problems to a chatbot, which then organizes the characteristics of each person and makes it possible for them to understand the problem themselves and share it with others in the form of a report. For example, a user inputs their daily problems into a chatbot. For example, a problem such as "I can't manage my work schedule well" is input. This information is input into a generation AI. Next, the generation AI analyzes the input information and delves into the user's characteristics. The generation AI identifies the background and cause of the user's problems based on the learned data. For example, it identifies that the cause of poor schedule management is a lack of attention or a lack of sense of time. The generation AI organizes the identified characteristics and outputs them as a report. This report includes the user's characteristics, the cause of the problem, and improvement measures. For example, for a user who is poor at schedule management, specific time management methods and tool suggestions are included. This report not only deepens the user's own understanding, but also allows them to share it with those around them. For example, it is useful when explaining one's characteristics and problems to superiors or colleagues at work. This makes it easier to gain understanding from those around oneself, and reduces the difficulties of living in society. By using generative AI, this solution can analyze the user's characteristics in detail and provide support according to individual needs. This allows people in the "gray zone of developmental disorders" to understand themselves better and live social lives more smoothly. This allows the solution system to analyze the user's characteristics in detail and provide support according to individual needs.

[0029] The solution system according to the embodiment includes a reception unit, an analysis unit, and an output unit. The reception unit receives input of a problem from a user. The problem from the user includes, for example, technical problems, problems in daily life, and the like, but is not limited to such examples. The reception unit can receive the problem by, for example, a method such as text input, voice input, image input, and the like. The analysis unit uses a generation AI to analyze the information received by the reception unit and delve into the characteristics of the user. Specific methods and standards for delving into the characteristics include, for example, analysis of behavioral patterns, evaluation of psychological characteristics, and the like, but are not limited to such examples. For example, the generation AI uses a text generation AI (for example, LLM) to identify the background and cause of the user's problem. In addition, the analysis unit can also use a multimodal generation AI to identify the background and cause of the user's problem. For example, the generation AI identifies the background and cause of the user's problem based on the learned data. The output unit organizes the characteristics obtained by the analysis unit and outputs it as a report. Specific contents and formats of the report include, for example, text format, graph format, types of information included, and the like, but are not limited to such examples. For example, the output unit generates a report including the user's characteristics, causes of problems, and remedial measures. This enables the solution system according to the embodiment to efficiently analyze the user's problems, organize the characteristics, and output them as a report. A part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can generate a report using a generation AI model that inputs the characteristics obtained by the analysis unit and outputs a report.

[0030] The reception unit receives input of a problem from a user. Problems from a user include, for example, technical problems, problems in daily life, etc., but are not limited to such examples. The reception unit can receive problems by, for example, text input, voice input, image input, and other methods. Specifically, in the case of text input, a user can input a problem through a dedicated input form or chat box. In the case of voice input, a user can use a microphone to voice a problem, which is converted into text using voice recognition technology. In the case of image input, a user can upload a photo or screenshot showing the problem situation. In this way, the reception unit supports various input methods, allowing users to report problems in the most convenient way. In addition, the reception unit can temporarily store the input information and perform necessary pre-processing before sending it to the analysis unit. For example, in the case of voice input, noise removal and voice clearing can be performed, and in the case of image input, the image resolution can be adjusted or necessary parts can be cropped. In addition, the reception unit can perform initial filtering to classify the user's input content and select an appropriate analysis method. This enables the reception unit to efficiently receive various problems from users and provide the information to the analysis unit in an appropriate format.

[0031] The analysis unit uses the generation AI to analyze the information received by the reception unit and delve into the characteristics of the user. Specific methods and standards for delving into the characteristics include, but are not limited to, for example, analysis of behavioral patterns and evaluation of psychological characteristics. For example, the generation AI uses a text generation AI (e.g., LLM) to identify the background and cause of the user's trouble. Specifically, the text generation AI analyzes the text entered by the user, understands the context using natural language processing technology, and extracts the factors behind the trouble. The analysis unit can also use a multimodal generation AI to identify the background and cause of the user's trouble. For example, in the case of image input, image recognition technology is used to analyze the content of the image and the cause of the trouble is identified by integrating it with related text information. Furthermore, in the case of voice input, voice recognition technology is used to convert the voice into text, and analysis is performed based on the text. The generation AI identifies the background and cause of the user's trouble based on the learned data. For example, it is possible to search for similar troubles from a past database and refer to their solutions. The analysis unit combines these technologies to analyze the user's problems from multiple angles and perform more accurate characteristic evaluations, which allows the analysis unit to provide basic information for a deeper understanding of the user's problems and to propose appropriate solutions.

[0032] The output unit organizes the characteristics obtained by the analysis unit and outputs them as a report. Specific contents and formats of the report include, but are not limited to, for example, a text format, a graph format, and the type of information included. For example, the output unit generates a report including the user's characteristics, causes of problems, and improvement measures. Specifically, in a text format report, a detailed explanation of the user's problems, analysis results, recommended solutions, and the like are described. In a graph format report, a visual representation of data can be used to show the trends of the user's characteristics and problems. For example, a line graph showing changes in behavior patterns and a radar chart showing evaluation results of psychological characteristics are included. In addition, the output unit can customize the contents of the report and adjust the type and level of detail of information according to the user's needs. Furthermore, the output unit can utilize a generation AI in the report generation process. For example, a report can be automatically generated using a generation AI model that uses the characteristics obtained by the analysis unit as input and outputs a report. This allows the output unit to provide a high-quality report quickly and efficiently. The output unit supports various means for providing the generated report to the user. For example, the information can be sent by email, downloaded from a dedicated web portal, printed and mailed, etc. In this way, the output unit can provide the information to the user in an appropriate format and help solve the problem.

[0033] The identification unit can identify the background and cause of the user's trouble using a learning method or algorithm of the generation AI. The learning method or algorithm of the generation AI includes, for example, deep learning, reinforcement learning, and the like, but is not limited to such examples. The identification unit can identify the background and cause of the user's trouble using, for example, deep learning. The identification unit can also identify the background and cause of the user's trouble using reinforcement learning. For example, the identification unit uses deep learning to build a model for identifying the background and cause of the user's trouble. Reinforcement learning is a method of learning optimal actions through trial and error, and the identification unit uses reinforcement learning to develop an algorithm for identifying the background and cause of the user's trouble. As a result, the identification unit can provide more appropriate support by identifying the background and cause of the user's trouble. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can build a model for identifying the background and cause of the user's trouble using the generation AI.

[0034] The output unit can generate a report including the user's characteristics, causes of the troubles, and remedial measures. Specific contents and formats of the report include, for example, a text format, a graph format, and types of information included, but are not limited to such examples. The output unit generates, for example, a report including the user's characteristics, causes of the troubles, and remedial measures. For example, the output unit generates a text report that describes the user's characteristics in detail. The output unit can also generate a report that identifies the cause of the trouble and proposes remedial measures based on the cause. For example, the output unit uses a template for generating a report including the user's characteristics, causes of the troubles, and remedial measures. As a result, the output unit can deepen the user's understanding by generating a report including the user's characteristics, causes of the troubles, and remedial measures. A part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can generate a report including the user's characteristics, causes of the troubles, and remedial measures using a generation AI.

[0035] The output unit can propose improvement measures based on the characteristics of the user. Specific content and proposal methods of improvement measures based on the characteristics include, for example, individualized advice, customized solutions, etc., but are not limited to such examples. The output unit, for example, proposes specific improvement measures based on the characteristics of the user. For example, the output unit proposes a time management method or tool based on the characteristics of the user. The output unit can also propose improvement measures for communication based on the characteristics of the user. For example, the output unit proposes specific behavioral changes based on the characteristics of the user. Thereby, the output unit can help the user solve the troubles by proposing specific improvement measures based on the characteristics of the user. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can propose specific improvement measures based on the characteristics of the user using a generation AI.

[0036] The reception unit can analyze the user's past problem input history and select an appropriate input method. Specific contents and analysis methods of the past problem input history include, for example, past input data, frequency analysis, etc., but are not limited to such examples. For example, if the user has used text input in the past, the reception unit preferentially suggests text input. In addition, if the user has used voice input in the past, the reception unit can also preferentially suggest voice input. Furthermore, if the user has input a problem in a specific time period in the past, the reception unit can also prompt the user to input during that time period. In this way, the reception unit can propose an optimal input method by analyzing the user's past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's past problem input history and select an appropriate input method using a generation AI.

[0037] The reception unit can filter the troubles based on the user's current living situation and field of interest when inputting the troubles. Specific contents and evaluation criteria of the living situation include, for example, daily activity patterns, living environment, etc., but are not limited to such examples. Specific contents and identification methods of the field of interest include, for example, hobbies, topics of interest, etc., but are not limited to such examples. For example, when the user is at work, the reception unit preferentially inputs troubles related to work. In addition, when the user is spending time at home, the reception unit can also preferentially input troubles related to home. Furthermore, when the user is interested in a hobby, the reception unit can also preferentially input troubles related to the hobby. In this way, the reception unit can input more relevant troubles by filtering based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's living situation and field of interest using a generation AI and perform filtering.

[0038] When inputting a problem, the reception unit can preferentially input a problem with high relevance based on the geographical location information of the user. Specific contents and acquisition methods of the geographical location information include, for example, GPS data, location information services, etc., but are not limited to such examples. Specific contents and identification methods of the problem with high relevance include, for example, problems specific to the region, geographical influences, etc., but are not limited to such examples. For example, when the user is in a specific region, the reception unit preferentially inputs a problem related to the region. In addition, when the user is traveling, the reception unit can also preferentially input a problem related to the travel. Furthermore, when the user is at home, the reception unit can also preferentially input a problem related to the home. In this way, the reception unit can preferentially input a problem with high relevance in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the geographical location information of the user using a generation AI, and preferentially input a problem with high relevance.

[0039] The reception unit can analyze the social media activity of the user when inputting the problem, and input the related problem. Specific contents and analysis methods of the social media activity include, for example, the post content, the reaction of followers, and the like, but are not limited to such examples. For example, the reception unit preferentially inputs the problem that the user frequently mentions on social media. The reception unit can also preferentially input the problem related to the topic that the user follows on social media. Furthermore, the reception unit can also preferentially input the problem related to the group that the user participates in on social media. In this way, the reception unit can input the related problem by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can analyze the social media activity of the user and input the related problem using the generation AI.

[0040] The analysis unit can adjust the details of the analysis based on the importance of the problem during the analysis. Specific evaluation criteria and methods for the importance of the problem include, for example, evaluation of the impact level and judgment of the urgency level, but are not limited to such examples. Specific methods and criteria for adjusting the details include, for example, the depth of the analysis and the level of detail of the information, but are not limited to such examples. For example, the analysis unit performs a detailed analysis on a problem with a high level of importance. In addition, the analysis unit can also perform a simplified analysis on a problem with a low level of importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on a problem with a medium level of importance. Thereby, the analysis unit can provide an appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the problem. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the details of the analysis based on the importance of the problem using a generation AI.

[0041] The analysis unit can apply an analysis algorithm according to the category of the problem during analysis. Specific classification methods and standards for the categories of problems include, for example, technical problems, psychological problems, etc., but are not limited to such examples. Specific types and implementation methods of the analysis algorithm include, for example, clustering algorithms, regression analysis, etc., but are not limited to such examples. For example, the analysis unit applies a time management algorithm to problems related to schedule management. In addition, the analysis unit can also apply a communication analysis algorithm to problems related to human relationships. Furthermore, the analysis unit can also apply a health data analysis algorithm to problems related to health. Thereby, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms according to the categories of the problems. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can apply an analysis algorithm according to the category of the problem using a generation AI.

[0042] During the analysis, the analysis unit can determine the order of analysis based on the submission time of each problem. Specific evaluation criteria and methods for the submission time include, for example, the submission date and time, the submission frequency, and the like, but are not limited to such examples. Specific methods and criteria for determining the order include, for example, evaluation of priority, judgment of urgency, and the like, but are not limited to such examples. For example, the analysis unit prioritizes analysis of recently submitted problems. In addition, the analysis unit can also postpone problems submitted earlier. Furthermore, the analysis unit can also moderately analyze problems submitted at a medium time. This allows the analysis unit to perform analysis in an appropriate order by determining the priority of analysis based on the submission time of each problem. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to determine the order of analysis based on the submission time of each problem.

[0043] The analysis unit can adjust the order of analysis based on the relationship between the problems during analysis. Specific evaluation criteria and methods for the relationship between the problems include, for example, analysis of causal relationships, evaluation of relevance, etc., but are not limited to such examples. Specific methods and criteria for adjusting the order include, for example, evaluation of priority, judgment of urgency, etc., but are not limited to such examples. For example, the analysis unit prioritizes analysis of problems with high relevance. In addition, the analysis unit can postpone problems with low relevance. Furthermore, the analysis unit can also moderately analyze problems with medium relevance. Thereby, the analysis unit can perform analysis in an appropriate order by adjusting the order of analysis based on the relevance between the problems. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relationship between the problems using a generation AI.

[0044] The output unit can adjust the details of the report based on the importance of the problem when generating the report. Specific evaluation criteria and methods for the importance of the problem include, for example, evaluation of the impact level, judgment of the urgency, etc., but are not limited to such examples. Specific methods and criteria for adjusting the details include, for example, the depth of the report, the detail level of the information, etc., but are not limited to such examples. For example, the output unit generates a detailed report for a problem with a high importance level. In addition, the output unit can also generate a simple report for a problem with a low importance level. Furthermore, the output unit can also generate a report with an appropriate degree of detail for a problem with a medium importance level. Thereby, the output unit can provide an appropriate report by adjusting the detail level of the report based on the importance of the problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can adjust the details of the report based on the importance of the problem using a generation AI.

[0045] The output unit can apply a report generation algorithm according to the category of the problem when generating the report. Specific classification methods and standards for the categories of the problems include, for example, technical problems, psychological problems, etc., but are not limited to such examples. Specific types and implementation methods of the report generation algorithm include, for example, template-based generation, data-driven generation, etc., but are not limited to such examples. For example, the output unit applies a time management report generation algorithm to a problem related to schedule management. In addition, the output unit can also apply a communication report generation algorithm to a problem related to human relationships. Furthermore, the output unit can also apply a health data report generation algorithm to a problem related to health. Thereby, the output unit can provide a more appropriate report by applying different report generation algorithms according to the category of the problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can apply a report generation algorithm according to the category of the problem using a generation AI.

[0046] The output unit can determine the order of the reports based on the submission time of each problem when generating the reports. Specific evaluation criteria and methods for the submission time include, for example, submission date and time, submission frequency, etc., but are not limited to such examples. Specific methods and criteria for determining the order include, for example, evaluation of priority, judgment of urgency, etc., but are not limited to such examples. The output unit, for example, generates reports preferentially for recently submitted problems. In addition, the output unit can also generate reports later for problems submitted earlier. Furthermore, the output unit can also generate reports appropriately for problems submitted at a medium time. In this way, the output unit can generate reports in an appropriate order by determining the priority of reports based on the submission time of each problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can determine the order of reports based on the submission time of each problem using a generation AI.

[0047] The output unit can adjust the order of the reports based on the relationship with each problem when generating the report. Specific evaluation criteria and methods for the relationship with each problem include, for example, analysis of causal relationship, evaluation of relevance, etc., but are not limited to such examples. Specific methods and criteria for adjusting the order include, for example, evaluation of priority, judgment of urgency, etc., but are not limited to such examples. For example, the output unit generates a report preferentially for a problem with high relevance. In addition, the output unit can also generate a report later for a problem with low relevance. Furthermore, the output unit can generate a report appropriately for a problem with medium relevance. Thereby, the output unit can generate a report in an appropriate order by adjusting the order of the reports based on the relevance of each problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can adjust the order of the reports based on the relationship with each problem using a generation AI.

[0048] When identifying the background or cause of a problem, the identification unit can select an appropriate identification method based on the user's past behavior history. Specific contents and analysis methods of the past behavior history include, for example, past behavior data, frequency analysis, etc., but are not limited to such examples. Specific contents and selection criteria of the appropriate identification method include, for example, data analysis methods, heuristic approaches, etc., but are not limited to such examples. The identification unit, for example, identifies the background or cause based on problems that the user has frequently experienced in the past. In addition, the identification unit can also analyze the user's past behavior patterns and identify the cause of the problem. Furthermore, the identification unit can identify the cause of the problem related to a specific time period or situation from the user's past behavior history. Thereby, the identification unit can select an optimal identification method by referring to the user's past behavior history. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the user's past behavior history and select an appropriate identification method using a generation AI.

[0049] The identification unit can adjust the specific means based on the user's current living situation when identifying the background or cause of the trouble. Specific contents and evaluation criteria of the current living situation include, for example, daily activity patterns, living environment, etc., but are not limited to such examples. Specific methods and criteria for adjusting the specific means include, for example, selection of an algorithm, depth of analysis, etc., but are not limited to such examples. For example, when the user is in a current work environment, the identification unit identifies the cause of the trouble related to the environment. In addition, when the user is spending time at home, the identification unit can also identify the cause of the trouble related to the home. Furthermore, when the user is interested in a hobby, the identification unit can also identify the cause of the trouble related to the hobby. In this way, the identification unit can provide a more appropriate identification result by customizing the specific means based on the user's current living situation. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the user's current living situation and adjust the specific means using a generation AI.

[0050] When identifying the background or cause of a problem, the identification unit can select an appropriate identification method based on the geographical location information of the user. Specific content and acquisition method of the geographical location information include, for example, GPS data, location information services, etc., but are not limited to such examples. For example, when the user is in a specific area, the identification unit identifies the cause of the problem related to the area. In addition, when the user is traveling, the identification unit can also identify the cause of the travel-related problem. Furthermore, when the user is at home, the identification unit can also identify the cause of the home-related problem. In this way, the identification unit can select an optimal identification method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the geographical location information of the user using a generation AI and select an appropriate identification method.

[0051] When identifying the background or cause of a problem, the identification unit can analyze the user's social media activity and propose a measure. Specific content and analysis method of the social media activity include, for example, the content of posts, the reaction of followers, etc., but are not limited to such examples. The identification unit, for example, identifies the cause of the problem that the user frequently mentions on social media. The identification unit can also identify the cause of the problem related to a topic that the user follows on social media. Furthermore, the identification unit can also identify the cause of the problem related to a group that the user participates in on social media. In this way, the identification unit can propose a specific measure by analyzing the user's social media activity. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the user's social media activity and propose a specific measure using a generation AI.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can analyze the user's past behavioral patterns and customize the input method for the problem. For example, if the user has preferred text input in the past, text input can be preferentially suggested. Also, if the user has used voice input in the past, voice input can be preferentially suggested. Furthermore, if the user has input a problem during a specific time period in the past, input during that time period can be prompted. In this way, the reception unit can suggest the optimal input method by analyzing the user's past behavioral patterns.

[0054] The analysis unit can adjust the level of detail of the analysis based on the user's living environment. For example, when the user is at work, work-related problems are analyzed with priority. Also, when the user is spending time at home, home-related problems can be analyzed with priority. Furthermore, when the user is interested in a hobby, problems related to the hobby can be analyzed with priority. In this way, the analysis unit can provide more relevant analysis results by adjusting the level of detail of the analysis based on the user's living environment.

[0055] The reception unit can preferentially input a problem that is highly relevant based on the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially inputs a problem related to the area. When the user is traveling, the reception unit can preferentially input a problem related to the travel. When the user is at home, the reception unit can preferentially input a problem related to the home. This allows the reception unit to preferentially input a problem that is highly relevant in consideration of the geographical location information of the user.

[0056] The analysis unit can adjust the details of the analysis based on the importance of the problem. For example, a detailed analysis can be performed for a problem with a high importance. Also, a simplified analysis can be performed for a problem with a low importance. Furthermore, an analysis with an appropriate level of detail can be performed for a problem with a medium importance. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the problem.

[0057] When generating a report, the output unit can apply a report generation algorithm according to the category of the problem. For example, a time management report generation algorithm can be applied to a problem related to schedule management. A communication report generation algorithm can also be applied to a problem related to human relationships. Furthermore, a health data report generation algorithm can be applied to a problem related to health. In this way, the output unit can provide a more appropriate report by applying different report generation algorithms according to the category of the problem.

[0058] When identifying the background or cause of a problem, the identification unit can select an appropriate identification method based on the user's past behavior history. For example, the identification unit can identify the background or cause based on problems that the user has frequently experienced in the past. The identification unit can also identify the cause of a problem by analyzing the user's past behavior patterns. Furthermore, the identification unit can identify the cause of a problem related to a specific time period or situation from the user's past behavior history. This allows the identification unit to select the optimal identification method by referring to the user's past behavior history.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The reception unit receives input of a problem from a user. Problems from users include technical problems and problems in daily life. The reception unit can receive problems by text input, voice input, image input, and other methods. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and delve deeper into the user's characteristics. Specific methods include analyzing behavioral patterns and evaluating psychological characteristics. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to identify the background and causes of the user's problems. Step 3: The output unit organizes the characteristics obtained by the analysis unit and outputs them as a report. The content and format of the report can be in text or graph format, and includes the user's characteristics, causes of problems, and improvement measures. The output unit can also generate reports using a generative AI model.

[0061] (Example 2) The solution system according to the embodiment of the present invention is a solution system for people who find it difficult to live in society due to their own characteristics. This solution system allows people in the "gray zone of developmental disorders" who cannot receive official diagnosis or support to present their daily problems to a chatbot, which then organizes the characteristics of each person and makes it possible for them to understand the problem themselves and share it with others in the form of a report. For example, a user inputs their daily problems into a chatbot. For example, a problem such as "I can't manage my work schedule well" is input. This information is input into a generation AI. Next, the generation AI analyzes the input information and delves into the user's characteristics. The generation AI identifies the background and cause of the user's problems based on the learned data. For example, it identifies that the cause of poor schedule management is a lack of attention or a lack of sense of time. The generation AI organizes the identified characteristics and outputs them as a report. This report includes the user's characteristics, the cause of the problem, and improvement measures. For example, for a user who is poor at schedule management, specific time management methods and tool suggestions are included. This report not only deepens the user's own understanding, but also allows them to share it with those around them. For example, it is useful when explaining one's characteristics and problems to superiors or colleagues at work. This makes it easier to gain understanding from those around oneself, and reduces the difficulties of living in society. By using generative AI, this solution can analyze the user's characteristics in detail and provide support according to individual needs. This allows people in the "gray zone of developmental disorders" to understand themselves better and live social lives more smoothly. This allows the solution system to analyze the user's characteristics in detail and provide support according to individual needs.

[0062] The solution system according to the embodiment includes a reception unit, an analysis unit, and an output unit. The reception unit receives input of a problem from a user. The problem from the user includes, for example, technical problems, problems in daily life, and the like, but is not limited to such examples. The reception unit can receive the problem by, for example, a method such as text input, voice input, image input, and the like. The analysis unit uses a generation AI to analyze the information received by the reception unit and delve into the characteristics of the user. Specific methods and standards for delving into the characteristics include, for example, analysis of behavioral patterns, evaluation of psychological characteristics, and the like, but are not limited to such examples. For example, the generation AI uses a text generation AI (for example, LLM) to identify the background and cause of the user's problem. In addition, the analysis unit can also use a multimodal generation AI to identify the background and cause of the user's problem. For example, the generation AI identifies the background and cause of the user's problem based on the learned data. The output unit organizes the characteristics obtained by the analysis unit and outputs it as a report. Specific contents and formats of the report include, for example, text format, graph format, types of information included, and the like, but are not limited to such examples. For example, the output unit generates a report including the user's characteristics, causes of problems, and remedial measures. This enables the solution system according to the embodiment to efficiently analyze the user's problems, organize the characteristics, and output them as a report. A part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can generate a report using a generation AI model that inputs the characteristics obtained by the analysis unit and outputs a report.

[0063] The reception unit receives input of a problem from a user. Problems from a user include, for example, technical problems, problems in daily life, etc., but are not limited to such examples. The reception unit can receive problems by, for example, text input, voice input, image input, and other methods. Specifically, in the case of text input, a user can input a problem through a dedicated input form or chat box. In the case of voice input, a user can use a microphone to voice a problem, which is converted into text using voice recognition technology. In the case of image input, a user can upload a photo or screenshot showing the problem situation. In this way, the reception unit supports various input methods, allowing users to report problems in the most convenient way. In addition, the reception unit can temporarily store the input information and perform necessary pre-processing before sending it to the analysis unit. For example, in the case of voice input, noise removal and voice clearing can be performed, and in the case of image input, the image resolution can be adjusted or necessary parts can be cropped. In addition, the reception unit can perform initial filtering to classify the user's input content and select an appropriate analysis method. This enables the reception unit to efficiently receive various problems from users and provide the information to the analysis unit in an appropriate format.

[0064] The analysis unit uses the generation AI to analyze the information received by the reception unit and delve into the characteristics of the user. Specific methods and standards for delving into the characteristics include, but are not limited to, for example, analysis of behavioral patterns and evaluation of psychological characteristics. For example, the generation AI uses a text generation AI (e.g., LLM) to identify the background and cause of the user's trouble. Specifically, the text generation AI analyzes the text entered by the user, understands the context using natural language processing technology, and extracts the factors behind the trouble. The analysis unit can also use a multimodal generation AI to identify the background and cause of the user's trouble. For example, in the case of image input, image recognition technology is used to analyze the content of the image and the cause of the trouble is identified by integrating it with related text information. Furthermore, in the case of voice input, voice recognition technology is used to convert the voice into text, and analysis is performed based on the text. The generation AI identifies the background and cause of the user's trouble based on the learned data. For example, it is possible to search for similar troubles from a past database and refer to their solutions. The analysis unit combines these technologies to analyze the user's problems from multiple angles and perform more accurate characteristic evaluations, which allows the analysis unit to provide basic information for a deeper understanding of the user's problems and to propose appropriate solutions.

[0065] The output unit organizes the characteristics obtained by the analysis unit and outputs them as a report. Specific contents and formats of the report include, but are not limited to, for example, a text format, a graph format, and the type of information included. For example, the output unit generates a report including the user's characteristics, causes of problems, and improvement measures. Specifically, in a text format report, a detailed explanation of the user's problems, analysis results, recommended solutions, and the like are described. In a graph format report, a visual representation of data can be used to show the trends of the user's characteristics and problems. For example, a line graph showing changes in behavior patterns and a radar chart showing evaluation results of psychological characteristics are included. In addition, the output unit can customize the contents of the report and adjust the type and level of detail of information according to the user's needs. Furthermore, the output unit can utilize a generation AI in the report generation process. For example, a report can be automatically generated using a generation AI model that uses the characteristics obtained by the analysis unit as input and outputs a report. This allows the output unit to provide a high-quality report quickly and efficiently. The output unit supports various means for providing the generated report to the user. For example, the information can be sent by email, downloaded from a dedicated web portal, printed and mailed, etc. In this way, the output unit can provide the information to the user in an appropriate format and help solve the problem.

[0066] The identification unit can identify the background and cause of the user's trouble using a learning method or algorithm of the generation AI. The learning method or algorithm of the generation AI includes, for example, deep learning, reinforcement learning, and the like, but is not limited to such examples. The identification unit can identify the background and cause of the user's trouble using, for example, deep learning. The identification unit can also identify the background and cause of the user's trouble using reinforcement learning. For example, the identification unit uses deep learning to build a model for identifying the background and cause of the user's trouble. Reinforcement learning is a method of learning optimal actions through trial and error, and the identification unit uses reinforcement learning to develop an algorithm for identifying the background and cause of the user's trouble. As a result, the identification unit can provide more appropriate support by identifying the background and cause of the user's trouble. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can build a model for identifying the background and cause of the user's trouble using the generation AI.

[0067] The output unit can generate a report including the user's characteristics, causes of the troubles, and remedial measures. Specific contents and formats of the report include, for example, a text format, a graph format, and types of information included, but are not limited to such examples. The output unit generates, for example, a report including the user's characteristics, causes of the troubles, and remedial measures. For example, the output unit generates a text report that describes the user's characteristics in detail. The output unit can also generate a report that identifies the cause of the trouble and proposes remedial measures based on the cause. For example, the output unit uses a template for generating a report including the user's characteristics, causes of the troubles, and remedial measures. As a result, the output unit can deepen the user's understanding by generating a report including the user's characteristics, causes of the troubles, and remedial measures. A part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can generate a report including the user's characteristics, causes of the troubles, and remedial measures using a generation AI.

[0068] The output unit can propose improvement measures based on the characteristics of the user. Specific content and proposal methods of improvement measures based on the characteristics include, for example, individualized advice, customized solutions, etc., but are not limited to such examples. The output unit, for example, proposes specific improvement measures based on the characteristics of the user. For example, the output unit proposes a time management method or tool based on the characteristics of the user. The output unit can also propose improvement measures for communication based on the characteristics of the user. For example, the output unit proposes specific behavioral changes based on the characteristics of the user. Thereby, the output unit can help the user solve the troubles by proposing specific improvement measures based on the characteristics of the user. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can propose specific improvement measures based on the characteristics of the user using a generation AI.

[0069] The reception unit can estimate the user's emotion and adjust the input timing of the problem based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit prompts the user to input the problem during a time period when the user can relax. In addition, when the user is concentrating, the reception unit can also prompt the user to input the problem at that timing. Furthermore, when the user is tired, the reception unit can also prompt the user to input the problem after resting. In this way, the reception unit can adjust the input timing of the problem according to the user's emotion, thereby allowing the user to input the problem at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can estimate the user's emotion using the generation AI and adjust the input timing of the problem based on the estimated emotion.

[0070] The reception unit can analyze the user's past problem input history and select an appropriate input method. Specific contents and analysis methods of the past problem input history include, for example, past input data, frequency analysis, etc., but are not limited to such examples. For example, if the user has used text input in the past, the reception unit preferentially suggests text input. In addition, if the user has used voice input in the past, the reception unit can also preferentially suggest voice input. Furthermore, if the user has input a problem in a specific time period in the past, the reception unit can also prompt the user to input during that time period. In this way, the reception unit can propose an optimal input method by analyzing the user's past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's past problem input history and select an appropriate input method using a generation AI.

[0071] The reception unit can filter the troubles based on the user's current living situation and field of interest when inputting the troubles. Specific contents and evaluation criteria of the living situation include, for example, daily activity patterns, living environment, etc., but are not limited to such examples. Specific contents and identification methods of the field of interest include, for example, hobbies, topics of interest, etc., but are not limited to such examples. For example, when the user is at work, the reception unit preferentially inputs troubles related to work. In addition, when the user is spending time at home, the reception unit can also preferentially input troubles related to home. Furthermore, when the user is interested in a hobby, the reception unit can also preferentially input troubles related to the hobby. In this way, the reception unit can input more relevant troubles by filtering based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's living situation and field of interest using a generation AI and perform filtering.

[0072] The reception unit can estimate the user's emotion and determine the priority of the troubles to be input based on the estimated user's emotion. Specific methods and criteria for estimating the emotion include, but are not limited to, facial expression recognition and voice analysis. Specific methods and criteria for determining the priority include, but are not limited to, importance evaluation and urgency judgment. For example, when the user is stressed, the reception unit preferentially inputs a trouble that reduces stress. In addition, when the user is relaxed, the reception unit can also preferentially input a long-term trouble. Furthermore, when the user is in a hurry, the reception unit can also preferentially input a short-term trouble. In this way, the reception unit can preferentially input a more important trouble by determining the priority of the troubles based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can use the generation AI to estimate the user's emotions and determine the priority order of the troubles based on the estimated emotions.

[0073] When inputting a problem, the reception unit can preferentially input a problem with high relevance based on the geographical location information of the user. Specific contents and acquisition methods of the geographical location information include, for example, GPS data, location information services, etc., but are not limited to such examples. Specific contents and identification methods of the problem with high relevance include, for example, problems specific to the region, geographical influences, etc., but are not limited to such examples. For example, when the user is in a specific region, the reception unit preferentially inputs a problem related to the region. In addition, when the user is traveling, the reception unit can also preferentially input a problem related to the travel. Furthermore, when the user is at home, the reception unit can also preferentially input a problem related to the home. In this way, the reception unit can preferentially input a problem with high relevance in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the geographical location information of the user using a generation AI, and preferentially input a problem with high relevance.

[0074] The reception unit can analyze the social media activity of the user when inputting the problem, and input the related problem. Specific contents and analysis methods of the social media activity include, for example, the post content, the reaction of followers, and the like, but are not limited to such examples. For example, the reception unit preferentially inputs the problem that the user frequently mentions on social media. The reception unit can also preferentially input the problem related to the topic that the user follows on social media. Furthermore, the reception unit can also preferentially input the problem related to the group that the user participates in on social media. In this way, the reception unit can input the related problem by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can analyze the social media activity of the user and input the related problem using the generation AI.

[0075] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. Specific methods and standards for estimating the emotion include, for example, facial expression recognition, voice analysis, etc., but are not limited to such examples. Specific methods and standards for adjusting the expression method include, for example, changing the wording, adjusting the visual expression, etc., but are not limited to such examples. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the expression method of the analysis based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generative AI to infer a user's emotions and adjust the way the analysis is presented based on the inferred emotions.

[0076] The analysis unit can adjust the details of the analysis based on the importance of the problem during the analysis. Specific evaluation criteria and methods for the importance of the problem include, for example, evaluation of the impact level and judgment of the urgency level, but are not limited to such examples. Specific methods and criteria for adjusting the details include, for example, the depth of the analysis and the level of detail of the information, but are not limited to such examples. For example, the analysis unit performs a detailed analysis on a problem with a high level of importance. In addition, the analysis unit can also perform a simplified analysis on a problem with a low level of importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on a problem with a medium level of importance. Thereby, the analysis unit can provide an appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the problem. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the details of the analysis based on the importance of the problem using a generation AI.

[0077] The analysis unit can apply an analysis algorithm according to the category of the problem during analysis. Specific classification methods and standards for the categories of problems include, for example, technical problems, psychological problems, etc., but are not limited to such examples. Specific types and implementation methods of the analysis algorithm include, for example, clustering algorithms, regression analysis, etc., but are not limited to such examples. For example, the analysis unit applies a time management algorithm to problems related to schedule management. In addition, the analysis unit can also apply a communication analysis algorithm to problems related to human relationships. Furthermore, the analysis unit can also apply a health data analysis algorithm to problems related to health. Thereby, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms according to the categories of the problems. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can apply an analysis algorithm according to the category of the problem using a generation AI.

[0078] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. Specific methods and standards for estimating the emotion include, for example, facial expression recognition, voice analysis, etc., but are not limited to such examples. Specific methods and standards for adjusting the length include, for example, the level of detail of the analysis, the amount of information, etc., but are not limited to such examples. For example, when the user is in a hurry, the analysis unit performs a short and to the point analysis. In addition, when the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, when the user is excited, the analysis unit can also perform a visually stimulating analysis. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to estimate a user's emotions and adjust the length of the analysis based on the estimated emotions.

[0079] During the analysis, the analysis unit can determine the order of analysis based on the submission time of each problem. Specific evaluation criteria and methods for the submission time include, for example, the submission date and time, the submission frequency, and the like, but are not limited to such examples. Specific methods and criteria for determining the order include, for example, evaluation of priority, judgment of urgency, and the like, but are not limited to such examples. For example, the analysis unit prioritizes analysis of recently submitted problems. In addition, the analysis unit can also postpone problems submitted earlier. Furthermore, the analysis unit can also moderately analyze problems submitted at a medium time. This allows the analysis unit to perform analysis in an appropriate order by determining the priority of analysis based on the submission time of each problem. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to determine the order of analysis based on the submission time of each problem.

[0080] The analysis unit can adjust the order of analysis based on the relationship between the problems during analysis. Specific evaluation criteria and methods for the relationship between the problems include, for example, analysis of causal relationships, evaluation of relevance, etc., but are not limited to such examples. Specific methods and criteria for adjusting the order include, for example, evaluation of priority, judgment of urgency, etc., but are not limited to such examples. For example, the analysis unit prioritizes analysis of problems with high relevance. In addition, the analysis unit can postpone problems with low relevance. Furthermore, the analysis unit can also moderately analyze problems with medium relevance. Thereby, the analysis unit can perform analysis in an appropriate order by adjusting the order of analysis based on the relevance between the problems. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relationship between the problems using a generation AI.

[0081] The output unit can estimate the user's emotion and adjust the report expression method based on the estimated user's emotion. Specific methods and standards for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and the like. Specific methods and standards for adjusting the expression method include, but are not limited to, changes in wording, adjustment of visual expression, and the like. For example, the output unit provides a simple and highly visible report when the user is nervous. In addition, the output unit can also provide a detailed report when the user is relaxed. Furthermore, the output unit can also provide a report with the main points when the user is in a hurry. This allows the output unit to provide a more appropriate report by adjusting the report expression method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the output unit can use a generative AI to infer a user's emotions and adjust the way the report is presented based on the inferred emotions.

[0082] The output unit can adjust the details of the report based on the importance of the problem when generating the report. Specific evaluation criteria and methods for the importance of the problem include, for example, evaluation of the impact level, judgment of the urgency, etc., but are not limited to such examples. Specific methods and criteria for adjusting the details include, for example, the depth of the report, the detail level of the information, etc., but are not limited to such examples. For example, the output unit generates a detailed report for a problem with a high importance level. In addition, the output unit can also generate a simple report for a problem with a low importance level. Furthermore, the output unit can also generate a report with an appropriate degree of detail for a problem with a medium importance level. Thereby, the output unit can provide an appropriate report by adjusting the detail level of the report based on the importance of the problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can adjust the details of the report based on the importance of the problem using a generation AI.

[0083] The output unit can apply a report generation algorithm according to the category of the problem when generating the report. Specific classification methods and standards for the categories of the problems include, for example, technical problems, psychological problems, etc., but are not limited to such examples. Specific types and implementation methods of the report generation algorithm include, for example, template-based generation, data-driven generation, etc., but are not limited to such examples. For example, the output unit applies a time management report generation algorithm to a problem related to schedule management. In addition, the output unit can also apply a communication report generation algorithm to a problem related to human relationships. Furthermore, the output unit can also apply a health data report generation algorithm to a problem related to health. Thereby, the output unit can provide a more appropriate report by applying different report generation algorithms according to the category of the problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can apply a report generation algorithm according to the category of the problem using a generation AI.

[0084] The output unit can estimate the user's emotion and adjust the length of the report based on the estimated user's emotion. Specific methods and criteria for estimating the emotion include, but are not limited to, facial expression recognition, voice analysis, and the like. Specific methods and criteria for adjusting the length include, but are not limited to, the level of detail of the report, the amount of information, and the like. For example, the output unit generates a short and to the point report when the user is in a hurry. In addition, the output unit can also generate a detailed report when the user is relaxed. Furthermore, the output unit can also generate a visually stimulating report when the user is excited. This allows the output unit to provide a more appropriate report by adjusting the length of the report based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the output unit can use generative AI to infer a user's emotions and adjust the length of the report based on the inferred emotions.

[0085] The output unit can determine the order of the reports based on the submission time of each problem when generating the reports. Specific evaluation criteria and methods for the submission time include, for example, submission date and time, submission frequency, etc., but are not limited to such examples. Specific methods and criteria for determining the order include, for example, evaluation of priority, judgment of urgency, etc., but are not limited to such examples. The output unit, for example, generates reports preferentially for recently submitted problems. In addition, the output unit can also generate reports later for problems submitted earlier. Furthermore, the output unit can also generate reports appropriately for problems submitted at a medium time. In this way, the output unit can generate reports in an appropriate order by determining the priority of reports based on the submission time of each problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can determine the order of reports based on the submission time of each problem using a generation AI.

[0086] The output unit can adjust the order of the reports based on the relationship with each problem when generating the report. Specific evaluation criteria and methods for the relationship with each problem include, for example, analysis of causal relationship, evaluation of relevance, etc., but are not limited to such examples. Specific methods and criteria for adjusting the order include, for example, evaluation of priority, judgment of urgency, etc., but are not limited to such examples. For example, the output unit generates a report preferentially for a problem with high relevance. In addition, the output unit can also generate a report later for a problem with low relevance. Furthermore, the output unit can generate a report appropriately for a problem with medium relevance. Thereby, the output unit can generate a report in an appropriate order by adjusting the order of the reports based on the relevance of each problem. Part or all of the above-mentioned processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can adjust the order of the reports based on the relationship with each problem using a generation AI.

[0087] The identification unit can estimate the user's emotion and adjust the method of identifying the background and cause of the trouble based on the estimated user's emotion. Specific methods and standards for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. Specific methods and standards for adjusting the identification method include, but are not limited to, algorithm selection and analysis depth. For example, when the user is nervous, the identification unit provides a simple and highly visible identification method. Furthermore, when the user is relaxed, the identification unit can also provide a detailed identification method. Furthermore, when the user is in a hurry, the identification unit can also provide a key point identification method. This allows the identification unit to provide a more appropriate identification result by adjusting the identification method based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can use the generation AI to infer the user's emotions and adjust the method of identifying the background and cause of the problem based on the inferred emotions.

[0088] When identifying the background or cause of a problem, the identification unit can select an appropriate identification method based on the user's past behavior history. Specific contents and analysis methods of the past behavior history include, for example, past behavior data, frequency analysis, etc., but are not limited to such examples. Specific contents and selection criteria of the appropriate identification method include, for example, data analysis methods, heuristic approaches, etc., but are not limited to such examples. The identification unit, for example, identifies the background or cause based on problems that the user has frequently experienced in the past. In addition, the identification unit can also analyze the user's past behavior patterns and identify the cause of the problem. Furthermore, the identification unit can identify the cause of the problem related to a specific time period or situation from the user's past behavior history. Thereby, the identification unit can select an optimal identification method by referring to the user's past behavior history. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the user's past behavior history and select an appropriate identification method using a generation AI.

[0089] The identification unit can adjust the specific means based on the user's current living situation when identifying the background or cause of the trouble. Specific contents and evaluation criteria of the current living situation include, for example, daily activity patterns, living environment, etc., but are not limited to such examples. Specific methods and criteria for adjusting the specific means include, for example, selection of an algorithm, depth of analysis, etc., but are not limited to such examples. For example, when the user is in a current work environment, the identification unit identifies the cause of the trouble related to the environment. In addition, when the user is spending time at home, the identification unit can also identify the cause of the trouble related to the home. Furthermore, when the user is interested in a hobby, the identification unit can also identify the cause of the trouble related to the hobby. In this way, the identification unit can provide a more appropriate identification result by customizing the specific means based on the user's current living situation. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the user's current living situation and adjust the specific means using a generation AI.

[0090] The identification unit can estimate the user's emotion and determine the priority of identifying the background and cause of the trouble based on the estimated user's emotion. Specific methods and standards for estimating the emotion include, but are not limited to, facial expression recognition and voice analysis. Specific methods and standards for determining the priority include, but are not limited to, importance evaluation and urgency judgment. For example, when the user is stressed, the identification unit preferentially identifies the cause of the trouble that reduces stress. In addition, when the user is relaxed, the identification unit can also preferentially identify the cause of the long-term trouble. Furthermore, when the user is in a hurry, the identification unit can also preferentially identify the cause of the short-term trouble. In this way, the identification unit can preferentially identify the cause of the more important trouble by determining the priority of identification based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the identification unit may use the generation AI to estimate the user's emotion and determine the priority of identifying the background and cause of the trouble based on the estimated emotion.

[0091] When identifying the background or cause of a problem, the identification unit can select an appropriate identification method based on the geographical location information of the user. Specific content and acquisition method of the geographical location information include, for example, GPS data, location information services, etc., but are not limited to such examples. For example, when the user is in a specific area, the identification unit identifies the cause of the problem related to the area. In addition, when the user is traveling, the identification unit can also identify the cause of the travel-related problem. Furthermore, when the user is at home, the identification unit can also identify the cause of the home-related problem. In this way, the identification unit can select an optimal identification method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the geographical location information of the user using a generation AI and select an appropriate identification method.

[0092] When identifying the background or cause of a problem, the identification unit can analyze the user's social media activity and propose a measure. Specific content and analysis method of the social media activity include, for example, the content of posts, the reaction of followers, etc., but are not limited to such examples. The identification unit, for example, identifies the cause of the problem that the user frequently mentions on social media. The identification unit can also identify the cause of the problem related to a topic that the user follows on social media. Furthermore, the identification unit can also identify the cause of the problem related to a group that the user participates in on social media. In this way, the identification unit can propose a specific measure by analyzing the user's social media activity. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can analyze the user's social media activity and propose a specific measure using a generation AI.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can analyze the user's past behavioral patterns and customize the input method for the problem. For example, if the user has preferred text input in the past, text input can be preferentially suggested. Also, if the user has used voice input in the past, voice input can be preferentially suggested. Furthermore, if the user has input a problem during a specific time period in the past, input during that time period can be prompted. In this way, the reception unit can suggest the optimal input method by analyzing the user's past behavioral patterns.

[0095] The analysis unit can adjust the level of detail of the analysis based on the user's living environment. For example, when the user is at work, work-related problems are analyzed with priority. Also, when the user is spending time at home, home-related problems can be analyzed with priority. Furthermore, when the user is interested in a hobby, problems related to the hobby can be analyzed with priority. In this way, the analysis unit can provide more relevant analysis results by adjusting the level of detail of the analysis based on the user's living environment.

[0096] The output unit can estimate the user's emotions and adjust the way in which the report is presented based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible report can be provided. If the user is relaxed, a detailed report can be provided. Furthermore, if the user is in a hurry, a report that focuses on the main points can be provided. In this way, the output unit can provide a more appropriate report by adjusting the way in which the report is presented based on the user's emotions.

[0097] The identification unit can estimate the user's emotions and adjust the method of identifying the background and cause of the trouble based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible identification method can be provided. If the user is relaxed, a detailed identification method can be provided. Furthermore, if the user is in a hurry, a method that focuses on the main points can be provided. In this way, the identification unit can provide a more appropriate identification result by adjusting the identification method based on the user's emotions.

[0098] The reception unit can preferentially input a problem that is highly relevant based on the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially inputs a problem related to the area. When the user is traveling, the reception unit can preferentially input a problem related to the travel. When the user is at home, the reception unit can preferentially input a problem related to the home. This allows the reception unit to preferentially input a problem that is highly relevant in consideration of the geographical location information of the user.

[0099] The analysis unit can adjust the details of the analysis based on the importance of the problem. For example, a detailed analysis can be performed for a problem with a high importance. Also, a simplified analysis can be performed for a problem with a low importance. Furthermore, an analysis with an appropriate level of detail can be performed for a problem with a medium importance. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the problem.

[0100] When generating a report, the output unit can apply a report generation algorithm according to the category of the problem. For example, a time management report generation algorithm can be applied to a problem related to schedule management. A communication report generation algorithm can also be applied to a problem related to human relationships. Furthermore, a health data report generation algorithm can be applied to a problem related to health. In this way, the output unit can provide a more appropriate report by applying different report generation algorithms according to the category of the problem.

[0101] When identifying the background or cause of a problem, the identification unit can select an appropriate identification method based on the user's past behavior history. For example, the identification unit can identify the background or cause based on problems that the user has frequently experienced in the past. The identification unit can also identify the cause of a problem by analyzing the user's past behavior patterns. Furthermore, the identification unit can identify the cause of a problem related to a specific time period or situation from the user's past behavior history. This allows the identification unit to select the optimal identification method by referring to the user's past behavior history.

[0102] The reception unit can estimate the user's emotions and adjust the timing of inputting the problem based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input the problem during a time when the user can relax. Also, if the user is concentrating, the reception unit can prompt the user to input the problem at that time. Furthermore, if the user is tired, the reception unit can prompt the user to input the problem after resting. In this way, the reception unit can adjust the timing of inputting the problem according to the user's emotions, allowing the user to input the problem at a more appropriate time.

[0103] 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, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, an analysis result that focuses on the main points can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is presented based on the user's emotions.

[0104] The process flow of the second embodiment will be briefly described below.

[0105] Step 1: The reception unit receives input of a problem from a user. Problems from users include technical problems and problems in daily life. The reception unit can receive problems by text input, voice input, image input, and other methods. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and delve deeper into the user's characteristics. Specific methods include analyzing behavioral patterns and evaluating psychological characteristics. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to identify the background and causes of the user's problems. Step 3: The output unit organizes the characteristics obtained by the analysis unit and outputs them as a report. The content and format of the report can be in text or graph format, and includes the user's characteristics, causes of problems, and improvement measures. The output unit can also generate reports using a generative AI model.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0108] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and output unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives a problem from the user by text input or voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to delve into the characteristics of the user. The output unit is realized, for example, by the output device 40 of the smart device 14, and provides the analysis result to the user as a report. The analysis unit and the output unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0117] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and output unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a problem from the user by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to deeply analyze the characteristics of the user. The output unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the analysis result to the user by voice. The analysis unit and the output unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and output unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and receives a problem from the user by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to deeply analyze the characteristics of the user. The output unit is realized, for example, by the display 343 of the headset type terminal 314, and visually provides the analysis result to the user. The analysis unit and the output unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and receives a problem from the user by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to deeply analyze the characteristics of the user. The output unit is realized, for example, by the speaker 240 of the robot 414, and provides the analysis result to the user by voice. The analysis unit and the output unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0160] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0164] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0166] 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.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0169] 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.

[0170] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0171] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0175] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0177] (Appendix 1) A reception unit that receives input of a problem from a user; an analysis unit that analyzes the information received by the reception unit and analyzes the characteristics of the user; and an output unit that organizes the characteristics obtained by the analysis unit and outputs the characteristics as a report. A system characterized by: (Appendix 2) Equipped with an identification unit that uses the generative AI's learning method or algorithm to identify the background and cause of the user's problem 2. The system of claim 1, (Appendix 3) The output unit is Generate reports that include user characteristics, causes of problems, and solutions 2. The system of claim 1, (Appendix 4) The output unit is Suggest improvements based on user characteristics 2. The system of claim 1, (Appendix 5) The reception unit is Estimate the user's emotions and adjust the timing of inputting the problem based on the estimated user emotions 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past problem input history and select the appropriate input method 2. The system of claim 1, (Appendix 7) The reception unit is When entering a problem, filtering is performed based on the user's current life situation or areas of interest 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and prioritize the problems to be input based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is When entering a problem, prioritize relevant problems based on the user's geographic location 2. The system of claim 1, (Appendix 10) The reception unit is When a user enters a problem, the system analyzes the user's social media activity and prompts the user to enter the problem. 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the details of the analysis based on the importance of the problem 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, analysis algorithms are applied according to the problem category 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the order of analysis is determined based on when the problem was submitted. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the order of analysis based on the relationship with the problem 2. The system of claim 1, (Appendix 17) The output unit is Infer user sentiment and adjust report presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The output unit is When generating a report, adjust the report details based on the severity of the issue 2. The system of claim 1, (Appendix 19) The output unit is When generating a report, apply the report generation algorithm according to the problem category 2. The system of claim 1, (Appendix 20) The output unit is Infer user sentiment and adjust report length based on estimated user sentiment 2. The system of claim 1, (Appendix 21) The output unit is When generating reports, order the reports based on when the issues were submitted 2. The system of claim 1, (Appendix 22) The output unit is When generating reports, order the reports based on their relevance to the problem 2. The system of claim 1, (Appendix 23) The identification unit is Infer the user's emotions and adjust the method of identifying the background and cause of the problem based on the inferred user emotions. 3. The system of claim 2, (Appendix 24) The identification unit is When identifying the background and cause of a problem, select an appropriate identification method based on the user's past behavior history. 3. The system of claim 2, (Appendix 25) The identification unit is When identifying the context and causes of a problem, tailor specific measures based on the user’s current life situation. 3. The system of claim 2, (Appendix 26) The identification unit is Infer the user's emotions and prioritize identifying the background and causes of the problem based on the inferred user emotions. 3. The system of claim 2, (Appendix 27) The identification unit is When identifying the background and cause of a problem, select an appropriate identification method based on the user's geographic location information. 3. The system of claim 2, (Appendix 28) The identification unit is When identifying the background and causes of a problem, analyze users' social media activity and suggest solutions 3. The system of claim 2, [Explanation of symbols]

[0178] 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 reception unit that receives input of a problem from a user; an analysis unit that analyzes the information received by the reception unit and analyzes the characteristics of the user; and an output unit that organizes the characteristics obtained by the analysis unit and outputs the characteristics as a report. A system characterized by:

2. The system is provided with an identification unit that uses the learning method or algorithm of the generation AI to identify the background and cause of the user's problem.

2. The system of claim 1 .

3. The output unit is Generate reports that include user characteristics, causes of problems, and solutions 2. The system of claim 1 .

4. The output unit is Suggest improvements based on user characteristics 2. The system of claim 1 .

5. The reception unit is Estimate the user's emotions and adjust the timing of inputting the problem based on the estimated user emotions 2. The system of claim 1 .

6. The reception unit is Analyze the user's past problem input history and select the appropriate input method 2. The system of claim 1 .

7. The reception unit is When entering a problem, filtering is performed based on the user's current life situation or areas of interest 2. The system of claim 1 .

8. The reception unit is Estimate the user's emotions and prioritize the problems to be input based on the estimated user emotions.

2. The system of claim 1 .

9. The reception unit is When entering a problem, prioritize relevant problems based on the user's geographic location 2. The system of claim 1 .

Images