System

Abstract

To provide a system that provides objective and dispassionate solutions to problems or tasks.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of a problem or a task. The analysis unit analyzes the information received by the reception unit. The generation unit generates a solution from a specific perspective based on the information analyzed by the analysis unit. The provision unit provides the solution generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques have the disadvantage that a human perspective is influential in solving problems and issues, making it difficult to arrive at a calm and objective solution.

[0005] The system according to the embodiment aims to provide objective and rational solutions to problems and issues. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives input of a problem or issue. The analysis unit analyzes the information received by the receiving unit. The generation unit generates a solution from a specific perspective based on the information analyzed by the analysis unit. The provision unit provides the solution generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can provide objective and dispassionate solutions to problems and issues. [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 a 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 advice providing system according to the embodiment of the present invention is a system that provides advice to humanity from the perspective of "motherly protection and peace" rather than "fatherly attack and development". In this advice providing system, a user inputs a problem or issue that the user wants to solve, the system analyzes the problem or issue, and generates a solution from a motherly perspective. In this case, the system derives a solution using a simple logic that eliminates human perspectives and ties. The generated solution is provided to the user and encourages calm judgment. For example, a user inputs a problem or issue that the user wants to solve. For example, various problems such as international conflicts, regional conflicts, and conflicts between companies are targeted. This information is input into the system. Next, the system analyzes the input information. The system is designed to avoid aggressive elements and conflicts in order to generate a solution from a motherly perspective. For example, a solution that promotes dialogue, cooperation, and mutual understanding is generated. The generated solution is provided to the user. The user can make a calm judgment by referring to the provided solution. For example, in the case of an international conflict, this includes peace negotiations through dialogue and proposals for joint projects. This system aims to make people happy through the information revolution. In addition, by connecting with a large number of unicorn services around the world, it is possible to support the process of realization. For example, by collaborating with investment funds, it can support feasible projects. This allows the advice provision system to help users make calm decisions and provide peaceful solutions.

[0029] The advice providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a problem or issue that a user wants to solve. For example, various problems such as international conflicts, regional conflicts, and conflicts between companies are targets. The reception unit can receive the problem or issue by, for example, a method such as text input, voice input, or image input. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes text data by, for example, natural language processing technology. The analysis unit can also analyze image data by image analysis technology. Furthermore, the analysis unit can analyze voice data by voice analysis technology. The generation unit generates a solution from a maternal perspective based on the information analyzed by the analysis unit. The generation unit generates a solution by, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI. The generation unit generates a solution that promotes, for example, dialogue, cooperation, and mutual understanding. The provision unit provides the solution generated by the generation unit to the user. The provision unit can provide the solution by, for example, an email, a dashboard, a report, or the like. In this way, the advice providing system according to the embodiment can support the user in making a calm decision and provide an amicable solution.

[0030] The reception unit inputs the problems and issues that the user wants to solve. For example, various problems such as international conflicts, regional conflicts, and conflicts between companies are targeted. The reception unit can accept problems and issues by, for example, text input, voice input, image input, and other methods. Specifically, the user can describe the problem or issue in detail through a dedicated interface. In the case of text input, the user inputs the details of the problem using a keyboard, and in the case of voice input, the user explains the problem by voice through a microphone. The voice input is converted into text data using voice recognition technology. In the case of image input, the user uploads related images and diagrams, and these visual data are also subject to analysis. The reception unit centrally manages these input data and checks the consistency and completeness of the data before sending it to the analysis unit. For example, if the input text is incomplete or the voice data contains noise, the reception unit can provide feedback to encourage the user to re-enter. Furthermore, the reception unit encrypts the user's input data and takes security measures to ensure privacy protection. As a result, the reception unit provides an environment in which the user can input problems and issues with peace of mind, thereby improving the reliability of the entire system.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text data using, for example, natural language processing technology. Specifically, the natural language processing technology is used to tokenize the text data input by the user and perform grammatical analysis and semantic analysis. This makes it possible to understand the content of the text data and grasp the essence of the problem. The analysis unit can also analyze image data using image analysis technology. The image analysis technology uses an image recognition algorithm to identify objects and scenes in an image and extract related information. For example, image data such as a map of a conflict area or a company logo can be analyzed to grasp the background information of the problem. Furthermore, the analysis unit can also analyze voice data using voice analysis technology. The voice analysis technology uses a voice recognition algorithm to convert voice data into text data and analyze the content. This makes it possible to accurately understand the problems and issues explained by the user through voice. The analysis unit combines these analysis technologies to analyze the information provided by the user from various angles and grasp the overall picture of the problem. Furthermore, the analysis unit can refer to past data and related literature to identify the background of the problem and related factors. This allows the analysis unit to accurately grasp the essence of the problem based on the information provided by the user and provide the information necessary to generate a solution.

[0032] The generation unit generates a solution from a parental perspective based on the information analyzed by the analysis unit. The generation unit generates a solution using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI. Specifically, the generation AI generates a solution to the problem based on the information provided by the analysis unit. The text generation AI uses natural language generation technology to generate sentences that propose specific solutions to the user. For example, in the case of an international conflict, a sentence can be generated that includes specific steps and suggestions to promote dialogue and cooperation. The multimodal generation AI utilizes not only text data but also image and audio data to generate a more comprehensive solution. For example, in the case of a conflict between companies, visual data can be used to visually show the cause of the conflict and the solution. The generation unit can use these generation AIs to provide the user with a solution from a multilateral perspective. Furthermore, the generation unit evaluates whether the generated solution is realistic and feasible, and makes corrections as necessary. For example, if the generated solution is not feasible, the generation unit uses the generation AI again to generate a more realistic solution. This allows the generation unit to provide the user with a feasible and effective solution.

[0033] The providing unit provides the solution generated by the generating unit to the user. The providing unit can provide the solution by, for example, email, dashboard, report, or other methods. Specifically, the providing unit provides a means for quickly and effectively communicating the generated solution to the user. When email is used, an email describing the generated solution in detail is sent to the user. When a dashboard is used, the user can check the generated solution in real time through a dedicated web interface. When a report is used, a document describing the generated solution in detail is created and provided to the user. The providing unit can combine these means to provide the solution to the user in an optimal manner. Furthermore, the providing unit can collect feedback from the user and evaluate the effectiveness of the generated solution. For example, the user can report the results of implementing the provided solution, and the solution can be improved based on the results. The providing unit can maximize the effectiveness of the solution and improve the user's satisfaction through communication with the user. This allows the providing unit to quickly and effectively provide the solution to the user and assist in solving the problem.

[0034] The analysis unit may include a filtering unit for eliminating a human perspective. The filtering unit may use, for example, an algorithm for eliminating emotional bias or personal opinions. For example, the filtering unit may use natural language processing technology to extract emotional expressions from text data and eliminate them. The filtering unit may also use image analysis technology to eliminate emotional elements from image data. Furthermore, the filtering unit may also use voice analysis technology to eliminate emotional tones from voice data. This allows the analysis unit to perform a more objective analysis by eliminating a human perspective. Some or all of the above-mentioned processing in the filtering unit may be performed, for example, using AI or may be performed without using AI. For example, the filtering unit may input text data to a generation AI and cause the generation AI to perform the elimination of emotional expressions.

[0035] The generating unit may include a generating algorithm unit for generating a specific solution. The generating algorithm unit may generate a solution using, for example, a technology such as a genetic algorithm or a neural network. For example, the generating algorithm unit may generate a plurality of solutions using a genetic algorithm and select an optimal solution from among the solutions. The generating algorithm unit may also generate a solution based on input data using a neural network. Furthermore, the generating algorithm unit may also optimize the solution generation process using reinforcement learning. This allows the generating unit to provide practical advice by generating a specific solution. A part or all of the above-mentioned processing in the generating algorithm unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating algorithm unit may input input data to the generating AI and cause the generating AI to generate a solution.

[0036] The providing unit can provide the generated solution to the user. The providing unit can provide the solution by, for example, email, dashboard, report, or other methods. For example, the providing unit sends the generated solution to the user by email. The providing unit can also display the solution in real time through a dashboard. Furthermore, the providing unit can provide the solution in a report format so that the user can check detailed information. In this way, the providing unit can provide the generated solution to the user, allowing the user to make a calm decision. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated solution to the generating AI and cause the generating AI to execute a method for providing the solution.

[0037] The reception unit can receive problems and issues such as international or regional conflicts and conflicts between companies. The reception unit can receive problems and issues such as, for example, international conflicts, regional conflicts, and conflicts between companies. For example, the reception unit receives information on international conflicts by text input. The reception unit can also receive information on regional conflicts by voice input. Furthermore, the reception unit can also receive information on conflicts between companies by image input. This allows the reception unit to receive a variety of problems and issues, making it possible to use the reception unit in a wide range of fields. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input information on international conflicts to the generation AI and cause the generation AI to receive the information.

[0038] The generation unit can generate a solution that promotes dialogue, cooperation, and mutual understanding. The generation unit can generate a solution that promotes dialogue, for example. For example, the generation unit generates a proposal for peace negotiations through dialogue. The generation unit can also generate a solution that promotes cooperation. For example, the generation unit generates a proposal for a joint project. Furthermore, the generation unit can generate a solution that promotes mutual understanding. For example, the generation unit generates a proposal for cultural exchange. In this way, the generation unit can provide a peaceful solution by promoting dialogue, cooperation, and mutual understanding. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input a solution that promotes dialogue into the generation AI and cause the generation AI to execute the generation of a solution.

[0039] The reception unit can analyze the input history of the user's past problems and tasks and select the optimal input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, the reception unit analyzes the user's past input history, and if the user has used voice input a lot, preferentially suggests voice input. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. For example, if the user has used text input a lot at night, the reception unit suggests text input at night. Furthermore, the reception unit can analyze the contents of problems and tasks that the user has input in the past and suggest the optimal input method for similar problems and tasks. For example, if the user has input technical questions a lot by voice input in the past, the reception unit suggests voice input for technical questions. In this way, the reception unit can provide the user with the optimal input method by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0040] The reception unit can filter the problems and tasks based on the user's current situation and field of interest when inputting the problems and tasks. The reception unit, for example, preferentially inputs problems and tasks related to the field in which the user is currently interested. For example, the reception unit identifies the user's current field of interest from a questionnaire result or past behavior history, and proposes related problems and tasks. The reception unit can also propose appropriate problems and tasks based on the user's current situation (for example, at work, on vacation, etc.). For example, when the user is at work, the reception unit proposes work-related problems and tasks. Furthermore, the reception unit can analyze the user's past behavior history, and filter and input problems and tasks related to the current field of interest. For example, the reception unit preferentially proposes problems and tasks related to the field in which the user has shown a lot of interest in the past. As a result, the reception unit can input more relevant problems and tasks by filtering based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0041] When inputting a problem or task, the reception unit can preferentially input a problem or task that is highly relevant in consideration of the geographical location information of the user. The reception unit, for example, preferentially inputs a problem or task related to the area where the user is currently located. For example, the reception unit acquires the geographical location information of the user from GPS data or an IP address, and proposes a problem or task related to the current area. The reception unit can also propose a problem or task related to an area that the user has visited in the past. For example, the reception unit analyzes the user's past behavior history, and proposes a problem or task related to an area that the user has visited in the past. Furthermore, the reception unit can predict and input a problem or task related to an area that the user plans to visit in the future. For example, the reception unit acquires the user's schedule from a calendar app, and proposes a problem or task related to an area that the user plans to visit in the future. As a result, the reception unit can input a problem or task that is more relevant by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographic location data into the generation AI and cause the generation AI to suggest highly relevant problems and issues.

[0042] The reception unit can analyze the user's social media activity when inputting a problem or a task, and input a related problem or task. The reception unit, for example, inputs a problem or task related to a topic in which the user is interested on social media. For example, the reception unit analyzes the content of the user's social media postings, and proposes a problem or task related to the topic in which the user is interested. The reception unit can also propose a problem or task related to a topic in which the user's friends or followers on social media are interested. For example, the reception unit analyzes the content of the user's friends or followers' posts, and proposes a related problem or task. Furthermore, the reception unit can analyze the user's social media activity history, and input a related problem or task. For example, the reception unit analyzes the user's past posting content and the number of likes, and proposes a related problem or task. In this way, the reception unit can input a more relevant problem or task by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI, and cause the generation AI to execute the proposal of a related problem or task.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the problem or issue during the analysis. The analysis unit, for example, performs a detailed analysis on problems or issues with high importance. For example, the analysis unit evaluates the impact range and risk level of the problem or issue, and performs a detailed analysis on those with high importance. The analysis unit can also perform a simplified analysis on problems or issues with low importance. For example, the analysis unit performs a simplified analysis on problems or issues with low impact range and risk level. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on problems or issues with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on problems or issues with medium impact range and risk level. As a result, the analysis unit can perform a more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the problem or issue. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input importance data of the problem or issue to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the problem or issue during analysis. For example, the analysis unit applies an algorithm that promotes dialogue and cooperation to an international conflict. For example, the analysis unit analyzes data related to an international conflict and applies an algorithm for promoting dialogue and cooperation. The analysis unit can also apply an algorithm that promotes mutual understanding to a regional conflict. For example, the analysis unit analyzes data related to a regional conflict and applies an algorithm for promoting mutual understanding. Furthermore, the analysis unit can apply an algorithm that proposes a joint project to a conflict between companies. For example, the analysis unit analyzes data related to a conflict between companies and applies an algorithm for proposing a joint project. As a result, the analysis unit can perform a more appropriate analysis by applying different analysis algorithms depending on the category of the problem or issue. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the problem or issue to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] During the analysis, the analysis unit can determine the priority of the analysis based on the submission time of the problem or assignment. The analysis unit, for example, prioritizes the analysis of problems or assignments with high urgency. For example, the analysis unit evaluates the submission date and submission time of the problem or assignment, and prioritizes the analysis of those with high urgency. The analysis unit can also prioritize the analysis of problems or assignments with a close submission time. For example, the analysis unit prioritizes the analysis of problems or assignments with a close submission deadline. Furthermore, the analysis unit can also postpone the analysis of problems or assignments with a distant submission time. For example, the analysis unit postpones the analysis of problems or assignments with a distant submission deadline. As a result, the analysis unit can perform the analysis in a more appropriate order by determining the priority of the analysis based on the submission time of the problem or assignment. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the problem or assignment to the generation AI, and cause the generation AI to execute the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the problems and tasks during analysis. The analysis unit, for example, prioritizes the analysis of highly relevant problems and tasks. For example, the analysis unit evaluates common themes and related keywords of the problems and tasks, and prioritizes the analysis of those with high relevance. The analysis unit can also postpone the analysis of low-relevance problems and tasks. For example, the analysis unit postpones the analysis of low-relevance problems and tasks. Furthermore, the analysis unit can also moderately analyze problems and tasks with a medium degree of relevance. For example, the analysis unit moderately analyzes problems and tasks with a medium degree of relevance. As a result, the analysis unit can perform analysis in a more appropriate order by adjusting the order of analysis based on the relevance of the problems and tasks. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input relevance data of problems and tasks to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The generation unit can improve the accuracy of generation by considering the interrelationships between problems and issues when generating a solution. The generation unit, for example, generates a method for solving mutually related problems and issues collectively. For example, the generation unit evaluates the causal relationship and correlation between problems and issues, and generates a method for solving the mutually related ones collectively. The generation unit can also generate an optimal solution by considering problems and issues that affect each other. For example, the generation unit evaluates the impact range and risk level of problems and issues, and generates an optimal solution by considering those that affect each other. Furthermore, the generation unit can generate an effective solution by combining problems and issues that complement each other. For example, the generation unit evaluates the complementary relationship between problems and issues, and generates an effective solution by combining those that complement each other. As a result, the generation unit can generate a more accurate solution by considering the interrelationships between problems and issues. A part or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input interrelationship data between problems and issues to the generation AI, and cause the generation AI to generate a solution.

[0048] The generation unit can generate a solution by taking into consideration attribute information of the submitter of the problem or task when generating the solution. The generation unit generates an appropriate solution based on, for example, the expertise and experience of the submitter. For example, the generation unit evaluates the expertise and experience of the submitter and generates an appropriate solution based on the expertise and experience. The generation unit can also generate an appropriate solution by taking into consideration the cultural background and values ​​of the submitter. For example, the generation unit evaluates the cultural background and values ​​of the submitter and generates an appropriate solution based on the evaluation. Furthermore, the generation unit can analyze the past behavior history of the submitter and generate an optimal solution. For example, the generation unit analyzes the past behavior history of the submitter and generates an optimal solution based on the evaluation. In this way, the generation unit can generate a more appropriate solution by taking into consideration the attribute information of the submitter. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input attribute information data of the submitter to the generation AI and cause the generation AI to generate a solution.

[0049] The generation unit can generate solutions while taking into consideration the geographical distribution of problems and issues. The generation unit, for example, generates a method for preferentially solving geographically close problems and issues. For example, the generation unit evaluates the geographical distribution of problems and issues and generates a method for preferentially solving geographically close problems and issues. The generation unit can also generate a method for solving geographically distant problems and issues by postponing their resolution. For example, the generation unit generates a method for solving geographically distant problems and issues by postponing their resolution. Furthermore, the generation unit can also generate a method for solving geographically related problems and issues collectively. For example, the generation unit evaluates geographically related problems and issues and generates a method for solving them collectively. In this way, the generation unit can generate more appropriate solutions by taking into consideration the geographical distribution of problems and issues. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input geographical distribution data of problems and issues to the generation AI and cause the generation AI to generate solutions.

[0050] The generation unit can improve the accuracy of generation by referring to related literature of the problem or issue when generating a solution. The generation unit generates an optimal solution based on, for example, related literature. For example, the generation unit refers to academic papers and patent documents related to the problem or issue, and generates an optimal solution based on the reference. The generation unit can also improve the accuracy of the solution by referring to related literature. For example, the generation unit refers to technical reports and industry reports, and improves the accuracy of the solution based on the reference. Furthermore, the generation unit can analyze related literature and generate an effective solution. For example, the generation unit analyzes related literature using text analysis technology, and generates an effective solution based on the analysis. As a result, the generation unit can generate a more accurate solution by referring to the related literature. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input related literature data to the generation AI and cause the generation AI to generate a solution.

[0051] When providing a solution, the providing unit can select the optimal providing method by referring to the user's past solution usage history. The providing unit, for example, preferentially proposes a solution providing method that the user has used in the past. For example, the providing unit analyzes the user's past solution usage history and preferentially proposes the providing method that was used in the past. The providing unit can also propose a providing method suitable for a specific time period from the user's past usage history. For example, the providing unit proposes a providing method that the user used a lot at night. Furthermore, the providing unit can analyze the user's past usage history and select the optimal providing method. For example, the providing unit analyzes the user's past usage history and selects the optimal providing method based on the analysis. As a result, the providing unit can select the optimal providing method for the user by referring to the past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past solution usage history data to the generating AI and cause the generating AI to select the optimal providing method.

[0052] The providing unit can select the optimal providing method in consideration of the device information of the user when providing a solution. For example, when the user is using a smartphone, the providing unit provides a providing method that matches the screen size. For example, the providing unit acquires the device information of the user and provides a providing method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the providing unit can also provide a providing method optimized for a large screen. For example, the providing unit provides a providing method optimized for the screen size of the tablet. Furthermore, when the user is using a smartwatch, the providing unit can also provide a simple providing method with high visibility. For example, the providing unit provides a providing method optimized for the screen size of the smartwatch. Thereby, the providing unit can provide a solution in a more appropriate manner by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it preferentially suggests input methods (such as voice and text) that the user has used frequently in the past. The reception unit can also predict and suggest an input method to be used during a specific time period from the user's past input history. Furthermore, the reception unit can analyze the contents of questions and tasks that the user has entered in the past and suggest the optimal input method for similar questions and tasks. In this way, the reception unit can provide the user with the optimal input method by analyzing the past input history.

[0055] The generation unit can improve the accuracy of solution generation by taking into account the interrelationships between problems and issues. For example, it can generate a method for solving interrelated problems and issues collectively. It can also generate optimal solutions by taking into account problems and issues that affect each other. Furthermore, it can generate effective solutions by combining problems and issues that complement each other. In this way, the generation unit can generate more accurate solutions by taking into account the interrelationships between problems and issues.

[0056] The reception unit can preferentially input highly relevant questions and tasks in consideration of the geographical location information of the user. For example, questions and tasks related to the area where the user is currently located can be preferentially input. In addition, questions and tasks related to areas the user has visited in the past can be suggested. Furthermore, questions and tasks related to areas the user plans to visit in the future can be predicted and input. In this way, the reception unit can input more relevant questions and tasks by considering the geographical location information of the user.

[0057] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the problem or issue. For example, a detailed analysis can be performed for problems or issues of high importance. Also, a simplified analysis can be performed for problems or issues of low importance. Furthermore, an analysis with an appropriate level of detail can be performed for problems or issues of medium importance. In this way, the analysis unit can perform a more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the problem or issue.

[0058] When providing a solution, the providing unit can select the optimal providing method by taking into consideration the device information of the user. For example, if the user is using a smartphone, a providing method that matches the screen size can be provided. Also, if the user is using a tablet, a providing method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a providing method that is simple and highly visible can be provided. In this way, the providing unit can provide a solution in a more appropriate manner by taking into consideration the device information of the user.

[0059] During analysis, the analysis unit can apply different analysis algorithms depending on the category of problem or issue. For example, for international conflicts, an algorithm that promotes dialogue and cooperation can be applied. For regional conflicts, an algorithm that promotes mutual understanding can be applied. Furthermore, for conflicts between companies, an algorithm that proposes joint projects can be applied. In this way, the analysis unit can perform more appropriate analysis by applying different analysis algorithms depending on the category of problem or issue.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit inputs the problem or issue that the user wants to solve. For example, various issues are targeted, such as international disputes, regional disputes, and conflicts between companies. The reception unit can accept problems or issues by text input, voice input, image input, and other methods. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze text data using natural language processing technology, image data using image analysis technology, and voice data using voice analysis technology. Step 3: The generator generates solutions from a specific perspective based on the information analyzed by the analyzer. The generator uses a generative AI to generate solutions, for example, solutions that promote dialogue, cooperation, and mutual understanding. Step 4: The providing unit provides the solution generated by the generating unit to the user. The providing unit can provide the solution by email, dashboard, report, or other methods.

[0062] (Example 2) The advice providing system according to the embodiment of the present invention is a system that provides advice to humanity from the perspective of "motherly protection and peace" rather than "fatherly attack and development". In this advice providing system, a user inputs a problem or issue that the user wants to solve, the system analyzes the problem or issue, and generates a solution from a motherly perspective. In this case, the system derives a solution using a simple logic that eliminates human perspectives and ties. The generated solution is provided to the user and encourages calm judgment. For example, a user inputs a problem or issue that the user wants to solve. For example, various problems such as international conflicts, regional conflicts, and conflicts between companies are targeted. This information is input into the system. Next, the system analyzes the input information. The system is designed to avoid aggressive elements and conflicts in order to generate a solution from a motherly perspective. For example, a solution that promotes dialogue, cooperation, and mutual understanding is generated. The generated solution is provided to the user. The user can make a calm judgment by referring to the provided solution. For example, in the case of an international conflict, this includes peace negotiations through dialogue and proposals for joint projects. This system aims to make people happy through the information revolution. In addition, by connecting with a large number of unicorn services around the world, it is possible to support the process of realization. For example, by collaborating with investment funds, it can support feasible projects. This allows the advice provision system to help users make calm decisions and provide peaceful solutions.

[0063] The advice providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a problem or issue that a user wants to solve. For example, various problems such as international conflicts, regional conflicts, and conflicts between companies are targets. The reception unit can receive the problem or issue by, for example, a method such as text input, voice input, or image input. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes text data by, for example, natural language processing technology. The analysis unit can also analyze image data by image analysis technology. Furthermore, the analysis unit can analyze voice data by voice analysis technology. The generation unit generates a solution from a maternal perspective based on the information analyzed by the analysis unit. The generation unit generates a solution by, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI. The generation unit generates a solution that promotes, for example, dialogue, cooperation, and mutual understanding. The provision unit provides the solution generated by the generation unit to the user. The provision unit can provide the solution by, for example, an email, a dashboard, a report, or the like. In this way, the advice providing system according to the embodiment can support the user in making a calm decision and provide an amicable solution.

[0064] The reception unit inputs the problems and issues that the user wants to solve. For example, various problems such as international conflicts, regional conflicts, and conflicts between companies are targeted. The reception unit can accept problems and issues by, for example, text input, voice input, image input, and other methods. Specifically, the user can describe the problem or issue in detail through a dedicated interface. In the case of text input, the user inputs the details of the problem using a keyboard, and in the case of voice input, the user explains the problem by voice through a microphone. The voice input is converted into text data using voice recognition technology. In the case of image input, the user uploads related images and diagrams, and these visual data are also subject to analysis. The reception unit centrally manages these input data and checks the consistency and completeness of the data before sending it to the analysis unit. For example, if the input text is incomplete or the voice data contains noise, the reception unit can provide feedback to encourage the user to re-enter. Furthermore, the reception unit encrypts the user's input data and takes security measures to ensure privacy protection. As a result, the reception unit provides an environment in which the user can input problems and issues with peace of mind, thereby improving the reliability of the entire system.

[0065] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the text data using, for example, natural language processing technology. Specifically, the natural language processing technology is used to tokenize the text data input by the user and perform grammatical analysis and semantic analysis. This makes it possible to understand the content of the text data and grasp the essence of the problem. The analysis unit can also analyze image data using image analysis technology. The image analysis technology uses an image recognition algorithm to identify objects and scenes in an image and extract related information. For example, image data such as a map of a conflict area or a company logo can be analyzed to grasp the background information of the problem. Furthermore, the analysis unit can also analyze voice data using voice analysis technology. The voice analysis technology uses a voice recognition algorithm to convert voice data into text data and analyze the content. This makes it possible to accurately understand the problems and issues explained by the user through voice. The analysis unit combines these analysis technologies to analyze the information provided by the user from various angles and grasp the overall picture of the problem. Furthermore, the analysis unit can refer to past data and related literature to identify the background of the problem and related factors. This allows the analysis unit to accurately grasp the essence of the problem based on the information provided by the user and provide the information necessary to generate a solution.

[0066] The generation unit generates a solution from a parental perspective based on the information analyzed by the analysis unit. The generation unit generates a solution using, for example, a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI. Specifically, the generation AI generates a solution to the problem based on the information provided by the analysis unit. The text generation AI uses natural language generation technology to generate sentences that propose specific solutions to the user. For example, in the case of an international conflict, a sentence can be generated that includes specific steps and suggestions to promote dialogue and cooperation. The multimodal generation AI utilizes not only text data but also image and audio data to generate a more comprehensive solution. For example, in the case of a conflict between companies, visual data can be used to visually show the cause of the conflict and the solution. The generation unit can use these generation AIs to provide the user with a solution from a multilateral perspective. Furthermore, the generation unit evaluates whether the generated solution is realistic and feasible, and makes corrections as necessary. For example, if the generated solution is not feasible, the generation unit uses the generation AI again to generate a more realistic solution. This allows the generation unit to provide the user with a feasible and effective solution.

[0067] The providing unit provides the solution generated by the generating unit to the user. The providing unit can provide the solution by, for example, email, dashboard, report, or other methods. Specifically, the providing unit provides a means for quickly and effectively communicating the generated solution to the user. When email is used, an email describing the generated solution in detail is sent to the user. When a dashboard is used, the user can check the generated solution in real time through a dedicated web interface. When a report is used, a document describing the generated solution in detail is created and provided to the user. The providing unit can combine these means to provide the solution to the user in an optimal manner. Furthermore, the providing unit can collect feedback from the user and evaluate the effectiveness of the generated solution. For example, the user can report the results of implementing the provided solution, and the solution can be improved based on the results. The providing unit can maximize the effectiveness of the solution and improve the user's satisfaction through communication with the user. This allows the providing unit to quickly and effectively provide the solution to the user and assist in solving the problem.

[0068] The analysis unit may include a filtering unit for eliminating a human perspective. The filtering unit may use, for example, an algorithm for eliminating emotional bias or personal opinions. For example, the filtering unit may use natural language processing technology to extract emotional expressions from text data and eliminate them. The filtering unit may also use image analysis technology to eliminate emotional elements from image data. Furthermore, the filtering unit may also use voice analysis technology to eliminate emotional tones from voice data. This allows the analysis unit to perform a more objective analysis by eliminating a human perspective. Some or all of the above-mentioned processing in the filtering unit may be performed, for example, using AI or may be performed without using AI. For example, the filtering unit may input text data to a generation AI and cause the generation AI to perform the elimination of emotional expressions.

[0069] The generating unit may include a generating algorithm unit for generating a specific solution. The generating algorithm unit may generate a solution using, for example, a technology such as a genetic algorithm or a neural network. For example, the generating algorithm unit may generate a plurality of solutions using a genetic algorithm and select an optimal solution from among the solutions. The generating algorithm unit may also generate a solution based on input data using a neural network. Furthermore, the generating algorithm unit may also optimize the solution generation process using reinforcement learning. This allows the generating unit to provide practical advice by generating a specific solution. A part or all of the above-mentioned processing in the generating algorithm unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating algorithm unit may input input data to the generating AI and cause the generating AI to generate a solution.

[0070] The providing unit can provide the generated solution to the user. The providing unit can provide the solution by, for example, email, dashboard, report, or other methods. For example, the providing unit sends the generated solution to the user by email. The providing unit can also display the solution in real time through a dashboard. Furthermore, the providing unit can provide the solution in a report format so that the user can check detailed information. In this way, the providing unit can provide the generated solution to the user, allowing the user to make a calm decision. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated solution to the generating AI and cause the generating AI to execute a method for providing the solution.

[0071] The reception unit can receive problems and issues such as international or regional conflicts and conflicts between companies. The reception unit can receive problems and issues such as, for example, international conflicts, regional conflicts, and conflicts between companies. For example, the reception unit receives information on international conflicts by text input. The reception unit can also receive information on regional conflicts by voice input. Furthermore, the reception unit can also receive information on conflicts between companies by image input. This allows the reception unit to receive a variety of problems and issues, making it possible to use the reception unit in a wide range of fields. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input information on international conflicts to the generation AI and cause the generation AI to receive the information.

[0072] The generation unit can generate a solution that promotes dialogue, cooperation, and mutual understanding. The generation unit can generate a solution that promotes dialogue, for example. For example, the generation unit generates a proposal for peace negotiations through dialogue. The generation unit can also generate a solution that promotes cooperation. For example, the generation unit generates a proposal for a joint project. Furthermore, the generation unit can generate a solution that promotes mutual understanding. For example, the generation unit generates a proposal for cultural exchange. In this way, the generation unit can provide a peaceful solution by promoting dialogue, cooperation, and mutual understanding. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input a solution that promotes dialogue into the generation AI and cause the generation AI to execute the generation of a solution.

[0073] The reception unit can estimate the user's emotion and adjust the input timing of the problem or task based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit provides an environment in which the user can relax by delaying the input timing. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, the reception unit can also prompt the user to input the problem or task immediately when the user is relaxed. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, the reception unit can adjust the timing so that the user can complete the input quickly when the user is in a hurry. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. As a result, the reception unit can input the problem or task in a more appropriate environment by adjusting the input timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate emotions.

[0074] The reception unit can analyze the input history of the user's past problems and tasks and select the optimal input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, the reception unit analyzes the user's past input history, and if the user has used voice input a lot, preferentially suggests voice input. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. For example, if the user has used text input a lot at night, the reception unit suggests text input at night. Furthermore, the reception unit can analyze the contents of problems and tasks that the user has input in the past and suggest the optimal input method for similar problems and tasks. For example, if the user has input technical questions a lot by voice input in the past, the reception unit suggests voice input for technical questions. In this way, the reception unit can provide the user with the optimal input method by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0075] The reception unit can filter the problems and tasks based on the user's current situation and field of interest when inputting the problems and tasks. The reception unit, for example, preferentially inputs problems and tasks related to the field in which the user is currently interested. For example, the reception unit identifies the user's current field of interest from a questionnaire result or past behavior history, and proposes related problems and tasks. The reception unit can also propose appropriate problems and tasks based on the user's current situation (for example, at work, on vacation, etc.). For example, when the user is at work, the reception unit proposes work-related problems and tasks. Furthermore, the reception unit can analyze the user's past behavior history, and filter and input problems and tasks related to the current field of interest. For example, the reception unit preferentially proposes problems and tasks related to the field in which the user has shown a lot of interest in the past. As a result, the reception unit can input more relevant problems and tasks by filtering based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0076] The reception unit can estimate the user's emotions and determine the priority order of problems and tasks to be input based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit preferentially inputs problems and tasks that are simple and easy to solve. For example, the reception unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. In addition, when the user is relaxed, the reception unit can also preferentially input complex and time-consuming problems and tasks. For example, the reception unit records the user's voice and estimates the emotions using a voice analysis technology. Furthermore, when the user is in a hurry, the reception unit can also preferentially input problems and tasks that can be solved quickly. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotions using an emotion estimation algorithm. As a result, the reception unit can input problems and tasks in a more appropriate order by determining the priority order of problems and tasks according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate emotions.

[0077] When inputting a problem or task, the reception unit can preferentially input a problem or task that is highly relevant in consideration of the geographical location information of the user. The reception unit, for example, preferentially inputs a problem or task related to the area where the user is currently located. For example, the reception unit acquires the geographical location information of the user from GPS data or an IP address, and proposes a problem or task related to the current area. The reception unit can also propose a problem or task related to an area that the user has visited in the past. For example, the reception unit analyzes the user's past behavior history, and proposes a problem or task related to an area that the user has visited in the past. Furthermore, the reception unit can predict and input a problem or task related to an area that the user plans to visit in the future. For example, the reception unit acquires the user's schedule from a calendar app, and proposes a problem or task related to an area that the user plans to visit in the future. As a result, the reception unit can input a problem or task that is more relevant by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographic location data into the generation AI and cause the generation AI to suggest highly relevant problems and issues.

[0078] The reception unit can analyze the user's social media activity when inputting a problem or a task, and input a related problem or task. The reception unit, for example, inputs a problem or task related to a topic in which the user is interested on social media. For example, the reception unit analyzes the content of the user's social media postings, and proposes a problem or task related to the topic in which the user is interested. The reception unit can also propose a problem or task related to a topic in which the user's friends or followers on social media are interested. For example, the reception unit analyzes the content of the user's friends or followers' posts, and proposes a related problem or task. Furthermore, the reception unit can analyze the user's social media activity history, and input a related problem or task. For example, the reception unit analyzes the user's past posting content and the number of likes, and proposes a related problem or task. In this way, the reception unit can input a more relevant problem or task by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI, and cause the generation AI to execute the proposal of a related problem or task.

[0079] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, the analysis unit can provide a detailed analysis result when the user is relaxed. For example, the analysis unit records the user's voice and estimates the emotion using a voice analysis technique. Furthermore, when the user is in a hurry, the analysis unit can provide an analysis result that covers the main points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate emotions.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the problem or issue during the analysis. The analysis unit, for example, performs a detailed analysis on problems or issues with high importance. For example, the analysis unit evaluates the impact range and risk level of the problem or issue, and performs a detailed analysis on those with high importance. The analysis unit can also perform a simplified analysis on problems or issues with low importance. For example, the analysis unit performs a simplified analysis on problems or issues with low impact range and risk level. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on problems or issues with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on problems or issues with medium impact range and risk level. As a result, the analysis unit can perform a more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the problem or issue. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input importance data of the problem or issue to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the problem or issue during analysis. For example, the analysis unit applies an algorithm that promotes dialogue and cooperation to an international conflict. For example, the analysis unit analyzes data related to an international conflict and applies an algorithm for promoting dialogue and cooperation. The analysis unit can also apply an algorithm that promotes mutual understanding to a regional conflict. For example, the analysis unit analyzes data related to a regional conflict and applies an algorithm for promoting mutual understanding. Furthermore, the analysis unit can apply an algorithm that proposes a joint project to a conflict between companies. For example, the analysis unit analyzes data related to a conflict between companies and applies an algorithm for proposing a joint project. As a result, the analysis unit can perform a more appropriate analysis by applying different analysis algorithms depending on the category of the problem or issue. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the problem or issue to the generation AI and cause the generation AI to apply the analysis algorithm.

[0082] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit performs a short and to the point analysis. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, the analysis unit can perform a detailed analysis when the user is relaxed. For example, the analysis unit records the user's voice and estimates the emotion using a voice analysis technique. In addition, the analysis unit can perform a visually stimulating analysis when the user is excited. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the analysis unit to provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate emotions.

[0083] During the analysis, the analysis unit can determine the priority of the analysis based on the submission time of the problem or assignment. The analysis unit, for example, prioritizes the analysis of problems or assignments with high urgency. For example, the analysis unit evaluates the submission date and submission time of the problem or assignment, and prioritizes the analysis of those with high urgency. The analysis unit can also prioritize the analysis of problems or assignments with a close submission time. For example, the analysis unit prioritizes the analysis of problems or assignments with a close submission deadline. Furthermore, the analysis unit can also postpone the analysis of problems or assignments with a distant submission time. For example, the analysis unit postpones the analysis of problems or assignments with a distant submission deadline. As a result, the analysis unit can perform the analysis in a more appropriate order by determining the priority of the analysis based on the submission time of the problem or assignment. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the problem or assignment to the generation AI, and cause the generation AI to execute the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the problems and tasks during analysis. The analysis unit, for example, prioritizes the analysis of highly relevant problems and tasks. For example, the analysis unit evaluates common themes and related keywords of the problems and tasks, and prioritizes the analysis of those with high relevance. The analysis unit can also postpone the analysis of low-relevance problems and tasks. For example, the analysis unit postpones the analysis of low-relevance problems and tasks. Furthermore, the analysis unit can also moderately analyze problems and tasks with a medium degree of relevance. For example, the analysis unit moderately analyzes problems and tasks with a medium degree of relevance. As a result, the analysis unit can perform analysis in a more appropriate order by adjusting the order of analysis based on the relevance of the problems and tasks. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input relevance data of problems and tasks to the generation AI and cause the generation AI to adjust the order of analysis.

[0085] The generation unit can estimate the user's emotion and adjust the solution generation method based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a solution that proceeds at a leisurely pace. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is in a hurry, the generation unit can also generate a solution that can be quickly implemented. For example, the generation unit records the user's voice and estimates the emotion using a voice analysis technique. Furthermore, when the user is excited, the generation unit can also generate a visually stimulating solution. For example, the generation unit collects the user's biodata (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. As a result, the generation unit adjusts the solution generation method according to the user's emotion, thereby providing a more appropriate solution. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate emotions.

[0086] The generation unit can improve the accuracy of generation by considering the interrelationships between problems and issues when generating a solution. The generation unit, for example, generates a method for solving mutually related problems and issues collectively. For example, the generation unit evaluates the causal relationship and correlation between problems and issues, and generates a method for solving the mutually related ones collectively. The generation unit can also generate an optimal solution by considering problems and issues that affect each other. For example, the generation unit evaluates the impact range and risk level of problems and issues, and generates an optimal solution by considering those that affect each other. Furthermore, the generation unit can generate an effective solution by combining problems and issues that complement each other. For example, the generation unit evaluates the complementary relationship between problems and issues, and generates an effective solution by combining those that complement each other. As a result, the generation unit can generate a more accurate solution by considering the interrelationships between problems and issues. A part or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input interrelationship data between problems and issues to the generation AI, and cause the generation AI to generate a solution.

[0087] The generation unit can generate a solution by taking into consideration attribute information of the submitter of the problem or task when generating the solution. The generation unit generates an appropriate solution based on, for example, the expertise and experience of the submitter. For example, the generation unit evaluates the expertise and experience of the submitter and generates an appropriate solution based on the expertise and experience. The generation unit can also generate an appropriate solution by taking into consideration the cultural background and values ​​of the submitter. For example, the generation unit evaluates the cultural background and values ​​of the submitter and generates an appropriate solution based on the evaluation. Furthermore, the generation unit can analyze the past behavior history of the submitter and generate an optimal solution. For example, the generation unit analyzes the past behavior history of the submitter and generates an optimal solution based on the evaluation. In this way, the generation unit can generate a more appropriate solution by taking into consideration the attribute information of the submitter. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input attribute information data of the submitter to the generation AI and cause the generation AI to generate a solution.

[0088] The generation unit can estimate the user's emotion and determine the priority of solutions to be generated based on the estimated user's emotion. For example, when the user is feeling stressed, the generation unit preferentially generates solutions that are simple and can be implemented quickly. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, the generation unit can also preferentially generate solutions that are complex and time-consuming when the user is relaxed. For example, the generation unit records the user's voice and estimates the emotion using a voice analysis technology. In addition, the generation unit can also preferentially generate solutions that can be implemented quickly when the user is in a hurry. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. In this way, the generation unit can provide solutions in a more appropriate order by determining the priority of solutions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate emotions.

[0089] The generation unit can generate solutions while taking into consideration the geographical distribution of problems and issues. The generation unit, for example, generates a method for preferentially solving geographically close problems and issues. For example, the generation unit evaluates the geographical distribution of problems and issues and generates a method for preferentially solving geographically close problems and issues. The generation unit can also generate a method for solving geographically distant problems and issues by postponing their resolution. For example, the generation unit generates a method for solving geographically distant problems and issues by postponing their resolution. Furthermore, the generation unit can also generate a method for solving geographically related problems and issues collectively. For example, the generation unit evaluates geographically related problems and issues and generates a method for solving them collectively. In this way, the generation unit can generate more appropriate solutions by taking into consideration the geographical distribution of problems and issues. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input geographical distribution data of problems and issues to the generation AI and cause the generation AI to generate solutions.

[0090] The generation unit can improve the accuracy of generation by referring to related literature of the problem or issue when generating a solution. The generation unit generates an optimal solution based on, for example, related literature. For example, the generation unit refers to academic papers and patent documents related to the problem or issue, and generates an optimal solution based on the reference. The generation unit can also improve the accuracy of the solution by referring to related literature. For example, the generation unit refers to technical reports and industry reports, and improves the accuracy of the solution based on the reference. Furthermore, the generation unit can analyze related literature and generate an effective solution. For example, the generation unit analyzes related literature using text analysis technology, and generates an effective solution based on the analysis. As a result, the generation unit can generate a more accurate solution by referring to the related literature. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input related literature data to the generation AI and cause the generation AI to generate a solution.

[0091] The providing unit can estimate the user's emotion and adjust the solution providing method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides the solution in a simple and highly visible manner. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the providing unit can also provide the solution in a manner including detailed information. For example, the providing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the providing unit can also provide the solution in a manner that focuses on the main points. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the providing unit can provide the solution in a more appropriate manner by adjusting the solution providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate emotions.

[0092] When providing a solution, the providing unit can select the optimal providing method by referring to the user's past solution usage history. The providing unit, for example, preferentially proposes a solution providing method that the user has used in the past. For example, the providing unit analyzes the user's past solution usage history and preferentially proposes the providing method that was used in the past. The providing unit can also propose a providing method suitable for a specific time period from the user's past usage history. For example, the providing unit proposes a providing method that the user used a lot at night. Furthermore, the providing unit can analyze the user's past usage history and select the optimal providing method. For example, the providing unit analyzes the user's past usage history and selects the optimal providing method based on the analysis. As a result, the providing unit can select the optimal providing method for the user by referring to the past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past solution usage history data to the generating AI and cause the generating AI to select the optimal providing method.

[0093] The providing unit can estimate the user's emotion and adjust the order of providing solutions based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit preferentially provides a solution that is simple and can be implemented quickly. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the providing unit can also preferentially provide a solution that is complex and time-consuming. For example, the providing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the providing unit can also preferentially provide a solution that can be implemented quickly. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the providing unit can provide solutions in a more appropriate order by adjusting the order of providing solutions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate emotions.

[0094] The providing unit can select the optimal providing method in consideration of the device information of the user when providing a solution. For example, when the user is using a smartphone, the providing unit provides a providing method that matches the screen size. For example, the providing unit acquires the device information of the user and provides a providing method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the providing unit can also provide a providing method optimized for a large screen. For example, the providing unit provides a providing method optimized for the screen size of the tablet. Furthermore, when the user is using a smartwatch, the providing unit can also provide a simple providing method with high visibility. For example, the providing unit provides a providing method optimized for the screen size of the smartwatch. Thereby, the providing unit can provide a solution in a more appropriate manner by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it preferentially suggests input methods (such as voice and text) that the user has used frequently in the past. The reception unit can also predict and suggest an input method to be used during a specific time period from the user's past input history. Furthermore, the reception unit can analyze the contents of questions and tasks that the user has entered in the past and suggest the optimal input method for similar questions and tasks. In this way, the reception unit can provide the user with the optimal input method by analyzing the past input history.

[0097] 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 according to the user's emotions.

[0098] The generation unit can improve the accuracy of solution generation by taking into account the interrelationships between problems and issues. For example, it can generate a method for solving interrelated problems and issues collectively. It can also generate optimal solutions by taking into account problems and issues that affect each other. Furthermore, it can generate effective solutions by combining problems and issues that complement each other. In this way, the generation unit can generate more accurate solutions by taking into account the interrelationships between problems and issues.

[0099] The providing unit can estimate the user's emotions and adjust the method of providing a solution based on the estimated user's emotions. For example, if the user is nervous, the solution can be provided in a simple and highly visible manner. If the user is relaxed, the solution can be provided in a manner including detailed information. Furthermore, if the user is in a hurry, the solution can be provided in a manner that focuses on the main points. In this way, the providing unit can provide a solution in a more appropriate manner by adjusting the method of providing the solution according to the user's emotions.

[0100] The reception unit can preferentially input highly relevant questions and tasks in consideration of the geographical location information of the user. For example, questions and tasks related to the area where the user is currently located can be preferentially input. In addition, questions and tasks related to areas the user has visited in the past can be suggested. Furthermore, questions and tasks related to areas the user plans to visit in the future can be predicted and input. In this way, the reception unit can input more relevant questions and tasks by considering the geographical location information of the user.

[0101] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the problem or issue. For example, a detailed analysis can be performed for problems or issues of high importance. Also, a simplified analysis can be performed for problems or issues of low importance. Furthermore, an analysis with an appropriate level of detail can be performed for problems or issues of medium importance. In this way, the analysis unit can perform a more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the problem or issue.

[0102] The generation unit can estimate the user's emotion and adjust the solution generation method based on the estimated user's emotion. For example, when the user is relaxed, a solution that proceeds at a leisurely pace can be generated. When the user is in a hurry, a solution that can be quickly executed can be generated. Furthermore, when the user is excited, a visually stimulating solution can be generated. In this way, the generation unit can provide a more appropriate solution by adjusting the solution generation method according to the user's emotion.

[0103] When providing a solution, the providing unit can select the optimal providing method by taking into consideration the device information of the user. For example, if the user is using a smartphone, a providing method that matches the screen size can be provided. Also, if the user is using a tablet, a providing method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a providing method that is simple and highly visible can be provided. In this way, the providing unit can provide a solution in a more appropriate manner by taking into consideration the device information of the user.

[0104] During analysis, the analysis unit can apply different analysis algorithms depending on the category of problem or issue. For example, for international conflicts, an algorithm that promotes dialogue and cooperation can be applied. For regional conflicts, an algorithm that promotes mutual understanding can be applied. Furthermore, for conflicts between companies, an algorithm that proposes joint projects can be applied. In this way, the analysis unit can perform more appropriate analysis by applying different analysis algorithms depending on the category of problem or issue.

[0105] The providing unit can estimate the user's emotion and adjust the order in which solutions are provided based on the estimated user's emotion. For example, when the user is stressed, a simple and quickly executable solution can be provided preferentially. When the user is relaxed, a complex and time-consuming solution can be provided preferentially. Furthermore, when the user is in a hurry, a quickly executable solution can be provided preferentially. In this way, the providing unit can provide solutions in a more appropriate order by adjusting the order in which solutions are provided according to the user's emotion.

[0106] The process flow of the second embodiment will be briefly described below.

[0107] Step 1: The reception unit inputs the problem or issue that the user wants to solve. For example, various issues are targeted, such as international disputes, regional disputes, and conflicts between companies. The reception unit can accept problems or issues by text input, voice input, image input, and other methods. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze text data using natural language processing technology, image data using image analysis technology, and voice data using voice analysis technology. Step 3: The generator generates solutions from a specific perspective based on the information analyzed by the analyzer. The generator uses a generative AI to generate solutions, for example, solutions that promote dialogue, cooperation, and mutual understanding. Step 4: The providing unit provides the solution generated by the generating unit to the user. The providing unit can provide the solution by email, dashboard, report, or other methods.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 inputs a problem or issue that the user wants to solve. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a solution from a parental perspective based on the analyzed information. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated solution to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 inputs a problem or issue that the user wants to solve. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a solution from a parental perspective based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated solution to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 inputs a problem or issue that the user wants to solve. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a solution from a parental perspective based on the analyzed information. The provision unit is realized, for example, by the display 343 of the headset type terminal 314, and provides the generated solution to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 inputs a problem or issue that the user wants to solve. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a solution from a parental perspective based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated solution to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] 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.

[0162] 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.

[0163] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Appendix 1) A reception unit for receiving input of problems and assignments; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a solution from a specific viewpoint based on the information analyzed by the analysis unit; a providing unit that provides the solution generated by the generating unit; A system characterized by: (Appendix 2) The analysis unit is Equipped with a filtering section to eliminate human perspective 2. The system of claim 1, (Appendix 3) The generation unit is Equipped with a generation algorithm section for generating specific solutions 2. The system of claim 1, (Appendix 4) The providing unit is Present the generated solution to the user 2. The system of claim 1, (Appendix 5) The reception unit is Accepting problems and issues such as international or regional disputes, and conflicts between companies 2. The system of claim 1, (Appendix 6) The generation unit is Generate solutions that foster dialogue, cooperation and mutual understanding 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and adjust the timing of inputting questions and tasks based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past problem and assignment input history and select the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is Filtering questions and assignments based on the user's current situation and areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize input problems and issues based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering questions or issues, the system considers the user's geographic location to prioritize the most relevant questions or issues. 2. The system of claim 1, (Appendix 12) The reception unit is When a problem or issue is entered, analyze the user's social media activity and enter related problems or issues. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail of the analysis based on the severity of the problem or issue 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of problem or issue 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when questions or assignments were submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of issues and problems 2. The system of claim 1, (Appendix 19) The generation unit is Inferring user emotions and adjusting the solution generation method based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The generation unit is When generating solutions, improve the accuracy of generation by considering the interrelationships between problems and issues. 2. The system of claim 1, (Appendix 21) The generation unit is When generating solutions, the attribute information of the person who submitted the problem or task is taken into consideration. 2. The system of claim 1, (Appendix 22) The generation unit is Estimate user emotions and prioritize solutions to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The generation unit is When generating solutions, take into account the geographical distribution of problems and issues. 2. The system of claim 1, (Appendix 24) The generation unit is When generating solutions, improve the accuracy of the generation by referring to literature related to the problem or issue. 2. The system of claim 1, (Appendix 25) The providing unit is Inferring user emotions and adjusting the way in which a solution is provided based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The providing unit is When providing a solution, the optimal solution delivery method is selected by referring to the user's past solution usage history. 2. The system of claim 1, (Appendix 27) The providing unit is Inferring user sentiment and adjusting the order in which solutions are provided based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The providing unit is When providing a solution, select the optimal method of providing the solution by taking into account the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0180] 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 for receiving input of problems and assignments; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a solution from a specific viewpoint based on the information analyzed by the analysis unit; a providing unit that provides the solution generated by the generating unit; A system characterized by:

2. The analysis unit is Equipped with a filtering section to eliminate human perspective 2. The system of claim 1 .

3. The generation unit is Equipped with a generation algorithm section for generating specific solutions 2. The system of claim 1 .

4. The reception unit is Accepting problems and issues such as international or regional disputes, and conflicts between companies 2. The system of claim 1 .

5. The generation unit is Generate solutions that foster dialogue, cooperation and mutual understanding 2. The system of claim 1 .

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

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

8. The reception unit is Filtering questions and assignments based on the user's current situation and areas of interest 2. The system of claim 1 .

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