system

Abstract

The system according to the embodiment aims to abstract sensitive issues, share them widely, and solicit solutions. [Solution] A system according to an embodiment includes a reception unit, an abstraction unit, a posing unit, a proposal unit, an evaluation unit, and a reward unit. The reception unit accepts problems from users. The abstraction unit abstracts the problems accepted by the reception unit. The posing unit proposes the problems abstracted by the abstraction unit to the community. The proposal unit proposes solutions to the problems posed by the posing unit. The evaluation unit evaluates the solutions proposed by the proposal unit. The reward unit manages rewards based on the results of the evaluation by the evaluation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to widely share sensitive issues and solicit solutions.

[0005] The system according to the embodiment aims to abstract sensitive issues, share them widely, and solicit solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an abstraction unit, a posing unit, a proposal unit, an evaluation unit, and a reward unit. The reception unit accepts problems from users. The abstraction unit abstracts the problems accepted by the reception unit. The posing unit proposes the problems abstracted by the abstraction unit to the community. The proposal unit proposes solutions to the problems posed by the posing unit. The evaluation unit evaluates the solutions proposed by the proposal unit. The reward unit manages rewards based on the results of the evaluation by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can abstract sensitive issues, share them widely, and solicit solutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A participatory problem-solving system according to an embodiment of the present invention is a system for solving problems faced by users in hobbies, work, and other fields. In this system, when a user inputs a problem they are having into an app, an AI abstracts the problem and presents it as a fictitious case within the community. Next, community participants and autonomous AI propose and share solutions to the fictitious problem, helping the user solve their problem. This approach allows for widespread solicitation of answers, even for sensitive issues such as patent issues or confidential matters, helping to resolve them. Although the answers are for fictitious cases, they can lead to new ideas and direct solutions to the original problem. This allows people from around the world to brainstorm on the same platform and obtain diverse answers. Furthermore, participants are rewarded, fostering an active community. Users can be inspired by simply viewing other participants' ideas, and they can also submit their own ideas, ensuring constant new discoveries. In this way, the participatory problem-solving system efficiently abstracts users' problems and enables the community to propose, evaluate, and manage rewards for solutions.

[0029] A participatory problem-solving system according to an embodiment includes a receiving unit, an abstraction unit, a proposal unit, a proposal unit, an evaluation unit, and a reward unit. The receiving unit receives a problem from a user. Examples of user problems include, but are not limited to, technical problems, business problems, and user experience problems. The receiving unit receives the problem by, for example, inputting a user's problem with an app. The receiving unit can also receive problems in various formats, such as voice input or image input. For example, when a user inputs a problem by voice, the receiving unit analyzes the voice data and converts it into text data. When a user inputs a problem by image, the receiving unit analyzes the image data and extracts the content of the problem. The abstraction unit abstracts the problem received by the receiving unit. Abstraction is performed, for example, by extracting elements of the problem and adjusting the level of abstraction, but is not limited to, for example. For example, the abstraction unit uses natural language processing technology to extract the elements of the problem. To adjust the level of abstraction, the abstraction unit can also adjust the level of detail of the abstraction based on the importance or category of the problem. The raising unit raises the problem abstracted by the abstraction unit to the community. The raising is performed, for example, by adjusting a notification method to the community or the timing of the raising, but is not limited to such examples. For example, the raising unit raises the problem by sending an email or a push notification to the community. The raising unit can also adjust the method of raising the problem based on a user's feelings or the importance of the problem in order to adjust the timing of the raising. The suggestion unit proposes a solution to the problem raised by the raising unit. The proposal is performed, for example, by adjusting a proposal format or evaluation criteria, but is not limited to such examples. For example, the suggestion unit provides a guide for the user to input the proposal. The suggestion unit can also adjust the level of detail of the proposal based on a user's feelings or the importance of the problem in order to adjust the evaluation criteria of the proposal. The evaluation unit evaluates the solution proposed by the proposal unit. The evaluation is performed, for example, by adjusting the evaluation scale or process, but is not limited to such examples. For example, the evaluation unit provides a guide for the user to input the evaluation.The evaluation unit can also adjust the evaluation criteria based on the user's emotions and the importance of the problem in order to adjust the evaluation process. The reward unit manages rewards based on the results of the evaluation by the evaluation unit. Rewards are given, for example, by adjusting the form of the reward or the distribution method, but are not limited to such examples. For example, the reward unit provides a guide for users to receive rewards. The reward unit can also adjust the level of detail of the reward based on the user's emotions and the importance of the problem in order to adjust the reward distribution method. This makes it possible for the participatory problem-solving system according to the embodiment to efficiently abstract users' problems and enable solutions to be proposed, evaluated, and rewards managed in a community.

[0030] The participatory problem-solving system includes a guide unit that enables users to effectively input problems. The guide unit supports users in effectively inputting problems. The guide unit assists users in inputting problems, for example, by providing user interface design and input guide content. For example, the guide unit displays input fields and input examples when the user inputs a problem. The guide unit can also analyze the content entered by the user and provide appropriate feedback. For example, the guide unit points out areas for improvement in the input content based on the content entered by the user. The guide unit can also provide related information based on the content entered by the user. For example, the guide unit displays past solutions and reference materials related to the problem entered by the user. This allows the user to effectively input problems. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without AI. For example, the guide unit can input the content entered by the user to a generation AI and have the generation AI analyze the input content and provide feedback.

[0031] The participatory problem-solving system includes a feedback unit that shares feedback within the community. The feedback unit promotes improvement of solutions by sharing feedback within the community. The feedback unit supports sharing feedback within the community, for example, by providing a feedback format and a sharing method. For example, the feedback unit provides a guide for users to input feedback. The feedback unit can also analyze the content of the feedback and provide appropriate feedback. For example, the feedback unit points out areas for improvement in the feedback content based on the feedback input by the user. The feedback unit can also provide related information based on the feedback content. For example, the feedback unit displays past solutions and reference materials related to the feedback input by the user. This allows feedback to be shared within the community and promotes improvement of solutions. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input feedback input by a user to a generation AI and cause the generation AI to analyze the feedback content and provide feedback.

[0032] The participatory problem-solving system includes a transparency management unit for ensuring the transparency of the reward system. The transparency management unit provides a method for disclosing reward allocation and transparency evaluation criteria to ensure the transparency of the reward system. The transparency management unit ensures the transparency of the reward system, for example, by disclosing details of reward allocation. For example, the transparency management unit discloses the reward allocation process and criteria, allowing users to check the reward allocation details. The transparency management unit can also evaluate the transparency of the reward system by providing transparency evaluation criteria. For example, the transparency management unit provides indicators and evaluation methods for evaluating the transparency of reward allocation. This ensures the transparency of the reward system. Some or all of the above-mentioned processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit may input details of reward allocation to a generation AI and have the generation AI perform a transparency evaluation.

[0033] The guide unit can cooperate with the reception unit to support the user in effectively entering a problem. The guide unit cooperates with the reception unit to assist the user in entering a problem. For example, the guide unit analyzes the user's input received from the reception unit and provides appropriate feedback. The guide unit can also cooperate with the reception unit to provide related information based on the user's input. For example, the guide unit displays past solutions and reference materials based on the user's input received from the reception unit. This allows the user to effectively enter a problem. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's input received from the reception unit into a generation AI and have the generation AI analyze the input and provide feedback. This supports the user in effectively entering a problem.

[0034] The feedback unit can work with the proposal unit and the evaluation unit to share improvements to the proposed solution. The feedback unit works with the proposal unit and the evaluation unit to share improvements to the proposed solution. For example, the feedback unit analyzes the proposal received from the proposal unit and identifies improvements based on the evaluation results received from the evaluation unit. The feedback unit can also work with the proposal unit and the evaluation unit to provide a guide for sharing the improvements. For example, the feedback unit displays feedback from the evaluation unit based on the proposal received from the proposal unit. This allows improvements to the proposed solution to be shared, improving the quality of the solution. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the proposal received from the proposal unit and the evaluation results received from the evaluation unit into the generation AI, and cause the generation AI to identify and share improvements.

[0035] The transparency management unit can work with the reward unit to ensure the transparency of the reward system. The transparency management unit works with the reward unit to ensure the transparency of the reward system. For example, the transparency management unit analyzes the details of the reward allocation received from the reward unit and evaluates them based on transparency evaluation criteria. The transparency management unit can also work with the reward unit to provide a guide for ensuring transparency. For example, the transparency management unit publishes the details of the reward allocation received from the reward unit so that users can check the details of the reward allocation. This ensures the transparency of the reward system. Some or all of the above-mentioned processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit may input the details of the reward allocation received from the reward unit into the generation AI and cause the generation AI to evaluate the transparency.

[0036] The reception unit can analyze the user's past question submission history and select the optimal reception method. The reception unit analyzes the user's past question submission history and selects the optimal reception method. For example, the reception unit analyzes the patterns of questions that the user has frequently submitted in the past and suggests the optimal reception method. The reception unit can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, the optimal reception method is selected based on the user's past question submission history. Some 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 question submission history data into a generation AI and have the generation AI select the optimal reception method.

[0037] The reception unit may filter questions based on the user's current project or area of ​​interest when receiving the questions. The reception unit may filter questions based on the user's current project or area of ​​interest when receiving the questions. For example, the reception unit may preferentially receive questions related to the project the user is currently working on. The reception unit may also filter and receive highly relevant questions based on the user's area of ​​interest. Furthermore, the reception unit may also receive questions at an appropriate time depending on the progress of the user's project. This allows questions to be filtered based on the user's current project or area of ​​interest. Some or all of the above-described 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 data on the user's project or area of ​​interest into a generation AI and have the generation AI perform the filtering.

[0038] When accepting questions, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. When accepting questions, the reception unit prioritizes accepting highly relevant questions by taking into account the user's geographical location information. For example, the reception unit prioritizes accepting questions related to the user's current location. The reception unit can also filter and accept region-specific questions based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can prioritize accepting questions closest to the user's current location. This allows highly relevant questions to be preferentially accepted based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter highly relevant questions.

[0039] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving related questions. The reception unit can also filter and receive highly relevant questions based on the activity of accounts the user follows. Furthermore, the reception unit can analyze the user's interests on social media and receive related questions. In this way, related questions are received based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to filter related questions.

[0040] The abstraction unit can adjust the level of detail of the abstraction based on the importance of the problem during abstraction. The abstraction unit adjusts the level of detail of the abstraction based on the importance of the problem during abstraction. For example, the abstraction unit performs detailed abstraction for problems of high importance. The abstraction unit can also perform simple abstraction for problems of low importance. Furthermore, the abstraction unit can gradually adjust the level of detail of the abstraction according to the importance of the problem. In this way, the level of detail of the abstraction is adjusted according to the importance of the problem. Some or all of the above-mentioned processing in the abstraction unit may be performed using AI, for example, or may be performed without using AI. For example, the abstraction unit can input problem importance data to the generation AI and cause the generation AI to adjust the level of detail of the abstraction.

[0041] The abstraction unit can apply different abstraction algorithms depending on the problem category during abstraction. The abstraction unit applies different abstraction algorithms depending on the problem category during abstraction. For example, the abstraction unit applies a technically specialized abstraction algorithm to a technical problem. The abstraction unit can also apply a business-specialized abstraction algorithm to a business-related problem. Furthermore, the abstraction unit can apply a social problem-specialized abstraction algorithm to a social problem. In this way, an appropriate abstraction algorithm is applied depending on the problem category. Some or all of the above-mentioned processing in the abstraction unit may be performed using AI, for example, or may be performed without using AI. For example, the abstraction unit can input problem category data to a generation AI and cause the generation AI to apply an abstraction algorithm.

[0042] The abstraction unit can determine the priority of abstraction based on the time of submission of the questions during abstraction. The abstraction unit determines the priority of abstraction based on the time of submission of the questions during abstraction. For example, the abstraction unit prioritizes abstraction of recently submitted questions. The abstraction unit can also postpone questions that were submitted earlier. Furthermore, the abstraction unit can gradually adjust the priority of abstraction according to the time of submission. In this way, the priority of abstraction is determined based on the time of submission of the questions. Some or all of the above-mentioned processing in the abstraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the abstraction unit can input question submission time data into the generation AI and cause the generation AI to determine the priority of abstraction.

[0043] The abstraction unit can adjust the order of abstraction based on the relevance of the problems during abstraction. The abstraction unit adjusts the order of abstraction based on the relevance of the problems during abstraction. For example, the abstraction unit prioritizes abstraction of highly relevant problems. The abstraction unit can also postpone abstraction of less relevant problems. Furthermore, the abstraction unit can gradually adjust the order of abstraction according to the relevance of the problems. In this way, the order of abstraction is adjusted based on the relevance of the problems. Some or all of the above-described processing in the abstraction unit may be performed using AI, for example, or may be performed without using AI. For example, the abstraction unit can input problem relevance data to a generation AI and cause the generation AI to adjust the order of abstraction.

[0044] The raising unit can adjust the level of detail of the proposal based on the importance of the problem when raising the proposal. The raising unit adjusts the level of detail of the proposal based on the importance of the problem when raising the proposal. For example, the raising unit makes a detailed proposal for a problem of high importance. The raising unit can also make a concise proposal for a problem of low importance. Furthermore, the raising unit can gradually adjust the level of detail of the proposal according to the importance of the problem. In this way, the level of detail of the proposal is adjusted according to the importance of the problem. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input problem importance data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0045] The raising unit can apply different raising algorithms depending on the problem category when raising a problem. The raising unit applies different raising algorithms depending on the problem category when raising a problem. For example, the raising unit applies a technical raising algorithm to a technical problem. The raising unit can also apply a business raising algorithm to a business-related problem. The raising unit can also apply a social problem raising algorithm to a social problem. In this way, an appropriate raising algorithm is applied depending on the problem category. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input problem category data to a generation AI and cause the generation AI to apply a raising algorithm.

[0046] The raising unit can determine the priority of the raising based on when the problem was submitted when it was raised. The raising unit determines the priority of the raising based on when the problem was submitted when it was raised. For example, the raising unit prioritizes raising recently submitted problems. The raising unit can also postpone older submitted problems. Furthermore, the raising unit can gradually adjust the priority of the raising based on when it was submitted. In this way, the priority of the raising is determined based on when the problem was submitted. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input data on when the problem was submitted to the generation AI and have the generation AI determine the priority of the raising.

[0047] The raising unit can adjust the order of raising based on the relevance of the problems when raising them. The raising unit adjusts the order of raising based on the relevance of the problems when raising them. For example, the raising unit prioritizes raising highly relevant problems. The raising unit can also postpone less relevant problems. Furthermore, the raising unit can gradually adjust the order of raising based on the relevance of the problems. In this way, the order of raising is adjusted based on the relevance of the problems. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input problem relevance data into a generation AI and cause the generation AI to adjust the order of raising.

[0048] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making the proposal. The proposal unit adjusts the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a concise proposal for a solution with low importance. Furthermore, the proposal unit can gradually adjust the level of detail of the proposal according to the importance of the solution. In this way, the level of detail of the proposal is adjusted according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the solution when making a proposal. The proposal unit can apply different proposal algorithms depending on the category of the solution when making a proposal. For example, the proposal unit can apply a technical proposal algorithm to a technical solution. The proposal unit can also apply a business proposal algorithm to a business-related solution. The proposal unit can also apply a social problem proposal algorithm to a social solution. In this way, an appropriate proposal algorithm is applied depending on the category of the solution. Some or all of the above-mentioned processing in the proposal unit can be performed using AI, for example, or can be performed without using AI. For example, the proposal unit can input solution category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0050] The proposal unit can determine the priority of proposals based on the time of submission of solutions when making proposals. The proposal unit determines the priority of proposals based on the time of submission of solutions when making proposals. For example, the proposal unit prioritizes proposals based on the time of submission of solutions. The proposal unit can also postpone solutions that were submitted earlier. Furthermore, the proposal unit can gradually adjust the priority of proposals according to the time of submission. In this way, the priority of proposals is determined based on the time of submission of solutions. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input solution submission time data into the generation AI and cause the generation AI to determine the priority of proposals.

[0051] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. The suggestion unit adjusts the order of proposals based on the relevance of the solutions when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant solutions. The suggestion unit can also postpone less relevant solutions. Furthermore, the suggestion unit can gradually adjust the order of proposals according to the relevance of the solutions. In this way, the order of proposals is adjusted based on the relevance of the solutions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the solutions to the generation AI and cause the generation AI to adjust the order of proposals.

[0052] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between solutions during evaluation. The evaluation unit improves the accuracy of the evaluation by taking into account the interrelationships between solutions during evaluation. For example, the evaluation unit analyzes the relevance between solutions and highly rates solutions that complement each other. The evaluation unit can also eliminate overlapping solutions by taking into account the interrelationships between solutions. Furthermore, the evaluation unit can evaluate the most effective combination based on the interrelationships between solutions. In this way, the accuracy of the evaluation is improved by taking into account the interrelationships between solutions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the interrelationships between solutions into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0053] The evaluation unit can perform the evaluation taking into account the attribute information of the submitter of the solution when performing the evaluation. The evaluation unit can perform the evaluation taking into account the attribute information of the submitter of the solution when performing the evaluation. For example, the evaluation unit performs the evaluation taking into account the submitter's expertise and experience. The evaluation unit can also prioritize evaluation of highly reliable solutions based on the submitter's past evaluation history. Furthermore, the evaluation unit can perform the evaluation taking into account diversity based on the submitter's attribute information. This improves the accuracy of the evaluation by taking into account the attribute information of the submitter of the solution. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submitter's attribute information data into the generation AI and have the generation AI perform the evaluation.

[0054] The evaluation unit can perform the evaluation taking into account the geographical distribution of the solutions. The evaluation unit performs the evaluation taking into account the geographical distribution of the solutions. For example, the evaluation unit may highly evaluate solutions that cover a wide geographical area. The evaluation unit may also preferentially evaluate solutions that address problems that are specific to a region. Furthermore, the evaluation unit can evaluate diverse solutions taking into account the geographical distribution. This improves the accuracy of the evaluation by taking into account the geographical distribution of the solutions. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input geographical distribution data of the solutions into the generation AI and have the generation AI perform the evaluation.

[0055] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the solution during the evaluation. The evaluation unit improves the accuracy of the evaluation by referring to literature related to the solution during the evaluation. For example, the evaluation unit performs the evaluation by referring to academic papers related to the solution. The evaluation unit can also perform the evaluation by referring to patent documents related to the solution. Furthermore, the evaluation unit can perform the evaluation by referring to industry reports related to the solution. In this way, the accuracy of the evaluation is improved by referring to literature related to the solution. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input literature data related to the solution into the generation AI and have the generation AI perform the evaluation.

[0056] The reward unit can adjust the level of detail of the reward based on the evaluation result when allocating the reward. The reward unit adjusts the level of detail of the reward based on the evaluation result when allocating the reward. For example, the reward unit allocates detailed rewards to highly rated solutions. The reward unit can also allocate simple rewards to low rated solutions. Furthermore, the reward unit can gradually adjust the level of detail of the reward according to the evaluation result. In this way, the level of detail of the reward is adjusted based on the evaluation result. Some or all of the above-mentioned processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input the evaluation result data to the generation AI and cause the generation AI to adjust the level of detail of the reward.

[0057] The reward unit can apply different reward allocation algorithms depending on the category of the solution when allocating rewards. The reward unit applies different reward allocation algorithms depending on the category of the solution when allocating rewards. For example, the reward unit applies a technology-specialized reward allocation algorithm to a technical solution. The reward unit can also apply a business-specialized reward allocation algorithm to a business-related solution. Furthermore, the reward unit can apply a social problem-specialized reward allocation algorithm to a social solution. In this way, an appropriate reward allocation algorithm is applied depending on the category of the solution. Some or all of the above-mentioned processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input solution category data to the generation AI and cause the generation AI to apply the reward allocation algorithm.

[0058] The reward unit can determine the priority of rewards based on the time of submission of solutions when allocating rewards. The reward unit determines the priority of rewards based on the time of submission of solutions when allocating rewards. For example, the reward unit preferentially allocates rewards to recently submitted solutions. The reward unit can also postpone solutions that have been submitted earlier. Furthermore, the reward unit can gradually adjust the priority of rewards according to the time of submission. In this way, the priority of rewards is determined based on the time of submission of solutions. Some or all of the above-mentioned processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input solution submission time data into the generation AI and have the generation AI determine the priority of rewards.

[0059] The reward unit can adjust the reward allocation based on the relevance of the solution when allocating the reward. The reward unit adjusts the reward allocation based on the relevance of the solution when allocating the reward. For example, the reward unit preferentially allocates rewards to highly relevant solutions. The reward unit can also postpone solutions with low relevance. Furthermore, the reward unit can gradually adjust the reward allocation according to the relevance of the solution. In this way, the reward allocation is adjusted based on the relevance of the solution. Some or all of the above-described processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input relevance data of the solution to the generation AI and cause the generation AI to adjust the reward allocation.

[0060] The guide unit can adjust the level of detail of the guide based on the importance of the question during guidance. The guide unit can adjust the level of detail of the guide based on the importance of the question during guidance. For example, the guide unit provides detailed guidance for questions of high importance. The guide unit can also provide brief guidance for questions of low importance. Furthermore, the guide unit can gradually adjust the level of detail of the guide depending on the importance of the question. In this way, the level of detail of the guide is adjusted depending on the importance of the question. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can input question importance data to a generation AI and cause the generation AI to adjust the level of detail of the guide.

[0061] The guide unit can determine the priority of guides based on the time when the questions were submitted during guidance. The guide unit determines the priority of guides based on the time when the questions were submitted during guidance. For example, the guide unit prioritizes guidance for recently submitted questions. The guide unit can also postpone questions that were submitted earlier. Furthermore, the guide unit can gradually adjust the priority of guides according to the time of submission. In this way, the priority of guides is determined based on the time when the questions were submitted. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can input question submission time data into the generation AI and have the generation AI determine the priority of guides.

[0062] The feedback unit can adjust the level of detail of the feedback based on the importance of the solution when providing feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the solution when providing feedback. For example, the feedback unit provides detailed feedback for solutions with high importance. The feedback unit can also provide brief feedback for solutions with low importance. Furthermore, the feedback unit can gradually adjust the level of detail of the feedback according to the importance of the solution. In this way, the level of detail of the feedback is adjusted according to the importance of the solution. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0063] The feedback unit can determine the priority of feedback based on the time of submission of a solution when providing feedback. The feedback unit determines the priority of feedback based on the time of submission of a solution when providing feedback. For example, the feedback unit prioritizes feedback for recently submitted solutions. The feedback unit can also postpone solutions that were submitted earlier. Furthermore, the feedback unit can gradually adjust the priority of feedback depending on the time of submission. In this way, the priority of feedback is determined based on the time of submission of a solution. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the time of submission of a solution to the generation AI and cause the generation AI to determine the priority of feedback.

[0064] The transparency management unit can adjust the level of transparency detail based on the importance of reward allocation during transparency management. The transparency management unit adjusts the level of transparency detail based on the importance of reward allocation during transparency management. For example, the transparency management unit performs detailed transparency management for reward allocations with high importance. The transparency management unit can also perform simple transparency management for reward allocations with low importance. Furthermore, the transparency management unit can gradually adjust the level of transparency detail according to the importance of reward allocation. In this way, the level of transparency detail is adjusted according to the importance of reward allocation. Some or all of the above-described processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit can input reward allocation importance data to the generation AI and cause the generation AI to adjust the level of transparency detail.

[0065] During transparency management, the transparency management unit can determine transparency priorities based on the submission timing of reward allocations. During transparency management, the transparency management unit determines transparency priorities based on the submission timing of reward allocations. For example, the transparency management unit prioritizes transparency management for recently submitted reward allocations. The transparency management unit can also postpone reward allocations that have been submitted earlier. Furthermore, the transparency management unit can gradually adjust the priority of transparency management depending on the submission timing. In this way, transparency priorities are determined based on the submission timing of reward allocations. Some or all of the above-described processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit can input reward allocation submission timing data into the generation AI and have the generation AI determine the transparency priorities.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The reception unit can analyze the user's past problem-solving history and automatically suggest similar problems. For example, if the user has solved a technical problem in the past, the reception unit can suggest related technical problems based on that history. Also, if the user has solved a business problem, the reception unit can similarly suggest related business problems. Furthermore, the reception unit can also suggest new problems that the user may be interested in based on the user's problem-solving history. This allows the user to take advantage of their own experience to tackle new problems.

[0068] The notifying unit can adjust the timing of the notifying in consideration of the user's current activity status. For example, if the user is in a meeting, the notifying unit delays the timing of the notifying. Also, if the user is on a break, the notifying unit can immediately notify. Furthermore, if the user is on the move, the notifying unit can adjust the timing of the notifying to make the notifying at an appropriate time. In this way, the timing of the notifying is adjusted according to the user's activity status.

[0069] The evaluation unit can analyze the user's past evaluation history and adjust the evaluation criteria. For example, if the user has given strict evaluations in the past, the evaluation unit will evaluate based on those criteria. Also, if the user has given lenient evaluations in the past, the evaluation unit can similarly evaluate based on those criteria. Furthermore, the evaluation criteria can be adjusted in stages based on the user's evaluation history. In this way, the evaluation criteria are adjusted based on the user's past evaluation history.

[0070] The feedback unit can analyze the user's past feedback history and adjust the content of the feedback. For example, if the user has provided detailed feedback in the past, the feedback unit can provide detailed feedback based on that criterion. Also, if the user has provided brief feedback in the past, the feedback unit can provide brief feedback based on that criterion as well. Furthermore, the content of the feedback can be adjusted in stages based on the user's feedback history. In this way, the content of the feedback is adjusted based on the user's past feedback history.

[0071] The guide unit can analyze the user's past guide usage history and suggest the optimal guide method. For example, if the user has used an audio guide in the past, the guide unit can suggest an audio guide based on that history. Also, if the user has used a text guide in the past, the guide unit can suggest a text guide based on that history as well. Furthermore, the guide method that the user can use most effectively can also be suggested based on the user's guide usage history. In this way, the optimal guide method is suggested based on the user's past guide usage history.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit accepts user issues. User issues can include technical issues, business issues, and issues related to the user experience. The reception unit accepts issues by having the user input the problem they are having with the app. It can also accept issues in various formats, such as voice input or image input. For example, it can analyze voice data and convert it into text data, or analyze image data to extract the content of the problem. Step 2: The abstraction unit abstracts the problem received by the reception unit. Abstraction is performed by extracting the elements of the problem and adjusting the level of abstraction. For example, natural language processing technology can be used to extract the elements of the problem and adjust the level of abstraction based on the importance and category of the problem. Step 3: The problem-raising unit raises the problem abstracted by the abstraction unit to the community. The raising is performed by adjusting the notification method and timing to the community. For example, the problem is raised by sending an email or push notification, and the method of raising the problem is adjusted based on the user's sentiment and the importance of the problem. Step 4: The suggestion unit proposes solutions to the problems raised by the problem raising unit. The proposals are made by adjusting the format and evaluation criteria of the proposals. For example, the suggestion unit provides a guide for users to input their proposals, and adjusts the level of detail of the proposals based on the user's feelings and the importance of the problem. Step 5: The evaluator evaluates the solutions proposed by the proposer. The evaluation is performed by adjusting the evaluation scale and process. For example, the evaluator provides a guide for users to input their evaluations and adjusts the evaluation criteria based on the user's feelings and the importance of the problem. Step 6: The reward unit administers rewards based on the results evaluated by the evaluation unit. Rewards are administered by adjusting the reward format and distribution method. For example, it provides a guide for users to receive rewards and adjusts the level of detail of rewards based on the user's feelings and the importance of the problem.

[0074] (Example 2) A participatory problem-solving system according to an embodiment of the present invention is a system for solving problems faced by users in hobbies, work, and other fields. In this system, when a user inputs a problem they are having into an app, an AI abstracts the problem and presents it as a fictitious case within the community. Next, community participants and autonomous AI propose and share solutions to the fictitious problem, helping the user solve their problem. This approach allows for widespread solicitation of answers, even for sensitive issues such as patent issues or confidential matters, helping to resolve them. Although the answers are for fictitious cases, they can lead to new ideas and direct solutions to the original problem. This allows people from around the world to brainstorm on the same platform and obtain diverse answers. Furthermore, participants are rewarded, fostering an active community. Users can be inspired by simply viewing other participants' ideas, and they can also submit their own ideas, ensuring constant new discoveries. In this way, the participatory problem-solving system efficiently abstracts users' problems and enables the community to propose, evaluate, and manage rewards for solutions.

[0075] A participatory problem-solving system according to an embodiment includes a receiving unit, an abstraction unit, a proposal unit, a proposal unit, an evaluation unit, and a reward unit. The receiving unit receives a problem from a user. Examples of user problems include, but are not limited to, technical problems, business problems, and user experience problems. The receiving unit receives the problem by, for example, inputting a user's problem with an app. The receiving unit can also receive problems in various formats, such as voice input or image input. For example, when a user inputs a problem by voice, the receiving unit analyzes the voice data and converts it into text data. When a user inputs a problem by image, the receiving unit analyzes the image data and extracts the content of the problem. The abstraction unit abstracts the problem received by the receiving unit. Abstraction is performed, for example, by extracting elements of the problem and adjusting the level of abstraction, but is not limited to, for example. For example, the abstraction unit uses natural language processing technology to extract the elements of the problem. To adjust the level of abstraction, the abstraction unit can also adjust the level of detail of the abstraction based on the importance or category of the problem. The raising unit raises the problem abstracted by the abstraction unit to the community. The raising is performed, for example, by adjusting a notification method to the community or the timing of the raising, but is not limited to such examples. For example, the raising unit raises the problem by sending an email or a push notification to the community. The raising unit can also adjust the method of raising the problem based on a user's feelings or the importance of the problem in order to adjust the timing of the raising. The suggestion unit proposes a solution to the problem raised by the raising unit. The proposal is performed, for example, by adjusting a proposal format or evaluation criteria, but is not limited to such examples. For example, the suggestion unit provides a guide for the user to input the proposal. The suggestion unit can also adjust the level of detail of the proposal based on a user's feelings or the importance of the problem in order to adjust the evaluation criteria of the proposal. The evaluation unit evaluates the solution proposed by the proposal unit. The evaluation is performed, for example, by adjusting the evaluation scale or process, but is not limited to such examples. For example, the evaluation unit provides a guide for the user to input the evaluation.The evaluation unit can also adjust the evaluation criteria based on the user's emotions and the importance of the problem in order to adjust the evaluation process. The reward unit manages rewards based on the results of the evaluation by the evaluation unit. Rewards are given, for example, by adjusting the form of the reward or the distribution method, but are not limited to such examples. For example, the reward unit provides a guide for users to receive rewards. The reward unit can also adjust the level of detail of the reward based on the user's emotions and the importance of the problem in order to adjust the reward distribution method. This makes it possible for the participatory problem-solving system according to the embodiment to efficiently abstract users' problems and enable solutions to be proposed, evaluated, and rewards managed in a community.

[0076] The participatory problem-solving system includes a guide unit that enables users to effectively input problems. The guide unit supports users in effectively inputting problems. The guide unit assists users in inputting problems, for example, by providing user interface design and input guide content. For example, the guide unit displays input fields and input examples when the user inputs a problem. The guide unit can also analyze the content entered by the user and provide appropriate feedback. For example, the guide unit points out areas for improvement in the input content based on the content entered by the user. The guide unit can also provide related information based on the content entered by the user. For example, the guide unit displays past solutions and reference materials related to the problem entered by the user. This allows the user to effectively input problems. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without AI. For example, the guide unit can input the content entered by the user to a generation AI and have the generation AI analyze the input content and provide feedback.

[0077] The participatory problem-solving system includes a feedback unit that shares feedback within the community. The feedback unit promotes improvement of solutions by sharing feedback within the community. The feedback unit supports sharing feedback within the community, for example, by providing a feedback format and a sharing method. For example, the feedback unit provides a guide for users to input feedback. The feedback unit can also analyze the content of the feedback and provide appropriate feedback. For example, the feedback unit points out areas for improvement in the feedback content based on the feedback input by the user. The feedback unit can also provide related information based on the feedback content. For example, the feedback unit displays past solutions and reference materials related to the feedback input by the user. This allows feedback to be shared within the community and promotes improvement of solutions. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input feedback input by a user to a generation AI and cause the generation AI to analyze the feedback content and provide feedback.

[0078] The participatory problem-solving system includes a transparency management unit for ensuring the transparency of the reward system. The transparency management unit provides a method for disclosing reward allocation and transparency evaluation criteria to ensure the transparency of the reward system. The transparency management unit ensures the transparency of the reward system, for example, by disclosing details of reward allocation. For example, the transparency management unit discloses the reward allocation process and criteria, allowing users to check the reward allocation details. The transparency management unit can also evaluate the transparency of the reward system by providing transparency evaluation criteria. For example, the transparency management unit provides indicators and evaluation methods for evaluating the transparency of reward allocation. This ensures the transparency of the reward system. Some or all of the above-mentioned processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit may input details of reward allocation to a generation AI and have the generation AI perform a transparency evaluation.

[0079] The guide unit can cooperate with the reception unit to support the user in effectively entering a problem. The guide unit cooperates with the reception unit to assist the user in entering a problem. For example, the guide unit analyzes the user's input received from the reception unit and provides appropriate feedback. The guide unit can also cooperate with the reception unit to provide related information based on the user's input. For example, the guide unit displays past solutions and reference materials based on the user's input received from the reception unit. This allows the user to effectively enter a problem. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's input received from the reception unit into a generation AI and have the generation AI analyze the input and provide feedback. This supports the user in effectively entering a problem.

[0080] The feedback unit can work with the proposal unit and the evaluation unit to share improvements to the proposed solution. The feedback unit works with the proposal unit and the evaluation unit to share improvements to the proposed solution. For example, the feedback unit analyzes the proposal received from the proposal unit and identifies improvements based on the evaluation results received from the evaluation unit. The feedback unit can also work with the proposal unit and the evaluation unit to provide a guide for sharing the improvements. For example, the feedback unit displays feedback from the evaluation unit based on the proposal received from the proposal unit. This allows improvements to the proposed solution to be shared, improving the quality of the solution. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the proposal received from the proposal unit and the evaluation results received from the evaluation unit into the generation AI, and cause the generation AI to identify and share improvements.

[0081] The transparency management unit can work with the reward unit to ensure the transparency of the reward system. The transparency management unit works with the reward unit to ensure the transparency of the reward system. For example, the transparency management unit analyzes the details of the reward allocation received from the reward unit and evaluates them based on transparency evaluation criteria. The transparency management unit can also work with the reward unit to provide a guide for ensuring transparency. For example, the transparency management unit publishes the details of the reward allocation received from the reward unit so that users can check the details of the reward allocation. This ensures the transparency of the reward system. Some or all of the above-mentioned processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit may input the details of the reward allocation received from the reward unit into the generation AI and cause the generation AI to evaluate the transparency.

[0082] The reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. For example, if the reception unit is stressed, it can delay the timing of question acceptance to provide the user with time to relax. Furthermore, if the user is relaxed, the reception unit can immediately accept questions and respond quickly. Furthermore, if the user is in a hurry, the reception unit can advance the timing of question acceptance to quickly process the questions. This adjusts the timing of question acceptance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation and adjustment of the timing of question acceptance.

[0083] The reception unit can analyze the user's past question submission history and select the optimal reception method. The reception unit analyzes the user's past question submission history and selects the optimal reception method. For example, the reception unit analyzes the patterns of questions that the user has frequently submitted in the past and suggests the optimal reception method. The reception unit can also prioritize and suggest reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, the optimal reception method is selected based on the user's past question submission history. Some 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 question submission history data into a generation AI and have the generation AI select the optimal reception method.

[0084] The reception unit may filter questions based on the user's current project or area of ​​interest when receiving the questions. The reception unit may filter questions based on the user's current project or area of ​​interest when receiving the questions. For example, the reception unit may preferentially receive questions related to the project the user is currently working on. The reception unit may also filter and receive highly relevant questions based on the user's area of ​​interest. Furthermore, the reception unit may also receive questions at an appropriate time depending on the progress of the user's project. This allows questions to be filtered based on the user's current project or area of ​​interest. Some or all of the above-described 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 data on the user's project or area of ​​interest into a generation AI and have the generation AI perform the filtering.

[0085] The reception unit can estimate the user's emotions and determine the priority of questions to be accepted based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of questions to be accepted based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can postpone questions of low importance. Furthermore, if the user is relaxed, the reception unit can also prioritize questions of high importance. Furthermore, if the user is in a hurry, the reception unit can also prioritize questions of high urgency. In this way, the priority of questions is determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and determine the priority of questions.

[0086] When accepting questions, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. When accepting questions, the reception unit prioritizes accepting highly relevant questions by taking into account the user's geographical location information. For example, the reception unit prioritizes accepting questions related to the user's current location. The reception unit can also filter and accept region-specific questions based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can prioritize accepting questions closest to the user's current location. This allows highly relevant questions to be preferentially accepted based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter highly relevant questions.

[0087] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving related questions. The reception unit can also filter and receive highly relevant questions based on the activity of accounts the user follows. Furthermore, the reception unit can analyze the user's interests on social media and receive related questions. In this way, related questions are received based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to filter related questions.

[0088] The abstraction unit can estimate the user's emotion and adjust the abstraction expression method based on the estimated user's emotion. The abstraction unit can estimate the user's emotion and adjust the abstraction expression method based on the estimated user's emotion. For example, if the user is stressed, the abstraction unit can adopt a simple and easy-to-understand expression method. If the user is relaxed, the abstraction unit can also adopt a detailed expression method. If the user is in a hurry, the abstraction unit can also adopt a concise expression method that focuses on the main points. In this way, the abstraction expression method is adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 abstraction unit can be performed using, for example, an AI, or without an AI. For example, the abstraction unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the abstraction expression method.

[0089] The abstraction unit can adjust the level of detail of the abstraction based on the importance of the problem during abstraction. The abstraction unit adjusts the level of detail of the abstraction based on the importance of the problem during abstraction. For example, the abstraction unit performs detailed abstraction for problems of high importance. The abstraction unit can also perform simple abstraction for problems of low importance. Furthermore, the abstraction unit can gradually adjust the level of detail of the abstraction according to the importance of the problem. In this way, the level of detail of the abstraction is adjusted according to the importance of the problem. Some or all of the above-mentioned processing in the abstraction unit may be performed using AI, for example, or may be performed without using AI. For example, the abstraction unit can input problem importance data to the generation AI and cause the generation AI to adjust the level of detail of the abstraction.

[0090] The abstraction unit can apply different abstraction algorithms depending on the problem category during abstraction. The abstraction unit applies different abstraction algorithms depending on the problem category during abstraction. For example, the abstraction unit applies a technically specialized abstraction algorithm to a technical problem. The abstraction unit can also apply a business-specialized abstraction algorithm to a business-related problem. Furthermore, the abstraction unit can apply a social problem-specialized abstraction algorithm to a social problem. In this way, an appropriate abstraction algorithm is applied depending on the problem category. Some or all of the above-mentioned processing in the abstraction unit may be performed using AI, for example, or may be performed without using AI. For example, the abstraction unit can input problem category data to a generation AI and cause the generation AI to apply an abstraction algorithm.

[0091] The abstraction unit can estimate the user's emotion and adjust the length of the abstraction based on the estimated user's emotion. The abstraction unit can estimate the user's emotion and adjust the length of the abstraction based on the estimated user's emotion. For example, if the user is stressed, the abstraction unit can provide a short, concise abstraction. If the user is relaxed, the abstraction unit can provide a longer abstraction with detailed information. If the user is in a hurry, the abstraction unit can provide a concise, short abstraction. This adjusts the length of the abstraction according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 abstraction unit can be performed using, for example, an AI, or without an AI. For example, the abstraction unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the length of the abstraction.

[0092] The abstraction unit can determine the priority of abstraction based on the time of submission of the questions during abstraction. The abstraction unit determines the priority of abstraction based on the time of submission of the questions during abstraction. For example, the abstraction unit prioritizes abstraction of recently submitted questions. The abstraction unit can also postpone questions that were submitted earlier. Furthermore, the abstraction unit can gradually adjust the priority of abstraction according to the time of submission. In this way, the priority of abstraction is determined based on the time of submission of the questions. Some or all of the above-mentioned processing in the abstraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the abstraction unit can input question submission time data into the generation AI and cause the generation AI to determine the priority of abstraction.

[0093] The abstraction unit can adjust the order of abstraction based on the relevance of the problems during abstraction. The abstraction unit adjusts the order of abstraction based on the relevance of the problems during abstraction. For example, the abstraction unit prioritizes abstraction of highly relevant problems. The abstraction unit can also postpone abstraction of less relevant problems. Furthermore, the abstraction unit can gradually adjust the order of abstraction according to the relevance of the problems. In this way, the order of abstraction is adjusted based on the relevance of the problems. Some or all of the above-described processing in the abstraction unit may be performed using AI, for example, or may be performed without using AI. For example, the abstraction unit can input problem relevance data to a generation AI and cause the generation AI to adjust the order of abstraction.

[0094] The suggestion unit can estimate the user's emotions and adjust the suggestion method based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the suggestion method based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can adopt a simple and easy-to-understand suggestion method. If the user is relaxed, the suggestion unit can adopt a suggestion method that includes detailed information. If the user is in a hurry, the suggestion unit can adopt a concise suggestion method that focuses on the main points. In this way, the suggestion method is adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the suggestion method.

[0095] The raising unit can adjust the level of detail of the proposal based on the importance of the problem when raising the proposal. The raising unit adjusts the level of detail of the proposal based on the importance of the problem when raising the proposal. For example, the raising unit makes a detailed proposal for a problem of high importance. The raising unit can also make a concise proposal for a problem of low importance. Furthermore, the raising unit can gradually adjust the level of detail of the proposal according to the importance of the problem. In this way, the level of detail of the proposal is adjusted according to the importance of the problem. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input problem importance data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0096] The raising unit can apply different raising algorithms depending on the problem category when raising a problem. The raising unit applies different raising algorithms depending on the problem category when raising a problem. For example, the raising unit applies a technical raising algorithm to a technical problem. The raising unit can also apply a business raising algorithm to a business-related problem. The raising unit can also apply a social problem raising algorithm to a social problem. In this way, an appropriate raising algorithm is applied depending on the problem category. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input problem category data to a generation AI and cause the generation AI to apply a raising algorithm.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with detailed information. If the user is in a hurry, the suggestion unit can make a concise and short suggestion. In this way, the length of the suggestion is adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the length of the suggestion.

[0098] The raising unit can determine the priority of the raising based on when the problem was submitted when it was raised. The raising unit determines the priority of the raising based on when the problem was submitted when it was raised. For example, the raising unit prioritizes raising recently submitted problems. The raising unit can also postpone older submitted problems. Furthermore, the raising unit can gradually adjust the priority of the raising based on when it was submitted. In this way, the priority of the raising is determined based on when the problem was submitted. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input data on when the problem was submitted to the generation AI and have the generation AI determine the priority of the raising.

[0099] The raising unit can adjust the order of raising based on the relevance of the problems when raising them. The raising unit adjusts the order of raising based on the relevance of the problems when raising them. For example, the raising unit prioritizes raising highly relevant problems. The raising unit can also postpone less relevant problems. Furthermore, the raising unit can gradually adjust the order of raising based on the relevance of the problems. In this way, the order of raising is adjusted based on the relevance of the problems. Some or all of the above-mentioned processing in the raising unit may be performed using AI, for example, or may be performed without using AI. For example, the raising unit can input problem relevance data into a generation AI and cause the generation AI to adjust the order of raising.

[0100] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is stressed, the suggestion unit can adopt a simple and easy-to-understand expression. If the user is relaxed, the suggestion unit can also adopt a expression that includes detailed information. If the user is in a hurry, the suggestion unit can also adopt a concise expression that focuses on the main points. This adjusts the way the suggestion is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the way the suggestion is expressed.

[0101] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making the proposal. The proposal unit adjusts the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a concise proposal for a solution with low importance. Furthermore, the proposal unit can gradually adjust the level of detail of the proposal according to the importance of the solution. In this way, the level of detail of the proposal is adjusted according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0102] The proposal unit can apply different proposal algorithms depending on the category of the solution when making a proposal. The proposal unit can apply different proposal algorithms depending on the category of the solution when making a proposal. For example, the proposal unit can apply a technical proposal algorithm to a technical solution. The proposal unit can also apply a business proposal algorithm to a business-related solution. The proposal unit can also apply a social problem proposal algorithm to a social solution. In this way, an appropriate proposal algorithm is applied depending on the category of the solution. Some or all of the above-mentioned processing in the proposal unit can be performed using AI, for example, or can be performed without using AI. For example, the proposal unit can input solution category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0103] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is stressed, the suggestion unit can provide a short, concise suggestion with detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise, short suggestion. In this way, the length of the suggestion is adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the length of the suggestion.

[0104] The proposal unit can determine the priority of proposals based on the time of submission of solutions when making proposals. The proposal unit determines the priority of proposals based on the time of submission of solutions when making proposals. For example, the proposal unit prioritizes proposals based on the time of submission of solutions. The proposal unit can also postpone solutions that were submitted earlier. Furthermore, the proposal unit can gradually adjust the priority of proposals according to the time of submission. In this way, the priority of proposals is determined based on the time of submission of solutions. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input solution submission time data into the generation AI and cause the generation AI to determine the priority of proposals.

[0105] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. The suggestion unit adjusts the order of proposals based on the relevance of the solutions when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant solutions. The suggestion unit can also postpone less relevant solutions. Furthermore, the suggestion unit can gradually adjust the order of proposals according to the relevance of the solutions. In this way, the order of proposals is adjusted based on the relevance of the solutions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the solutions to the generation AI and cause the generation AI to adjust the order of proposals.

[0106] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can relax the evaluation criteria and perform the evaluation. The evaluation unit can also apply strict evaluation criteria when the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can simplify the criteria to perform the evaluation quickly. In this way, the evaluation criteria are adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI estimate emotions and adjust the evaluation criteria.

[0107] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between solutions during evaluation. The evaluation unit improves the accuracy of the evaluation by taking into account the interrelationships between solutions during evaluation. For example, the evaluation unit analyzes the relevance between solutions and highly rates solutions that complement each other. The evaluation unit can also eliminate overlapping solutions by taking into account the interrelationships between solutions. Furthermore, the evaluation unit can evaluate the most effective combination based on the interrelationships between solutions. In this way, the accuracy of the evaluation is improved by taking into account the interrelationships between solutions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the interrelationships between solutions into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0108] The evaluation unit can perform the evaluation taking into account the attribute information of the submitter of the solution when performing the evaluation. The evaluation unit can perform the evaluation taking into account the attribute information of the submitter of the solution when performing the evaluation. For example, the evaluation unit performs the evaluation taking into account the submitter's expertise and experience. The evaluation unit can also prioritize evaluation of highly reliable solutions based on the submitter's past evaluation history. Furthermore, the evaluation unit can perform the evaluation taking into account diversity based on the submitter's attribute information. This improves the accuracy of the evaluation by taking into account the attribute information of the submitter of the solution. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submitter's attribute information data into the generation AI and have the generation AI perform the evaluation.

[0109] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user emotions. The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize displaying positive evaluation results. Furthermore, if the user is relaxed, the evaluation unit can also display detailed evaluation results. Furthermore, if the user is in a hurry, the evaluation unit can prioritize displaying evaluation results that highlight the main points. In this way, the order in which the evaluation results are displayed is adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input user emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the display order of the evaluation results.

[0110] The evaluation unit can perform the evaluation taking into account the geographical distribution of the solutions. The evaluation unit performs the evaluation taking into account the geographical distribution of the solutions. For example, the evaluation unit may highly evaluate solutions that cover a wide geographical area. The evaluation unit may also preferentially evaluate solutions that address problems that are specific to a region. Furthermore, the evaluation unit can evaluate diverse solutions taking into account the geographical distribution. This improves the accuracy of the evaluation by taking into account the geographical distribution of the solutions. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input geographical distribution data of the solutions into the generation AI and have the generation AI perform the evaluation.

[0111] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the solution during the evaluation. The evaluation unit improves the accuracy of the evaluation by referring to literature related to the solution during the evaluation. For example, the evaluation unit performs the evaluation by referring to academic papers related to the solution. The evaluation unit can also perform the evaluation by referring to patent documents related to the solution. Furthermore, the evaluation unit can perform the evaluation by referring to industry reports related to the solution. In this way, the accuracy of the evaluation is improved by referring to literature related to the solution. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input literature data related to the solution into the generation AI and have the generation AI perform the evaluation.

[0112] The reward unit can estimate the user's emotions and adjust the reward allocation method based on the estimated user emotions. The reward unit can estimate the user's emotions and adjust the reward allocation method based on the estimated user emotions. For example, if the user is feeling stressed, the reward unit can increase the reward to increase motivation. The reward unit can also perform normal reward allocation if the user is relaxed. Furthermore, the reward unit can quickly allocate rewards if the user is in a hurry. This adjusts the reward allocation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 reward unit can be performed using an AI, for example, or without an AI. For example, the reward unit can input user emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the reward allocation method.

[0113] The reward unit can adjust the level of detail of the reward based on the evaluation result when allocating the reward. The reward unit adjusts the level of detail of the reward based on the evaluation result when allocating the reward. For example, the reward unit allocates detailed rewards to highly rated solutions. The reward unit can also allocate simple rewards to low rated solutions. Furthermore, the reward unit can gradually adjust the level of detail of the reward according to the evaluation result. In this way, the level of detail of the reward is adjusted based on the evaluation result. Some or all of the above-mentioned processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input the evaluation result data to the generation AI and cause the generation AI to adjust the level of detail of the reward.

[0114] The reward unit can apply different reward allocation algorithms depending on the category of the solution when allocating rewards. The reward unit applies different reward allocation algorithms depending on the category of the solution when allocating rewards. For example, the reward unit applies a technology-specialized reward allocation algorithm to a technical solution. The reward unit can also apply a business-specialized reward allocation algorithm to a business-related solution. Furthermore, the reward unit can apply a social problem-specialized reward allocation algorithm to a social solution. In this way, an appropriate reward allocation algorithm is applied depending on the category of the solution. Some or all of the above-mentioned processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input solution category data to the generation AI and cause the generation AI to apply the reward allocation algorithm.

[0115] The reward unit can estimate the user's emotions and adjust the reward distribution order based on the estimated user emotions. The reward unit can estimate the user's emotions and adjust the reward distribution order based on the estimated user emotions. For example, if the user is feeling stressed, the reward unit can distribute rewards early. If the user is relaxed, the reward unit can also distribute rewards in the normal distribution order. Furthermore, if the user is in a hurry, the reward unit can also distribute rewards quickly. In this way, the reward distribution order is adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 reward unit may be performed using an AI, for example, or without an AI. For example, the reward unit can input user emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the reward distribution order.

[0116] The reward unit can determine the priority of rewards based on the time of submission of solutions when allocating rewards. The reward unit determines the priority of rewards based on the time of submission of solutions when allocating rewards. For example, the reward unit preferentially allocates rewards to recently submitted solutions. The reward unit can also postpone solutions that have been submitted earlier. Furthermore, the reward unit can gradually adjust the priority of rewards according to the time of submission. In this way, the priority of rewards is determined based on the time of submission of solutions. Some or all of the above-mentioned processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input solution submission time data into the generation AI and have the generation AI determine the priority of rewards.

[0117] The reward unit can adjust the reward allocation based on the relevance of the solution when allocating the reward. The reward unit adjusts the reward allocation based on the relevance of the solution when allocating the reward. For example, the reward unit preferentially allocates rewards to highly relevant solutions. The reward unit can also postpone solutions with low relevance. Furthermore, the reward unit can gradually adjust the reward allocation according to the relevance of the solution. In this way, the reward allocation is adjusted based on the relevance of the solution. Some or all of the above-described processing in the reward unit may be performed using AI, for example, or may be performed without using AI. For example, the reward unit can input relevance data of the solution to the generation AI and cause the generation AI to adjust the reward allocation.

[0118] The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated user's emotions. The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the guide unit can adopt a simple and easy-to-understand presentation. If the user is relaxed, the guide unit can also adopt a presentation that includes detailed information. If the user is in a hurry, the guide unit can also adopt a concise presentation that focuses on the main points. This allows the way the guide is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the guide unit can be performed using, for example, an AI, or without an AI. For example, the guide unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the way the guide is presented.

[0119] The guide unit can adjust the level of detail of the guide based on the importance of the question during guidance. The guide unit can adjust the level of detail of the guide based on the importance of the question during guidance. For example, the guide unit provides detailed guidance for questions of high importance. The guide unit can also provide brief guidance for questions of low importance. Furthermore, the guide unit can gradually adjust the level of detail of the guide depending on the importance of the question. In this way, the level of detail of the guide is adjusted depending on the importance of the question. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can input question importance data to a generation AI and cause the generation AI to adjust the level of detail of the guide.

[0120] The guide unit can estimate the user's emotions and adjust the length of the guidance based on the estimated user emotions. The guide unit can estimate the user's emotions and adjust the length of the guidance based on the estimated user emotions. For example, if the user is feeling stressed, the guide unit can provide short, to-the-point guidance. If the user is relaxed, the guide unit can provide longer guidance with detailed information. If the user is in a hurry, the guide unit can provide concise, short guidance. This adjusts the length of the guidance according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guide unit can be performed using, for example, an AI, or without an AI. For example, the guide unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the length of the guidance.

[0121] The guide unit can determine the priority of guides based on the time when the questions were submitted during guidance. The guide unit determines the priority of guides based on the time when the questions were submitted during guidance. For example, the guide unit prioritizes guidance for recently submitted questions. The guide unit can also postpone questions that were submitted earlier. Furthermore, the guide unit can gradually adjust the priority of guides according to the time of submission. In this way, the priority of guides is determined based on the time when the questions were submitted. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can input question submission time data into the generation AI and have the generation AI determine the priority of guides.

[0122] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, if the user is stressed, the feedback unit can adopt a simple and easy-to-understand expression method. If the user is relaxed, the feedback unit can also adopt a expression method that includes detailed information. If the user is in a hurry, the feedback unit can also adopt a concise expression method that focuses on the main points. In this way, the feedback expression method is adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the feedback expression method.

[0123] The feedback unit can adjust the level of detail of the feedback based on the importance of the solution when providing feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the solution when providing feedback. For example, the feedback unit provides detailed feedback for solutions with high importance. The feedback unit can also provide brief feedback for solutions with low importance. Furthermore, the feedback unit can gradually adjust the level of detail of the feedback according to the importance of the solution. In this way, the level of detail of the feedback is adjusted according to the importance of the solution. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0124] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, if the user is stressed, the feedback unit can provide short, to-the-point feedback. If the user is relaxed, the feedback unit can provide longer feedback with detailed information. If the user is in a hurry, the feedback unit can provide concise, short feedback. In this way, the length of the feedback is adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the length of the feedback.

[0125] The feedback unit can determine the priority of feedback based on the time of submission of a solution when providing feedback. The feedback unit determines the priority of feedback based on the time of submission of a solution when providing feedback. For example, the feedback unit prioritizes feedback for recently submitted solutions. The feedback unit can also postpone solutions that were submitted earlier. Furthermore, the feedback unit can gradually adjust the priority of feedback depending on the time of submission. In this way, the priority of feedback is determined based on the time of submission of a solution. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the time of submission of a solution to the generation AI and cause the generation AI to determine the priority of feedback.

[0126] The transparency management unit can estimate the user's emotions and adjust the transparency expression method based on the estimated user emotions. The transparency management unit can estimate the user's emotions and adjust the transparency expression method based on the estimated user emotions. For example, if the user is stressed, the transparency management unit can adopt a simple and easy-to-understand transparency expression method. If the user is relaxed, the transparency management unit can also adopt a transparency expression method that includes detailed information. If the user is in a hurry, the transparency management unit can also adopt a concise transparency expression method that focuses on the main points. In this way, the transparency expression method is adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 transparency management unit can be performed using, for example, AI, or without AI. For example, the transparency management unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and adjustment of the transparency expression method.

[0127] The transparency management unit can adjust the level of transparency detail based on the importance of reward allocation during transparency management. The transparency management unit adjusts the level of transparency detail based on the importance of reward allocation during transparency management. For example, the transparency management unit performs detailed transparency management for reward allocations with high importance. The transparency management unit can also perform simple transparency management for reward allocations with low importance. Furthermore, the transparency management unit can gradually adjust the level of transparency detail according to the importance of reward allocation. In this way, the level of transparency detail is adjusted according to the importance of reward allocation. Some or all of the above-described processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit can input reward allocation importance data to the generation AI and cause the generation AI to adjust the level of transparency detail.

[0128] The transparency management unit can estimate the user's emotions and adjust the length of the transparency based on the estimated user's emotions. The transparency management unit can estimate the user's emotions and adjust the length of the transparency based on the estimated user's emotions. For example, if the user is stressed, the transparency management unit can express a short, concise transparency that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the transparency management unit can express a concise, short transparency. This adjusts the length of the transparency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 transparency management unit can be performed using AI, for example, or without AI. For example, the transparency management unit can input user's emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the length of the transparency.

[0129] During transparency management, the transparency management unit can determine transparency priorities based on the submission timing of reward allocations. During transparency management, the transparency management unit determines transparency priorities based on the submission timing of reward allocations. For example, the transparency management unit prioritizes transparency management for recently submitted reward allocations. The transparency management unit can also postpone reward allocations that have been submitted earlier. Furthermore, the transparency management unit can gradually adjust the priority of transparency management depending on the submission timing. In this way, transparency priorities are determined based on the submission timing of reward allocations. Some or all of the above-described processing in the transparency management unit may be performed using AI, for example, or may be performed without using AI. For example, the transparency management unit can input reward allocation submission timing data into the generation AI and have the generation AI determine the transparency priorities. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, abstraction unit, posing unit, suggestion unit, evaluation unit, reward unit, guide unit, feedback unit, transparency management unit, and emotion estimation function, 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 control unit 46A of the smart device 14 and accepts a problem from a user. The abstraction unit is realized by the specific processing unit 290 of the data processing device 12 and abstracts the accepted problem. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes the abstracted problem to the community. The suggestion unit is realized by the control unit 46A of the smart device 14 and proposes a solution to the proposed problem. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the proposed solution. The reward unit is realized by the specific processing unit 290 of the data processing device 12 and manages rewards based on the evaluation results. The guide unit is realized by the control unit 46A of the smart device 14 and supports the user in effectively inputting a problem. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and shares feedback within the community. The transparency management unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the transparency of the reward system. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the acceptance timing. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, abstraction unit, posing unit, suggestion unit, evaluation unit, reward unit, guide unit, feedback unit, transparency management unit, and emotion estimation function, 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 control unit 46A of the smart glasses 214 and receives a problem from a user. The abstraction unit is realized by the specific processing unit 290 of the data processing device 12 and abstracts the received problem. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes the abstracted problem to the community. The suggestion unit is realized by the control unit 46A of the smart glasses 214 and proposes a solution to the proposed problem. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the proposed solution. The reward unit is realized by the specific processing unit 290 of the data processing device 12 and manages rewards based on the evaluation result. The guide unit is realized by the control unit 46A of the smart glasses 214 and supports the user in effectively inputting a problem. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and shares feedback within the community. The transparency management unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the transparency of the reward system. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the acceptance timing. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, abstraction unit, posing unit, suggestion unit, evaluation unit, reward unit, guide unit, feedback unit, transparency management unit, and emotion estimation function, 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 control unit 46A of the headset type terminal 314 and accepts a problem from a user. The abstraction unit is realized by the specific processing unit 290 of the data processing device 12 and abstracts the accepted problem. The posing unit is realized by the specific processing unit 290 of the data processing device 12 and proposes the abstracted problem to the community. The suggestion unit is realized by the control unit 46A of the headset type terminal 314 and proposes a solution to the proposed problem. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the proposed solution. The reward unit is realized by the specific processing unit 290 of the data processing device 12 and manages rewards based on the evaluation results. The guide unit is realized by the control unit 46A of the headset type terminal 314 and supports the user in effectively inputting a problem. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and shares feedback within the community. The transparency management unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the transparency of the reward system. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the acceptance timing. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, abstraction unit, posing unit, suggestion unit, evaluation unit, reward unit, guide unit, feedback unit, transparency management unit, and emotion estimation function, 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 control unit 46A of the robot 414 and accepts a problem from a user. The abstraction unit is realized by the specific processing unit 290 of the data processing device 12 and abstracts the accepted problem. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes the abstracted problem to the community. The suggestion unit is realized by the control unit 46A of the robot 414 and proposes a solution to the proposed problem. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the proposed solution. The reward unit is realized by the specific processing unit 290 of the data processing device 12 and manages rewards based on the evaluation results. The guide unit is realized by the control unit 46A of the robot 414 and supports the user in effectively inputting a problem. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and shares feedback within the community. The transparency management unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the transparency of the reward system. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the acceptance timing.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The reception unit can analyze the user's past problem-solving history and automatically suggest similar problems. For example, if the user has solved a technical problem in the past, the reception unit can suggest related technical problems based on that history. Also, if the user has solved a business problem, the reception unit can similarly suggest related business problems. Furthermore, the reception unit can also suggest new problems that the user may be interested in based on the user's problem-solving history. This allows the user to take advantage of their own experience to tackle new problems.

[0132] The abstraction unit can estimate the user's emotions and adjust the abstraction process based on the estimated user's emotions. For example, if the user is stressed, the abstraction unit can provide a simple and easy-to-understand abstraction. If the user is relaxed, the abstraction unit can provide an abstraction that includes detailed information. Furthermore, if the user is in a hurry, the abstraction unit can provide a concise abstraction that focuses on the main points. In this way, the abstraction process is adjusted according to the user's emotions.

[0133] The notifying unit can adjust the timing of the notifying in consideration of the user's current activity status. For example, if the user is in a meeting, the notifying unit delays the timing of the notifying. Also, if the user is on a break, the notifying unit can immediately notify. Furthermore, if the user is on the move, the notifying unit can adjust the timing of the notifying to make the notifying at an appropriate time. In this way, the timing of the notifying is adjusted according to the user's activity status.

[0134] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make simple and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make concise suggestions that focus on the main points. In this way, the content of the suggestions is adjusted according to the user's emotions.

[0135] The evaluation unit can analyze the user's past evaluation history and adjust the evaluation criteria. For example, if the user has given strict evaluations in the past, the evaluation unit will evaluate based on those criteria. Also, if the user has given lenient evaluations in the past, the evaluation unit can similarly evaluate based on those criteria. Furthermore, the evaluation criteria can be adjusted in stages based on the user's evaluation history. In this way, the evaluation criteria are adjusted based on the user's past evaluation history.

[0136] The reward unit can estimate the user's emotion and adjust the form of the reward based on the estimated user's emotion. For example, if the user is feeling stressed, the reward unit can provide a reward immediately. If the user is feeling relaxed, the reward unit can provide a reward gradually. Furthermore, if the user is in a hurry, the reward can be provided quickly. In this way, the form of the reward is adjusted according to the user's emotion.

[0137] The feedback unit can analyze the user's past feedback history and adjust the content of the feedback. For example, if the user has provided detailed feedback in the past, the feedback unit can provide detailed feedback based on that criterion. Also, if the user has provided brief feedback in the past, the feedback unit can provide brief feedback based on that criterion as well. Furthermore, the content of the feedback can be adjusted in stages based on the user's feedback history. In this way, the content of the feedback is adjusted based on the user's past feedback history.

[0138] The transparency management unit can estimate the user's emotions and adjust the method of providing transparency information based on the estimated user's emotions. For example, if the user is feeling stressed, the transparency management unit can provide simple and easy-to-understand information. If the user is relaxed, the transparency management unit can provide detailed information. Furthermore, if the user is in a hurry, the transparency management unit can provide concise information that focuses on the main points. In this way, the method of providing transparency information is adjusted according to the user's emotions.

[0139] The guide unit can analyze the user's past guide usage history and suggest the optimal guide method. For example, if the user has used an audio guide in the past, the guide unit can suggest an audio guide based on that history. Also, if the user has used a text guide in the past, the guide unit can suggest a text guide based on that history as well. Furthermore, the guide method that the user can use most effectively can also be suggested based on the user's guide usage history. In this way, the optimal guide method is suggested based on the user's past guide usage history.

[0140] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can recommend voice input. If the user is relaxed, the reception unit can recommend text input. Furthermore, if the user is in a hurry, the reception unit can recommend a simple input method. In this way, the method of receiving questions is adjusted according to the user's emotions.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The reception unit accepts user issues. User issues can include technical issues, business issues, and issues related to the user experience. The reception unit accepts issues by having the user input the problem they are having with the app. It can also accept issues in various formats, such as voice input or image input. For example, it can analyze voice data and convert it into text data, or analyze image data to extract the content of the problem. Step 2: The abstraction unit abstracts the problem received by the reception unit. Abstraction is performed by extracting the elements of the problem and adjusting the level of abstraction. For example, natural language processing technology can be used to extract the elements of the problem and adjust the level of abstraction based on the importance and category of the problem. Step 3: The problem-raising unit raises the problem abstracted by the abstraction unit to the community. The raising is performed by adjusting the notification method and timing to the community. For example, the problem is raised by sending an email or push notification, and the method of raising the problem is adjusted based on the user's sentiment and the importance of the problem. Step 4: The suggestion unit proposes solutions to the problems raised by the problem raising unit. The proposals are made by adjusting the format and evaluation criteria of the proposals. For example, the suggestion unit provides a guide for users to input their proposals, and adjusts the level of detail of the proposals based on the user's feelings and the importance of the problem. Step 5: The evaluator evaluates the solutions proposed by the proposer. The evaluation is performed by adjusting the evaluation scale and process. For example, the evaluator provides a guide for users to input their evaluations and adjusts the evaluation criteria based on the user's feelings and the importance of the problem. Step 6: The reward unit administers rewards based on the results evaluated by the evaluation unit. Rewards are administered by adjusting the reward format and distribution method. For example, it provides a guide for users to receive rewards and adjusts the level of detail of rewards based on the user's feelings and the importance of the problem.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0145] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] 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.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 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.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] 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.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] 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.

[0171] 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.

[0172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] 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.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0186] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0187] 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.

[0188] 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.

[0189] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0194] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0198] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0199] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0200] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0203] 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.

[0204] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0205] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0206] 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.

[0207] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0208] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0209] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0211] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0213] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; an abstraction unit that abstracts the problem accepted by the accepting unit; a submission unit that submits the problem abstracted by the abstraction unit to a community; a suggestion unit that proposes a solution to the problem raised by the proposal unit; an evaluation unit that evaluates the solution proposed by the proposal unit; a reward unit that manages rewards based on the results of the evaluation by the evaluation unit; A system characterized by:

2. Provides a guide section to help users input questions effectively 2. The system of claim 1.

3. Have a feedback section to share feedback within the community 2. The system of claim 1.

4. Establish a transparency management department to ensure transparency in the compensation system 2. The system of claim 1.

5. The guide portion is Cooperating with the reception unit, it supports users in inputting questions effectively.

3. The system of claim 2.

6. The feedback unit Collaborate with the Proposal Department and the Evaluation Department to share improvements to the proposed solutions 4. The system of claim 3.

7. The transparency management unit Cooperate with the Compensation Department to ensure transparency of the compensation system 5. The system of claim 4.

8. The reception unit Estimate the user's emotions and adjust the timing of accepting problems based on the estimated user emotions 2. The system of claim 1.

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

10. The reception unit Filtering issues based on your current projects and areas of interest 2. The system of claim 1.

11. The reception unit Estimate user emotions and prioritize accepted problems based on the estimated user emotions.

2. The system of claim 1.

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