system

Abstract

The system according to the embodiment aims to efficiently find solutions to the problems that arise. [Solution] A system according to an embodiment includes a reception unit, a collection unit, a hypothesis unit, a proposal unit, and a verification unit. The reception unit inputs a problem. The collection unit analyzes the problem input by the reception unit and collects related information. The hypothesis unit formulates a hypothesis based on the information collected by the collection unit. The proposal unit proposes potential solutions based on the hypothesis formulated by the hypothesis unit. The verification unit verifies the solutions proposed by the proposal 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 technologies have had the problem that the process for finding an appropriate solution to a problem is complicated, making it difficult to find a solution efficiently.

[0005] The system according to the embodiment aims to efficiently find solutions to the problems that arise. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a hypothesis unit, a proposal unit, and a verification unit. The reception unit inputs a problem. The collection unit analyzes the problem input by the reception unit and collects related information. The hypothesis unit formulates a hypothesis based on the information collected by the collection unit. The proposal unit proposes candidate solutions based on the hypothesis formulated by the hypothesis unit. The verification unit verifies the solutions proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently find solutions to the problems that are faced. [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 problem-solving support system according to an embodiment of the present invention provides a process for digging into problems and verifying hypotheses using SmartAI-Chat. This system includes a process in which a user inputs a problem, SmartAI-Chat analyzes the problem, collects related information, formulates hypotheses, proposes potential solutions, and verifies the solutions proposed by the user. For example, the system includes a reception unit where the user inputs the problem, a collection unit where the problem is analyzed and related information is collected, a hypothesis unit where a hypothesis is formulated based on the collected information, a proposal unit where a potential solution is proposed based on the hypotheses, and a verification unit where a solution proposed by the user is verified. These units are interrelated. For example, the hypothesis unit formulates a hypothesis based on the information collected by the collection unit, the proposal unit proposes a solution based on the hypothesis formulated by the hypothesis unit, and the verification unit verifies the solution proposed by the proposal unit. The collection unit collects similar past cases, related research papers, expert opinions, etc. The hypothesis unit formulates multiple hypotheses based on the collected information and clearly explains the rationale for each hypothesis. The proposal unit proposes potential solutions based on the hypotheses. The verification unit has the user actually try out solutions and run simulations to verify them based on their effectiveness, feasibility, cost, etc. This allows the user to obtain appropriate solution candidates even for problems they have never encountered before.This allows the problem-solving support system to obtain appropriate solution candidates even for problems they have never encountered before.

[0029] A problem-solving support system according to an embodiment includes a receiving unit, a collecting unit, a hypothesis unit, a proposing unit, and a verifying unit. The receiving unit receives a problem input by a user. For example, the user can input the problem in text format. The collecting unit analyzes the problem input by the receiving unit and collects related information. For example, the collecting unit collects similar past cases, related research papers, expert opinions, and the like. The hypothesis unit formulates a hypothesis based on the information collected by the collecting unit. For example, the hypothesis unit formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. The proposing unit proposes potential solutions based on the hypotheses formulated by the hypothesis unit. For example, the proposing unit proposes potential solutions based on the hypotheses. The verifying unit verifies the solutions proposed by the proposing unit. For example, the verifying unit verifies the solutions based on the effectiveness, feasibility, cost, and the like of solutions by having the user actually try them out or by running simulations. This allows the problem-solving support system according to an embodiment to obtain accurate potential solutions even for problems that the user has never experienced before.

[0030] The collection department can collect similar past cases, related research papers, and expert opinions. For example, the collection department can collect similar past cases through database searches and literature reviews. The collection department can also collect related research papers through academic databases and cited references. Furthermore, the collection department can collect expert opinions through interviews and questionnaire surveys. In this way, the accuracy of the hypothesis can be improved by the collection department collecting similar past cases, related research papers, expert opinions, etc.

[0031] The hypothesis section can formulate multiple hypotheses based on the collected information and clearly state the basis for each hypothesis. For example, the hypothesis section can formulate multiple hypotheses from different perspectives based on the collected information. The hypothesis section can also formulate hypotheses based on multiple assumptions of causal relationships. Furthermore, the hypothesis section can clearly state the basis for each hypothesis based on data analysis results and literature citations. By formulating multiple hypotheses, the hypothesis section can diversify potential solutions, making it easier to find the optimal solution.

[0032] The proposal unit can propose candidate solutions based on the hypothesis. The proposal unit can propose candidate solutions such as technical solutions or process improvements based on the hypothesis. The proposal unit can also clarify a method for proposing a specific solution based on the hypothesis. As a result, the proposal unit can propose candidate solutions based on the hypothesis, allowing the user to obtain a specific solution.

[0033] The verification unit can verify the solution based on its effectiveness, feasibility, cost, etc. by having the user actually try it out or by running a simulation. The verification unit can verify the solution based on its effectiveness, feasibility, cost, etc. by having the user actually try it out or by running a simulation. The verification unit can also verify the effectiveness of the solution through experiments or field tests. Furthermore, the verification unit can evaluate the feasibility of the solution through simulation. This allows the verification unit to select the optimal solution by verifying the solution based on its effectiveness, feasibility, cost, etc.

[0034] The reception unit can analyze the user's past assignment input history and suggest the optimal input format. For example, the reception unit can automatically display as candidates the assignment formats that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the assignment format to be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the reception unit can suggest the optimal input format to the user and improve input efficiency.

[0035] When inputting an assignment, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can preferentially display assignments related to the project the user is currently working on. The reception unit can also automatically filter related assignments based on the user's area of ​​interest. Furthermore, the reception unit can suggest appropriate assignments depending on the user's project progress. In this way, by filtering the input content based on the user's project or area of ​​interest, highly relevant assignments can be preferentially input.

[0036] When inputting a task, the reception unit can prioritize inputting highly relevant tasks by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying tasks related to that area. The reception unit can also suggest tasks that can utilize nearby resources based on the user's current location. Furthermore, the reception unit can prioritize displaying area-specific tasks based on the user's location information. This allows area-specific tasks to be input preferentially by taking into account the user's geographical location information.

[0037] When inputting a task, the reception unit can analyze the user's social media activity and input related tasks. The reception unit can, for example, analyze the content of the user's social media posts and suggest related tasks. The reception unit can also display related tasks based on the opinions of experts the user follows. Furthermore, the reception unit can suggest appropriate tasks based on the user's social media interests. In this way, by analyzing social media activity, tasks based on the user's interests can be input.

[0038] The collection unit can determine the priority of information based on the success rate of past similar cases when collecting information. For example, the collection unit can prioritize collecting information from past similar cases with a high success rate. The collection unit can also postpone information with a low success rate. Furthermore, the collection unit can prioritize collecting related information based on cases with a high success rate. This enables effective information collection by determining the priority of information based on the success rate of past similar cases.

[0039] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. The collection unit can, for example, prioritize collection of information from highly reliable information sources. Furthermore, the collection unit can postpone information with low reliability. Furthermore, the collection unit can evaluate the reliability of information and prioritize collection of highly reliable information. In this way, by evaluating the reliability of information, highly reliable information can be prioritized and collected.

[0040] The collection unit can determine the priority of collection based on the time of submission of information at the time of collection. For example, the collection unit can prioritize collection of the latest information. Also, the collection unit can postpone collection of older information. Furthermore, the collection unit can prioritize collection of information whose submission time is important. In this way, by determining the priority of collection based on the time of submission of information, it is possible to prioritize collection of the latest information.

[0041] The collection unit can adjust the order of collection based on the relevance of the information when collecting the information. For example, the collection unit can prioritize collecting information that is most relevant to the task. The collection unit can also postpone collecting less relevant information. Furthermore, the collection unit can evaluate the relevance of the information and prioritize collecting highly relevant information. In this way, by adjusting the order of collection based on the relevance of the information, highly relevant information can be collected with priority.

[0042] When formulating a hypothesis, the hypothesis unit can determine the priority of hypotheses based on the reliability of collected information. For example, the hypothesis unit can prioritize hypotheses based on highly reliable information. The hypothesis unit can also postpone information with low reliability. Furthermore, the hypothesis unit can evaluate the reliability of information and formulate hypotheses based on highly reliable information. In this way, by determining the priority of hypotheses based on the reliability of collected information, highly reliable hypotheses can be formulated with priority.

[0043] The hypothesis unit can apply different hypothesis generation algorithms depending on the category of information when generating hypotheses. For example, the hypothesis unit can apply a technical algorithm to hypotheses based on technical information. The hypothesis unit can also apply a market analysis algorithm to hypotheses based on market information. Furthermore, the hypothesis unit can apply a user behavior analysis algorithm to hypotheses based on user feedback. In this way, by applying different hypothesis generation algorithms depending on the category of information, appropriate hypotheses can be generated.

[0044] When formulating a hypothesis, the hypothesis unit can determine the priority of hypotheses based on the time of submission of information. The hypothesis unit can, for example, prioritize hypotheses based on the latest information. The hypothesis unit can also postpone older information. Furthermore, the hypothesis unit can formulate hypotheses based on information whose submission time is important. In this way, by determining the priority of hypotheses based on the time of submission of information, hypotheses based on the latest information can be formulated.

[0045] The hypothesis unit can adjust the order of hypotheses based on the relevance of information when formulating hypotheses. For example, the hypothesis unit can prioritize hypotheses based on information that is most relevant to the problem. The hypothesis unit can also postpone less relevant information. Furthermore, the hypothesis unit can evaluate the relevance of information and formulate hypotheses based on highly relevant information. Thus, by adjusting the order of hypotheses based on the relevance of information, highly relevant hypotheses can be formulated with priority.

[0046] The proposal unit can adjust the level of detail of the proposal based on the importance of the hypothesis when making a proposal. For example, the proposal unit can make a detailed proposal based on a hypothesis with high importance. Also, the proposal unit can make a concise proposal based on a hypothesis with low importance. Furthermore, the proposal unit can evaluate the importance of the hypotheses and prioritize proposals based on hypotheses with high importance. In this way, by adjusting the level of detail of the proposal based on the importance of the hypotheses, proposals based on important hypotheses can be prioritized.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the hypothesis when making a proposal. For example, the proposal unit can apply a technical algorithm to a proposal based on a technical hypothesis. The proposal unit can also apply a market analysis algorithm to a proposal based on a market hypothesis. The proposal unit can also apply a user behavior analysis algorithm to a proposal based on a user behavior hypothesis. This allows appropriate proposals to be made by applying different proposal algorithms depending on the category of the hypothesis.

[0048] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the hypothesis. For example, the proposal unit can give priority to proposals based on the latest hypothesis. Also, the proposal unit can postpone proposals based on older hypotheses. Furthermore, the proposal unit can give priority to proposals based on hypotheses whose submission time is important. In this way, by determining the priority of the proposal based on the time of submission of the hypothesis, it is possible to give priority to proposals based on the latest hypothesis.

[0049] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the hypotheses. For example, the proposal unit can prioritize proposals based on hypotheses that are most relevant to the problem. The proposal unit can also postpone proposals based on hypotheses that are less relevant. Furthermore, the proposal unit evaluates the relevance of hypotheses and prioritizes proposals based on hypotheses that are more relevant. In this way, by adjusting the order of proposals based on the relevance of hypotheses, proposals based on hypotheses that are more relevant can be prioritized.

[0050] During verification, the verification unit can select the optimal verification method by referring to past verification results. For example, the verification unit can preferentially select a method that has had good past verification results. Also, the verification unit can postpone a method that has had insufficient past verification results. Furthermore, the verification unit can select the optimal verification method based on past verification results. In this way, the optimal verification method can be selected by referring to past verification results.

[0051] The verification unit can customize the verification means based on the reliability of the hypothesis during verification. For example, the verification unit can preferentially select verification means based on a highly reliable hypothesis. The verification unit can also postpone verification means based on a less reliable hypothesis. Furthermore, the verification unit can evaluate the reliability of the hypothesis and select verification means based on a highly reliable hypothesis. In this way, by customizing the verification means based on the reliability of the hypothesis, verification based on a highly reliable hypothesis can be performed.

[0052] During verification, the verification unit can select the optimal verification method by taking into account the user's geographical location information. For example, if the user is in a specific area, the verification unit can provide a verification method suitable for that area. Furthermore, the verification unit can suggest a verification method that can utilize nearby resources based on the user's current location. Furthermore, the verification unit can provide a region-specific verification method based on the user's location information. In this way, a region-specific verification method can be provided by taking into account the user's geographical location information.

[0053] During verification, the verification unit can analyze the user's social media activities and suggest verification means. For example, the verification unit can analyze the content of the user's social media posts and suggest relevant verification means. The verification unit can also provide relevant verification means based on the opinions of experts the user follows. Furthermore, the verification unit can suggest appropriate verification means based on the user's social media interests. In this way, relevant verification means can be suggested by analyzing the user's social media activities.

[0054] During verification, the verification unit can select the optimal verification method by taking into account the user's geographical location information. For example, if the user is in a specific area, the verification unit can provide a verification method suitable for that area. Furthermore, the verification unit can suggest a verification method that can utilize nearby resources based on the user's current location. Furthermore, the verification unit can provide a region-specific verification method based on the user's location information. In this way, a region-specific verification method can be provided by taking into account the user's geographical location information.

[0055] During verification, the verification unit can analyze the user's social media activities and suggest verification means. For example, the verification unit can analyze the content of the user's social media posts and suggest relevant verification means. The verification unit can also provide relevant verification means based on the opinions of experts the user follows. Furthermore, the verification unit can suggest appropriate verification means based on the user's social media interests. In this way, relevant verification means can be suggested by analyzing the user's social media activities.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the user's past problem-solving history and automatically suggest solutions to similar problems. For example, it can refer to a database of problems the user has solved in the past and suggest solutions to similar problems. The reception unit can also evaluate the effectiveness of solutions the user has used in the past and preferentially suggest the most effective solutions. Furthermore, the reception unit can analyze the user's past tendency to select solutions and suggest solutions that suit the user's preferences. This allows the user to efficiently solve problems by leveraging their past experience.

[0058] The collection unit can prioritize collection of necessary information in consideration of the user's current project progress status. For example, depending on the stage of the project, basic information can be collected in the early stages, and detailed technical information can be collected in the later stages. The collection unit can also prioritize collection of the latest related research papers and technical reports based on the project progress status. Furthermore, the collection unit can evaluate risks associated with the project progress and information necessary for risk management. This makes it possible to collect appropriate information according to the project progress status.

[0059] The reception unit can prioritize input of region-specific tasks by taking into account the user's geographical location information. For example, if the user is in a specific region, tasks related to that region can be displayed with priority. The reception unit can also suggest tasks that utilize nearby resources based on the user's current location. Furthermore, the reception unit can prioritize display of region-specific tasks based on the user's location information. This allows region-specific tasks to be input with priority by taking into account the user's geographical location information.

[0060] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. For example, information from highly reliable information sources can be collected with priority. The collection unit can also postpone collection of less reliable information. Furthermore, the collection unit can evaluate the reliability of information and prioritize collection of highly reliable information. In this way, by evaluating the reliability of information, highly reliable information can be collected with priority.

[0061] When formulating a hypothesis, the hypothesis unit can apply different hypothesis formulation algorithms depending on the category of information. For example, a technical algorithm can be applied to a hypothesis based on technical information. The hypothesis unit can also apply a market analysis algorithm to a hypothesis based on market information. Furthermore, the hypothesis unit can apply a user behavior analysis algorithm to a hypothesis based on user feedback. In this way, by applying different hypothesis formulation algorithms depending on the category of information, appropriate hypotheses can be formulated.

[0062] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the hypothesis. For example, a proposal based on the latest hypothesis can be given priority. The proposal unit can also postpone proposals based on older hypotheses. Furthermore, the proposal unit can give priority to proposals based on hypotheses whose submission time is important. In this way, by determining the priority of the proposal based on the time of submission of the hypothesis, a proposal based on the latest hypothesis can be given priority.

[0063] During verification, the verification unit can select the optimal verification method by referring to past verification results. For example, it can prioritize the selection of a method that has had good past verification results. The verification unit can also postpone the selection of a method that has had insufficient past verification results. Furthermore, the verification unit can select the optimal verification method based on past verification results. This makes it possible to select the optimal verification method by referring to past verification results.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The user inputs the assignment into the reception unit. For example, the user can input the assignment in text format. Step 2: The collection unit analyzes the problem entered by the reception unit and collects related information. For example, the collection unit collects similar past cases, related research papers, expert opinions, etc. Step 3: The hypothesis unit formulates hypotheses based on the information collected by the collection unit. For example, the hypothesis unit formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. Step 4: The proposing unit proposes a candidate solution based on the hypothesis established by the hypothesis unit. For example, the proposing unit proposes a candidate solution based on the hypothesis. Step 5: The verification unit verifies the solution proposed by the proposal unit. For example, the verification unit may have users actually try it out or run simulations to verify the solution based on its effectiveness, feasibility, cost, etc.

[0066] (Example 2) A problem-solving support system according to an embodiment of the present invention provides a process for digging into problems and verifying hypotheses using SmartAI-Chat. This system includes a process in which a user inputs a problem, SmartAI-Chat analyzes the problem, collects related information, formulates hypotheses, proposes potential solutions, and verifies the solutions proposed by the user. For example, the system includes a reception unit where the user inputs the problem, a collection unit where the problem is analyzed and related information is collected, a hypothesis unit where a hypothesis is formulated based on the collected information, a proposal unit where a potential solution is proposed based on the hypotheses, and a verification unit where a solution proposed by the user is verified. These units are interrelated. For example, the hypothesis unit formulates a hypothesis based on the information collected by the collection unit, the proposal unit proposes a solution based on the hypothesis formulated by the hypothesis unit, and the verification unit verifies the solution proposed by the proposal unit. The collection unit collects similar past cases, related research papers, expert opinions, etc. The hypothesis unit formulates multiple hypotheses based on the collected information and clearly explains the rationale for each hypothesis. The proposal unit proposes potential solutions based on the hypotheses. The verification unit has the user actually try out solutions and run simulations to verify them based on their effectiveness, feasibility, cost, etc. This allows the user to obtain appropriate solution candidates even for problems they have never encountered before.This allows the problem-solving support system to obtain appropriate solution candidates even for problems they have never encountered before.

[0067] A problem-solving support system according to an embodiment includes a receiving unit, a collecting unit, a hypothesis unit, a proposing unit, and a verifying unit. The receiving unit receives a problem input by a user. For example, the user can input the problem in text format. The collecting unit analyzes the problem input by the receiving unit and collects related information. For example, the collecting unit collects similar past cases, related research papers, expert opinions, and the like. The hypothesis unit formulates a hypothesis based on the information collected by the collecting unit. For example, the hypothesis unit formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. The proposing unit proposes potential solutions based on the hypotheses formulated by the hypothesis unit. For example, the proposing unit proposes potential solutions based on the hypotheses. The verifying unit verifies the solutions proposed by the proposing unit. For example, the verifying unit verifies the solutions based on the effectiveness, feasibility, cost, and the like of solutions by having the user actually try them out or by running simulations. This allows the problem-solving support system according to an embodiment to obtain accurate potential solutions even for problems that the user has never experienced before.

[0068] The collection department can collect similar past cases, related research papers, and expert opinions. For example, the collection department can collect similar past cases through database searches and literature reviews. The collection department can also collect related research papers through academic databases and cited references. Furthermore, the collection department can collect expert opinions through interviews and questionnaire surveys. In this way, the accuracy of the hypothesis can be improved by the collection department collecting similar past cases, related research papers, expert opinions, etc.

[0069] The hypothesis section can formulate multiple hypotheses based on the collected information and clearly state the basis for each hypothesis. For example, the hypothesis section can formulate multiple hypotheses from different perspectives based on the collected information. The hypothesis section can also formulate hypotheses based on multiple assumptions of causal relationships. Furthermore, the hypothesis section can clearly state the basis for each hypothesis based on data analysis results and literature citations. By formulating multiple hypotheses, the hypothesis section can diversify potential solutions, making it easier to find the optimal solution.

[0070] The proposal unit can propose candidate solutions based on the hypothesis. The proposal unit can propose candidate solutions such as technical solutions or process improvements based on the hypothesis. The proposal unit can also clarify a method for proposing a specific solution based on the hypothesis. As a result, the proposal unit can propose candidate solutions based on the hypothesis, allowing the user to obtain a specific solution.

[0071] The verification unit can verify the solution based on its effectiveness, feasibility, cost, etc. by having the user actually try it out or by running a simulation. The verification unit can verify the solution based on its effectiveness, feasibility, cost, etc. by having the user actually try it out or by running a simulation. The verification unit can also verify the effectiveness of the solution through experiments or field tests. Furthermore, the verification unit can evaluate the feasibility of the solution through simulation. This allows the verification unit to select the optimal solution by verifying the solution based on its effectiveness, feasibility, cost, etc.

[0072] The reception unit can estimate the user's emotions and adjust the task input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to input the task quickly. In this way, adjusting the task input method according to the user's emotions reduces the burden on the user and enables the user to input the task efficiently.

[0073] The reception unit can analyze the user's past assignment input history and suggest the optimal input format. For example, the reception unit can automatically display as candidates the assignment formats that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the assignment format to be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the reception unit can suggest the optimal input format to the user and improve input efficiency.

[0074] When inputting an assignment, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can preferentially display assignments related to the project the user is currently working on. The reception unit can also automatically filter related assignments based on the user's area of ​​interest. Furthermore, the reception unit can suggest appropriate assignments depending on the user's project progress. In this way, by filtering the input content based on the user's project or area of ​​interest, highly relevant assignments can be preferentially input.

[0075] The reception unit can estimate the user's emotions and determine the priority of input tasks based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize displaying tasks with a high level of urgency. Furthermore, when the user is relaxed, the reception unit can prioritize displaying long-term tasks. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying tasks that can be solved in a short amount of time. In this way, by determining the priority of tasks according to the user's emotions, tasks can be processed according to their urgency and importance.

[0076] When inputting a task, the reception unit can prioritize inputting highly relevant tasks by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying tasks related to that area. The reception unit can also suggest tasks that can utilize nearby resources based on the user's current location. Furthermore, the reception unit can prioritize displaying area-specific tasks based on the user's location information. This allows area-specific tasks to be input preferentially by taking into account the user's geographical location information.

[0077] When inputting a task, the reception unit can analyze the user's social media activity and input related tasks. The reception unit can, for example, analyze the content of the user's social media posts and suggest related tasks. The reception unit can also display related tasks based on the opinions of experts the user follows. Furthermore, the reception unit can suggest appropriate tasks based on the user's social media interests. In this way, by analyzing social media activity, tasks based on the user's interests can be input.

[0078] The collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting concise and to-the-point information. Furthermore, when the user is relaxed, the collection unit can collect detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. In this way, by adjusting the type of information to be collected according to the user's emotions, appropriate information can be collected.

[0079] The collection unit can determine the priority of information based on the success rate of past similar cases when collecting information. For example, the collection unit can prioritize collecting information from past similar cases with a high success rate. The collection unit can also postpone information with a low success rate. Furthermore, the collection unit can prioritize collecting related information based on cases with a high success rate. This enables effective information collection by determining the priority of information based on the success rate of past similar cases.

[0080] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. The collection unit can, for example, prioritize collection of information from highly reliable information sources. Furthermore, the collection unit can postpone information with low reliability. Furthermore, the collection unit can evaluate the reliability of information and prioritize collection of highly reliable information. In this way, by evaluating the reliability of information, highly reliable information can be prioritized and collected.

[0081] The collection unit can estimate the user's emotions and adjust the amount of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can collect the minimum amount of information necessary. Furthermore, when the user is relaxed, the collection unit can collect detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. In this way, an appropriate amount of information can be collected by adjusting the amount of information to be collected according to the user's emotions.

[0082] The collection unit can determine the priority of collection based on the time of submission of information at the time of collection. For example, the collection unit can prioritize collection of the latest information. Also, the collection unit can postpone collection of older information. Furthermore, the collection unit can prioritize collection of information whose submission time is important. In this way, by determining the priority of collection based on the time of submission of information, it is possible to prioritize collection of the latest information.

[0083] The collection unit can adjust the order of collection based on the relevance of the information when collecting the information. For example, the collection unit can prioritize collecting information that is most relevant to the task. The collection unit can also postpone collecting less relevant information. Furthermore, the collection unit can evaluate the relevance of the information and prioritize collecting highly relevant information. In this way, by adjusting the order of collection based on the relevance of the information, highly relevant information can be collected with priority.

[0084] The hypothesis unit can estimate the user's emotions and adjust the way the hypotheses are formed based on the estimated user's emotions. For example, if the user is feeling stressed, the hypothesis unit can form a simple and clear hypothesis. Also, if the user is relaxed, the hypothesis unit can form a detailed hypothesis. Furthermore, if the user is in a hurry, the hypothesis unit can prioritize a hypothesis that can be formed quickly. In this way, by adjusting the way the hypotheses are formed according to the user's emotions, an appropriate hypothesis can be formed.

[0085] When formulating a hypothesis, the hypothesis unit can determine the priority of hypotheses based on the reliability of collected information. For example, the hypothesis unit can prioritize hypotheses based on highly reliable information. The hypothesis unit can also postpone information with low reliability. Furthermore, the hypothesis unit can evaluate the reliability of information and formulate hypotheses based on highly reliable information. In this way, by determining the priority of hypotheses based on the reliability of collected information, highly reliable hypotheses can be formulated with priority.

[0086] The hypothesis unit can apply different hypothesis generation algorithms depending on the category of information when generating hypotheses. For example, the hypothesis unit can apply a technical algorithm to hypotheses based on technical information. The hypothesis unit can also apply a market analysis algorithm to hypotheses based on market information. Furthermore, the hypothesis unit can apply a user behavior analysis algorithm to hypotheses based on user feedback. In this way, by applying different hypothesis generation algorithms depending on the category of information, appropriate hypotheses can be generated.

[0087] The hypothesis unit can estimate the user's emotions and adjust the level of detail of the hypothesis based on the estimated user's emotions. For example, if the user is feeling stressed, the hypothesis unit can create a concise and to-the-point hypothesis. Also, if the user is relaxed, the hypothesis unit can create a detailed hypothesis. Furthermore, if the user is in a hurry, the hypothesis unit can prioritize a hypothesis that can be created quickly. In this way, by adjusting the level of detail of the hypothesis according to the user's emotions, an appropriate hypothesis can be created.

[0088] When formulating a hypothesis, the hypothesis unit can determine the priority of hypotheses based on the time of submission of information. The hypothesis unit can, for example, prioritize hypotheses based on the latest information. The hypothesis unit can also postpone older information. Furthermore, the hypothesis unit can formulate hypotheses based on information whose submission time is important. In this way, by determining the priority of hypotheses based on the time of submission of information, hypotheses based on the latest information can be formulated.

[0089] The hypothesis unit can adjust the order of hypotheses based on the relevance of information when formulating hypotheses. For example, the hypothesis unit can prioritize hypotheses based on information that is most relevant to the problem. The hypothesis unit can also postpone less relevant information. Furthermore, the hypothesis unit can evaluate the relevance of information and formulate hypotheses based on highly relevant information. Thus, by adjusting the order of hypotheses based on the relevance of information, highly relevant hypotheses can be formulated with priority.

[0090] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple and clear suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide quick and understandable suggestions. In this way, by adjusting the way in which suggestions are expressed according to the user's emotions, appropriate suggestions can be made.

[0091] The proposal unit can adjust the level of detail of the proposal based on the importance of the hypothesis when making a proposal. For example, the proposal unit can make a detailed proposal based on a hypothesis with high importance. Also, the proposal unit can make a concise proposal based on a hypothesis with low importance. Furthermore, the proposal unit can evaluate the importance of the hypotheses and prioritize proposals based on hypotheses with high importance. In this way, by adjusting the level of detail of the proposal based on the importance of the hypotheses, proposals based on important hypotheses can be prioritized.

[0092] The proposal unit can apply different proposal algorithms depending on the category of the hypothesis when making a proposal. For example, the proposal unit can apply a technical algorithm to a proposal based on a technical hypothesis. The proposal unit can also apply a market analysis algorithm to a proposal based on a market hypothesis. The proposal unit can also apply a user behavior analysis algorithm to a proposal based on a user behavior hypothesis. This allows appropriate proposals to be made by applying different proposal algorithms depending on the category of the hypothesis.

[0093] 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, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can make a short, quickly understandable suggestion. In this way, by adjusting the length of the suggestion according to the user's emotions, appropriate suggestions can be made.

[0094] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the hypothesis. For example, the proposal unit can give priority to proposals based on the latest hypothesis. Also, the proposal unit can postpone proposals based on older hypotheses. Furthermore, the proposal unit can give priority to proposals based on hypotheses whose submission time is important. In this way, by determining the priority of the proposal based on the time of submission of the hypothesis, it is possible to give priority to proposals based on the latest hypothesis.

[0095] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the hypotheses. For example, the proposal unit can prioritize proposals based on hypotheses that are most relevant to the problem. The proposal unit can also postpone proposals based on hypotheses that are less relevant. Furthermore, the proposal unit evaluates the relevance of hypotheses and prioritizes proposals based on hypotheses that are more relevant. In this way, by adjusting the order of proposals based on the relevance of hypotheses, proposals based on hypotheses that are more relevant can be prioritized.

[0096] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated user's emotions. For example, the verification unit can provide a simple and quick verification method when the user is stressed. Furthermore, the verification unit can provide a detailed verification method when the user is relaxed. Furthermore, the verification unit can provide a verification method that can quickly obtain results when the user is in a hurry. In this way, appropriate verification can be performed by adjusting the verification method according to the user's emotions.

[0097] During verification, the verification unit can select the optimal verification method by referring to past verification results. For example, the verification unit can preferentially select a method that has had good past verification results. Also, the verification unit can postpone a method that has had insufficient past verification results. Furthermore, the verification unit can select the optimal verification method based on past verification results. In this way, the optimal verification method can be selected by referring to past verification results.

[0098] The verification unit can customize the verification means based on the reliability of the hypothesis during verification. For example, the verification unit can preferentially select verification means based on a highly reliable hypothesis. The verification unit can also postpone verification means based on a less reliable hypothesis. Furthermore, the verification unit can evaluate the reliability of the hypothesis and select verification means based on a highly reliable hypothesis. In this way, by customizing the verification means based on the reliability of the hypothesis, verification based on a highly reliable hypothesis can be performed.

[0099] The verification unit can estimate the user's emotions and determine the priority of verification based on the estimated user's emotions. For example, if the user is feeling stressed, the verification unit can prioritize verification with a high level of urgency. Furthermore, if the user is relaxed, the verification unit can prioritize detailed verification. Furthermore, if the user is in a hurry, the verification unit can prioritize verification that can provide results quickly. In this way, by determining the priority of verification according to the user's emotions, verification according to urgency and importance becomes possible.

[0100] During verification, the verification unit can select the optimal verification method by taking into account the user's geographical location information. For example, if the user is in a specific area, the verification unit can provide a verification method suitable for that area. Furthermore, the verification unit can suggest a verification method that can utilize nearby resources based on the user's current location. Furthermore, the verification unit can provide a region-specific verification method based on the user's location information. In this way, a region-specific verification method can be provided by taking into account the user's geographical location information.

[0101] During verification, the verification unit can analyze the user's social media activities and suggest verification means. For example, the verification unit can analyze the content of the user's social media posts and suggest relevant verification means. The verification unit can also provide relevant verification means based on the opinions of experts the user follows. Furthermore, the verification unit can suggest appropriate verification means based on the user's social media interests. In this way, relevant verification means can be suggested by analyzing the user's social media activities.

[0102] The verification unit can estimate the user's emotions and determine the priority of verification based on the estimated user's emotions. For example, if the user is feeling stressed, the verification unit can prioritize verification with a high level of urgency. Furthermore, if the user is relaxed, the verification unit can prioritize detailed verification. Furthermore, if the user is in a hurry, the verification unit can prioritize verification that can provide results quickly. In this way, by determining the priority of verification according to the user's emotions, verification according to urgency and importance becomes possible.

[0103] During verification, the verification unit can select the optimal verification method by taking into account the user's geographical location information. For example, if the user is in a specific area, the verification unit can provide a verification method suitable for that area. Furthermore, the verification unit can suggest a verification method that can utilize nearby resources based on the user's current location. Furthermore, the verification unit can provide a region-specific verification method based on the user's location information. In this way, a region-specific verification method can be provided by taking into account the user's geographical location information.

[0104] During verification, the verification unit can analyze the user's social media activities and suggest verification means. For example, the verification unit can analyze the content of the user's social media posts and suggest relevant verification means. The verification unit can also provide relevant verification means based on the opinions of experts the user follows. Furthermore, the verification unit can suggest appropriate verification means based on the user's social media interests. In this way, relevant verification means can be suggested by analyzing the user's social media activities. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, collection unit, hypothesis unit, proposal unit, and verification unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input the problem in text format. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects past similar cases, related research papers, expert opinions, etc. The hypothesis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes potential solutions based on the hypotheses. The verification unit is implemented, for example, by the control unit 46A of the smart device 14, and allows the user to actually try out solutions or perform simulations to verify the solutions based on their effectiveness, feasibility, cost, etc. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, collection unit, hypothesis unit, proposal unit, and verification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to input a problem in text format. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects past similar cases, related research papers, expert opinions, etc. The hypothesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes potential solutions based on the hypotheses. The verification unit is realized, for example, by the control unit 46A of the smart glasses 214, and allows a user to actually try out solutions or perform simulations to verify them based on their effectiveness, feasibility, cost, etc. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, collection unit, hypothesis unit, proposal unit, and verification unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset-type terminal 314, allowing the user to input the problem in text format. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects past similar cases, related research papers, expert opinions, etc. The hypothesis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes potential solutions based on the hypotheses. The verification unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and allows the user to actually try out solutions or perform simulations to verify them based on their effectiveness, feasibility, cost, etc. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, collection unit, hypothesis unit, proposal unit, and verification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to input a problem in text format. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects past similar cases, related research papers, expert opinions, etc. The hypothesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes potential solutions based on the hypotheses. The verification unit is realized, for example, by the control unit 46A of the robot 414, and verifies the effectiveness, feasibility, cost, etc. of solutions by having the user actually try them out or by running simulations.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can analyze the user's past problem-solving history and automatically suggest solutions to similar problems. For example, it can refer to a database of problems the user has solved in the past and suggest solutions to similar problems. The reception unit can also evaluate the effectiveness of solutions the user has used in the past and preferentially suggest the most effective solutions. Furthermore, the reception unit can analyze the user's past tendency to select solutions and suggest solutions that suit the user's preferences. This allows the user to efficiently solve problems by leveraging their past experience.

[0107] The collection unit can prioritize collection of necessary information in consideration of the user's current project progress status. For example, depending on the stage of the project, basic information can be collected in the early stages, and detailed technical information can be collected in the later stages. The collection unit can also prioritize collection of the latest related research papers and technical reports based on the project progress status. Furthermore, the collection unit can evaluate risks associated with the project progress and information necessary for risk management. This makes it possible to collect appropriate information according to the project progress status.

[0108] The hypothesis unit can estimate the user's emotions and adjust the way the hypotheses are generated based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and clear hypothesis can be generated. Also, if the user is relaxed, the hypothesis unit can generate a detailed hypothesis. Furthermore, if the user is in a hurry, the hypothesis unit can prioritize a hypothesis that can be generated quickly. In this way, by adjusting the way the hypotheses are generated according to the user's emotions, an appropriate hypothesis can be generated.

[0109] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple and clear suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide quick and understandable suggestions. In this way, appropriate suggestions can be made by adjusting the way suggestions are expressed according to the user's emotions.

[0110] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and quick verification method can be provided. Furthermore, if the user is relaxed, the verification unit can provide a detailed verification method. Furthermore, if the user is in a hurry, the verification unit can provide a verification method that can quickly obtain results. In this way, by adjusting the verification method according to the user's emotions, appropriate verification can be performed.

[0111] The reception unit can prioritize input of region-specific tasks by taking into account the user's geographical location information. For example, if the user is in a specific region, tasks related to that region can be displayed with priority. The reception unit can also suggest tasks that utilize nearby resources based on the user's current location. Furthermore, the reception unit can prioritize display of region-specific tasks based on the user's location information. This allows region-specific tasks to be input with priority by taking into account the user's geographical location information.

[0112] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. For example, information from highly reliable information sources can be collected with priority. The collection unit can also postpone collection of less reliable information. Furthermore, the collection unit can evaluate the reliability of information and prioritize collection of highly reliable information. In this way, by evaluating the reliability of information, highly reliable information can be collected with priority.

[0113] When formulating a hypothesis, the hypothesis unit can apply different hypothesis formulation algorithms depending on the category of information. For example, a technical algorithm can be applied to a hypothesis based on technical information. The hypothesis unit can also apply a market analysis algorithm to a hypothesis based on market information. Furthermore, the hypothesis unit can apply a user behavior analysis algorithm to a hypothesis based on user feedback. In this way, by applying different hypothesis formulation algorithms depending on the category of information, appropriate hypotheses can be formulated.

[0114] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the hypothesis. For example, a proposal based on the latest hypothesis can be given priority. The proposal unit can also postpone proposals based on older hypotheses. Furthermore, the proposal unit can give priority to proposals based on hypotheses whose submission time is important. In this way, by determining the priority of the proposal based on the time of submission of the hypothesis, a proposal based on the latest hypothesis can be given priority.

[0115] During verification, the verification unit can select the optimal verification method by referring to past verification results. For example, it can prioritize the selection of a method that has had good past verification results. The verification unit can also postpone the selection of a method that has had insufficient past verification results. Furthermore, the verification unit can select the optimal verification method based on past verification results. This makes it possible to select the optimal verification method by referring to past verification results.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The user inputs the assignment into the reception unit. For example, the user can input the assignment in text format. Step 2: The collection unit analyzes the problem entered by the reception unit and collects related information. For example, the collection unit collects similar past cases, related research papers, expert opinions, etc. Step 3: The hypothesis unit formulates hypotheses based on the information collected by the collection unit. For example, the hypothesis unit formulates multiple hypotheses based on the collected information and clearly explains the basis for each hypothesis. Step 4: The proposing unit proposes a candidate solution based on the hypothesis established by the hypothesis unit. For example, the proposing unit proposes a candidate solution based on the hypothesis. Step 5: The verification unit verifies the solution proposed by the proposal unit. For example, the verification unit may have users actually try it out or run simulations to verify the solution based on its effectiveness, feasibility, cost, etc.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 section for inputting assignments; a collection unit that analyzes the task input by the reception unit and collects related information; a hypothesis unit that formulates a hypothesis based on the information collected by the collection unit; a proposal unit that proposes potential solutions based on the hypotheses established by the hypothesis unit; a verification unit that verifies the solution proposed by the proposal unit. A system characterized by:

2. The collecting unit Collect similar past cases, related research papers, and expert opinions 2. The system of claim 1.

3. The hypothesis section Formulate multiple hypotheses based on the collected information and clearly explain the rationale for each hypothesis 2. The system of claim 1.

4. The proposal unit Propose potential solutions based on hypotheses 2. The system of claim 1.

5. The verification unit Users actually try it out, run simulations, and verify the solution based on its effectiveness, feasibility, cost, etc.

2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting the task input method based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyzes the user's past assignment input history and suggests input formats 2. The system of claim 1.

8. The reception unit When entering an issue, filter your entries based on your current projects and areas of interest 2. The system of claim 1.

Images