System

Abstract

An object of a system according to an embodiment is to efficiently obtain advice from an expert outside a company in management decision.SOLUTION: A system includes a reception part, an analysis part, an advice part, and an evaluation part. The reception unit receives a question or consultation content of a user. The analysis unit analyzes the information received by the reception unit. The advice unit provides advice based on the information analyzed by the analysis unit. The evaluation unit evaluates the reliability of the advice provided by the advice unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently obtain advice from outside experts when making management decisions.

[0005] The system according to the embodiment aims to efficiently obtain advice from outside experts when making management decisions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an advice unit, and an evaluation unit. The reception unit receives a question or consultation from a user. The analysis unit analyzes the information received by the reception unit. The advice unit provides advice based on the information analyzed by the analysis unit. The evaluation unit evaluates the reliability of the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to efficiently obtain advice from outside experts when making management decisions. [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 management advice system according to an embodiment of the present invention efficiently accepts and analyzes user questions and consultations, provides appropriate advice, and evaluates the reliability of such advice. In this management advice system, users input management-related questions and consultations, and a generation AI analyzes the manager's thinking and judgments based on past information and provides appropriate advice. This advice serves as a reference for users when making management decisions. Furthermore, the management advice system is intended for continuous use and is generally used on a subscription basis, but spot use is also possible for important situations. For example, when a startup company enters a new market, the generation AI provides advice based on past successes and failures, allowing the company to develop an effective strategy while minimizing risk. Similarly, when a large company launches a new project, the generation AI can provide appropriate advice, increasing the project's success rate. This allows the management advice system to support corporate growth and success. For example, companies can obtain the necessary advice when they need it and make strategic management decisions. This allows the management advice system to support corporate growth and success.

[0029] The management advice system according to the embodiment includes a reception unit, an analysis unit, an advice unit, and an evaluation unit. The reception unit receives a user's question or consultation content. The question or consultation content includes, but is not limited to, technical questions, business consultations, and the like. The reception unit receives, for example, text data input by the user. The reception unit can also receive voice input and image input. The analysis unit analyzes the information received by the reception unit. The analysis can be performed using, for example, data mining or text analysis, but is not limited to, the above. The analysis unit analyzes the user's question or consultation content using, for example, data mining technology. The analysis unit can also analyze the user's question or consultation content using text analysis technology. The advice unit provides advice based on the information analyzed by the analysis unit. The advice can be provided using, for example, text-based advice or audio advice, but is not limited to, the above. The advice unit provides, for example, text-based advice. The advice unit can also provide audio advice. The evaluation unit evaluates the reliability of the advice provided by the advice unit. The reliability evaluation is performed based on, for example, past performance and user feedback, but is not limited to these examples. The evaluation unit evaluates the reliability of advice based on, for example, past performance. The evaluation unit can also evaluate the reliability of advice based on user feedback. This allows the management advice system according to the embodiment to efficiently accept and analyze the content of questions and consultations from users, provide appropriate advice, and evaluate its reliability.

[0030] The reception unit can analyze the user's past question history and select a reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. For example, the reception unit can also suggest the optimal reception time slot based on the user's past question history. For example, the reception unit can analyze the content of the user's past questions and automatically accept related questions. This makes it possible to provide the optimal reception method based on the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history data into the generation AI and have the generation AI select the optimal reception method.

[0031] The reception unit can filter questions and consultation contents based on the user's current business situation and areas of interest when receiving the questions and consultation contents. The reception unit, for example, prioritizes receiving related questions and consultation contents based on the user's current business situation. The reception unit can also filter related questions and consultation contents based on the user's areas of interest. The reception unit can also receive optimal questions and consultation contents by combining the user's business situation and areas of interest, for example. This makes it possible to prioritize receiving questions and consultation contents that correspond to the user's business situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's business situation data and area of ​​interest data to the generation AI and have the generation AI perform filtering.

[0032] The reception unit can select a reception means according to the user's input method when receiving a question or consultation content. For example, if the user uses voice input, the reception unit receives the question or consultation content using voice recognition technology. For example, if the user uses text input, the reception unit can also receive the question or consultation content using text analysis technology. For example, if the user uses image input, the reception unit can also receive the question or consultation content using image recognition technology. This makes it possible to provide the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input voice data, text data, and image data into a generation AI and have the generation AI select the optimal reception means.

[0033] When receiving a question or consultation, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit prioritizes receiving questions or consultation content related to that region. For example, if the user is traveling, the reception unit can also prioritize receiving questions or consultation content related to the user's current location. For example, if the user is interested in a specific country or region, the reception unit can also prioritize receiving questions or consultation content related to that region. This allows highly relevant questions or consultation content to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant content.

[0034] The reception unit can analyze the user's social media activity when receiving a question or consultation content and receive related content. The reception unit can, for example, receive related questions or consultation content based on information shared by the user on social media. The reception unit can, for example, analyze the user's social media activity and prioritize receiving related questions or consultation content. The reception unit can, for example, receive related questions or consultation content based on the activity of the user's friends on social media. This makes it possible to receive related questions or consultation content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related content.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or consultation. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also, for example, analyze the user's past feedback and customize the reception method. The reception unit can also, for example, optimize the reception procedure by reflecting the user's feedback. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the manager's thinking and the importance of the decision. For example, when the manager's decision is important, the analysis unit provides detailed analysis results. For example, the analysis unit can also adjust the level of detail of the analysis based on the manager's thinking. For example, the analysis unit can also adjust the accuracy of the analysis results according to the importance of the manager's decision. This makes it possible to adjust the level of detail of the analysis according to the manager's thinking and the importance of the decision. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the manager's decision data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the manager's category. For example, if the manager is a startup company, the analysis unit can apply an analysis algorithm that focuses on risk management. For example, if the manager is a large company, the analysis unit can also apply an analysis algorithm that focuses on efficiency. For example, if the manager belongs to a specific industry, the analysis unit can also apply an analysis algorithm specialized for that industry. This makes it possible to apply the optimal analysis algorithm depending on the manager's category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the manager's category data into the generation AI and have the generation AI apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by analyzing the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of the analysis based on the timing of the submission of the manager's judgment. For example, if the manager's judgment is urgent, the analysis unit sets a high priority for the analysis. For example, the analysis unit can also adjust the analysis schedule based on the timing of the submission of the manager's judgment. For example, the analysis unit can also determine the priority of the analysis according to the timing of the submission of the manager's judgment. This makes it possible to determine the priority of the analysis according to the timing of the submission of the manager's judgment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the timing of the submission of the manager's judgment into the generation AI and have the generation AI determine the priority of the analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the manager's judgment. For example, if the manager's judgment is highly relevant, the analysis unit sets the order of analysis with priority. For example, the analysis unit can also adjust the order of analysis based on the relevance of the manager's judgment. For example, the analysis unit can also determine the order of analysis according to the relevance of the manager's judgment. This makes it possible to adjust the order of analysis according to the relevance of the manager's judgment. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the manager's judgment to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. For example, if the user does not have technical expertise, the analysis unit can also provide analysis results in easy-to-understand language. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it possible to provide analysis results using optimal technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0042] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the business decision. For example, when the business decision is important, the advice unit provides detailed advice. For example, the advice unit can also adjust the level of detail of the advice based on the importance of the business decision. For example, the advice unit can also adjust the precision of the advice according to the importance of the business decision. This makes it possible to adjust the level of detail of the advice according to the importance of the business decision. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data on the importance of the business decision to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0043] When providing advice, the advice unit can apply different advice algorithms depending on the category of the business decision. For example, if the business decision is for a startup company, the advice unit can apply an advice algorithm that focuses on risk management. For example, if the business decision is for a large company, the advice unit can also apply an advice algorithm that focuses on efficiency. For example, if the business decision belongs to a specific industry, the advice unit can also apply an advice algorithm specialized for that industry. This makes it possible to apply the optimal advice algorithm depending on the category of the business decision. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input business decision category data into the generation AI and have the generation AI apply the advice algorithm.

[0044] When providing advice, the advice unit can determine the priority of the advice based on the timing of submission of the business decision. For example, if the business decision is urgent, the advice unit sets a high priority for the advice. The advice unit can also adjust the schedule of the advice based on the timing of submission of the business decision. For example, the advice unit can also determine the priority of the advice according to the timing of submission of the business decision. This makes it possible to determine the priority of the advice according to the timing of submission of the business decision. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data on the timing of submission of the business decision into the generation AI and have the generation AI determine the priority of the advice.

[0045] When providing advice, the advice unit can adjust the order of advice based on the relevance of the business decisions. For example, if the business decisions are highly relevant, the advice unit sets the order of advice preferentially. For example, the advice unit can also adjust the order of advice based on the relevance of the business decisions. For example, the advice unit can also determine the order of advice according to the relevance of the business decisions. This makes it possible to adjust the order of advice according to the relevance of the business decisions. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input relevance data of the business decisions to the generation AI and cause the generation AI to adjust the order of advice.

[0046] When providing advice, the advice unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, if the user has technical expertise, the advice unit can provide advice that uses a lot of technical terminology. For example, if the user does not have technical expertise, the advice unit can also provide advice in easy-to-understand language. For example, the advice unit can adjust the use of technical terminology in the advice according to the user's level of expertise. This makes it possible to provide advice using optimal technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0047] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating the reliability of advice. The evaluation unit, for example, optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also improve the accuracy of the reliability evaluation by referring to past evaluation data. The evaluation unit can also analyze past evaluation data and improve the evaluation algorithm, for example. This makes it possible to optimize the evaluation algorithm based on the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input past evaluation data into the generation AI and cause the generation AI to optimize the evaluation algorithm.

[0048] The evaluation unit can update the evaluation data by reflecting user feedback when evaluating the reliability of advice. The evaluation unit updates the evaluation data based on, for example, user feedback. The evaluation unit can also improve the accuracy of the reliability evaluation by reflecting, for example, user feedback. The evaluation unit can also analyze, for example, user feedback and improve the evaluation data. This makes it possible to update the evaluation data based on user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input user feedback data to the generation AI and cause the generation AI to update the evaluation data.

[0049] The evaluation unit can integrate information from different data sources to enrich the evaluation data when evaluating the reliability of advice. For example, the evaluation unit integrates information from different data sources to enrich the evaluation data. For example, the evaluation unit can also refer to information from different data sources to improve the accuracy of the reliability evaluation. For example, the evaluation unit can analyze information from different data sources to improve the evaluation data. This makes it possible to integrate information from different data sources to enrich the evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input information from different data sources into the generation AI and cause the generation AI to enrich the evaluation data.

[0050] The evaluation unit can weight the evaluation data based on the time of submission of the business decision when evaluating the reliability of the advice. For example, if the time of submission of the business decision is close, the evaluation unit sets a high weighting of the evaluation data. For example, the evaluation unit can also adjust the weighting of the evaluation data based on the time of submission of the business decision. For example, the evaluation unit can also weight the evaluation data according to the time of submission of the business decision. This makes it possible to weight the evaluation data based on the time of submission of the business decision. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input data on the time of submission of the business decision to the generation AI and cause the generation AI to weight the evaluation data.

[0051] The evaluation unit can integrate information from different data sources to enrich the evaluation data when evaluating the reliability of advice. For example, the evaluation unit integrates information from different data sources to enrich the evaluation data. For example, the evaluation unit can also refer to information from different data sources to improve the accuracy of the reliability evaluation. For example, the evaluation unit can analyze information from different data sources to improve the evaluation data. This makes it possible to integrate information from different data sources to enrich the evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input information from different data sources into the generation AI and cause the generation AI to enrich the evaluation data.

[0052] The evaluation unit can update the evaluation data by reflecting user feedback when evaluating the reliability of advice. The evaluation unit updates the evaluation data based on, for example, user feedback. The evaluation unit can also improve the accuracy of the reliability evaluation by reflecting, for example, user feedback. The evaluation unit can also analyze, for example, user feedback and improve the evaluation data. This makes it possible to update the evaluation data based on user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input user feedback data to the generation AI and cause the generation AI to update the evaluation data.

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

[0054] When accepting a user's question or consultation, the acceptance unit can analyze the user's past behavioral patterns and suggest the optimal acceptance method. For example, it can preferentially suggest acceptance methods that the user has frequently used in the past. It can also suggest the optimal acceptance time period based on the user's past question history. Furthermore, it can analyze the content of the user's past questions and automatically accept related questions. This makes it possible to provide the optimal acceptance method based on the user's past behavioral patterns.

[0055] The reception unit can analyze the user's past question history and select a reception method. For example, it can preferentially suggest reception methods that the user has frequently used in the past. It can also suggest the optimal reception time period based on the user's past question history. Furthermore, it can analyze the content of the user's past questions and automatically accept related questions. This makes it possible to provide the optimal reception method based on the user's past question history.

[0056] When receiving questions or consultation contents, the reception unit can filter them based on the user's current business situation or areas of interest. For example, it is possible to preferentially receive questions or consultation contents related to the user's current business situation. It is also possible to filter related questions or consultation contents based on the user's areas of interest. Furthermore, it is possible to combine the user's business situation and areas of interest to receive the most appropriate questions or consultation contents. This makes it possible to preferentially receive questions or consultation contents that correspond to the user's business situation or areas of interest.

[0057] When accepting a question or consultation, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit can accept the question or consultation using voice recognition technology. If the user uses text input, the acceptance unit can accept the question or consultation using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can accept the question or consultation using image recognition technology. This makes it possible to provide the most suitable acceptance means according to the user's input method.

[0058] When accepting questions or consultation contents, the reception unit can prioritize accepting highly relevant contents in consideration of the user's geographical location information. For example, if the user is in a specific area, questions or consultation contents related to that area can be prioritized. Also, if the user is traveling, related questions or consultation contents can be prioritized based on the user's current location. Furthermore, if the user is interested in a specific country or area, questions or consultation contents related to that area can be prioritized. This makes it possible to prioritize accepting highly relevant questions or consultation contents based on the user's geographical location information.

[0059] When receiving a question or consultation, the reception unit can analyze the user's social media activity and receive related content. For example, it can receive related questions or consultations based on information shared by the user on social media. It can also analyze the user's social media activity and prioritize the reception of related questions or consultations. It can also receive related questions or consultations by taking into account the activity of the user's friends on social media. This makes it possible to receive related questions or consultations based on the user's social media activity.

[0060] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or consultation. For example, the reception unit can propose the optimal reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. Furthermore, the reception procedure can be optimized by reflecting the user's feedback. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives a question or inquiry from a user. The question or inquiry may be a technical question or a business question. The reception unit receives text data entered by the user, as well as voice input and image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as data mining and text analysis. The analysis unit uses data mining technology and text analysis technology to analyze the user's question or inquiry. Step 3: The advice unit provides advice based on the information analyzed by the analysis unit. The advice may be provided by a method such as text-based advice or audio advice. Step 4: The evaluation unit evaluates the reliability of the advice provided by the advice unit. The reliability is evaluated based on criteria such as past performance and user feedback.

[0063] (Example 2) A management advice system according to an embodiment of the present invention efficiently accepts and analyzes user questions and consultations, provides appropriate advice, and evaluates the reliability of such advice. In this management advice system, users input management-related questions and consultations, and a generation AI analyzes the manager's thinking and judgments based on past information and provides appropriate advice. This advice serves as a reference for users when making management decisions. Furthermore, the management advice system is intended for continuous use and is generally used on a subscription basis, but spot use is also possible for important situations. For example, when a startup company enters a new market, the generation AI provides advice based on past successes and failures, allowing the company to develop an effective strategy while minimizing risk. Similarly, when a large company launches a new project, the generation AI can provide appropriate advice, increasing the project's success rate. This allows the management advice system to support corporate growth and success. For example, companies can obtain the necessary advice when they need it and make strategic management decisions. This allows the management advice system to support corporate growth and success.

[0064] The management advice system according to the embodiment includes a reception unit, an analysis unit, an advice unit, and an evaluation unit. The reception unit receives a user's question or consultation content. The question or consultation content includes, but is not limited to, technical questions, business consultations, and the like. The reception unit receives, for example, text data input by the user. The reception unit can also receive voice input and image input. The analysis unit analyzes the information received by the reception unit. The analysis can be performed using, for example, data mining or text analysis, but is not limited to, the above. The analysis unit analyzes the user's question or consultation content using, for example, data mining technology. The analysis unit can also analyze the user's question or consultation content using text analysis technology. The advice unit provides advice based on the information analyzed by the analysis unit. The advice can be provided using, for example, text-based advice or audio advice, but is not limited to, the above. The advice unit provides, for example, text-based advice. The advice unit can also provide audio advice. The evaluation unit evaluates the reliability of the advice provided by the advice unit. The reliability evaluation is performed based on, for example, past performance and user feedback, but is not limited to these examples. The evaluation unit evaluates the reliability of advice based on, for example, past performance. The evaluation unit can also evaluate the reliability of advice based on user feedback. This allows the management advice system according to the embodiment to efficiently accept and analyze the content of questions and consultations from users, provide appropriate advice, and evaluate its reliability.

[0065] The reception unit can estimate the user's emotions and adjust the timing of receiving questions or consultations based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive questions or consultations during a time when the user is able to relax. For example, if the user is in a hurry, the reception unit can also receive questions or consultations immediately. For example, if the user is relaxed, the reception unit can also receive detailed questions or consultations. This allows questions or consultations to be received at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, or may be performed without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0066] The reception unit can analyze the user's past question history and select a reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. For example, the reception unit can also suggest the optimal reception time slot based on the user's past question history. For example, the reception unit can analyze the content of the user's past questions and automatically accept related questions. This makes it possible to provide the optimal reception method based on the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history data into the generation AI and have the generation AI select the optimal reception method.

[0067] The reception unit can filter questions and consultation contents based on the user's current business situation and areas of interest when receiving the questions and consultation contents. The reception unit, for example, prioritizes receiving related questions and consultation contents based on the user's current business situation. The reception unit can also filter related questions and consultation contents based on the user's areas of interest. The reception unit can also receive optimal questions and consultation contents by combining the user's business situation and areas of interest, for example. This makes it possible to prioritize receiving questions and consultation contents that correspond to the user's business situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's business situation data and area of ​​interest data to the generation AI and have the generation AI perform filtering.

[0068] The reception unit can select a reception means according to the user's input method when receiving a question or consultation content. For example, if the user uses voice input, the reception unit receives the question or consultation content using voice recognition technology. For example, if the user uses text input, the reception unit can also receive the question or consultation content using text analysis technology. For example, if the user uses image input, the reception unit can also receive the question or consultation content using image recognition technology. This makes it possible to provide the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input voice data, text data, and image data into a generation AI and have the generation AI select the optimal reception means.

[0069] The reception unit can estimate the user's emotions and determine the priority of questions and consultation contents to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving questions and consultation contents with high importance. For example, when the user is relaxed, the reception unit can also prioritize receiving detailed questions and consultation contents. For example, when the user is in a hurry, the reception unit can also prioritize receiving questions and consultation contents that require a quick response. This makes it possible to determine the priority of questions and consultation contents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0070] When receiving a question or consultation, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit prioritizes receiving questions or consultation content related to that region. For example, if the user is traveling, the reception unit can also prioritize receiving questions or consultation content related to the user's current location. For example, if the user is interested in a specific country or region, the reception unit can also prioritize receiving questions or consultation content related to that region. This allows highly relevant questions or consultation content to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant content.

[0071] The reception unit can analyze the user's social media activity when receiving a question or consultation content and receive related content. The reception unit can, for example, receive related questions or consultation content based on information shared by the user on social media. The reception unit can, for example, analyze the user's social media activity and prioritize receiving related questions or consultation content. The reception unit can, for example, receive related questions or consultation content based on the activity of the user's friends on social media. This makes it possible to receive related questions or consultation content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related content.

[0072] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or consultation. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also, for example, analyze the user's past feedback and customize the reception method. The reception unit can also, for example, optimize the reception procedure by reflecting the user's feedback. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0073] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can use a simple and easy-to-understand expression method. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows the analysis results to be provided in an optimal expression method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the manager's thinking and the importance of the decision. For example, when the manager's decision is important, the analysis unit provides detailed analysis results. For example, the analysis unit can also adjust the level of detail of the analysis based on the manager's thinking. For example, the analysis unit can also adjust the accuracy of the analysis results according to the importance of the manager's decision. This makes it possible to adjust the level of detail of the analysis according to the manager's thinking and the importance of the decision. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the manager's decision data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the manager's category. For example, if the manager is a startup company, the analysis unit can apply an analysis algorithm that focuses on risk management. For example, if the manager is a large company, the analysis unit can also apply an analysis algorithm that focuses on efficiency. For example, if the manager belongs to a specific industry, the analysis unit can also apply an analysis algorithm specialized for that industry. This makes it possible to apply the optimal analysis algorithm depending on the manager's category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the manager's category data into the generation AI and have the generation AI apply the analysis algorithm.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by analyzing the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows the analysis result to be provided at an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] During analysis, the analysis unit can determine the priority of the analysis based on the timing of the submission of the manager's judgment. For example, if the manager's judgment is urgent, the analysis unit sets a high priority for the analysis. For example, the analysis unit can also adjust the analysis schedule based on the timing of the submission of the manager's judgment. For example, the analysis unit can also determine the priority of the analysis according to the timing of the submission of the manager's judgment. This makes it possible to determine the priority of the analysis according to the timing of the submission of the manager's judgment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the timing of the submission of the manager's judgment into the generation AI and have the generation AI determine the priority of the analysis.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the manager's judgment. For example, if the manager's judgment is highly relevant, the analysis unit sets the order of analysis with priority. For example, the analysis unit can also adjust the order of analysis based on the relevance of the manager's judgment. For example, the analysis unit can also determine the order of analysis according to the relevance of the manager's judgment. This makes it possible to adjust the order of analysis according to the relevance of the manager's judgment. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the manager's judgment to the generation AI and cause the generation AI to adjust the order of analysis.

[0080] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. For example, if the user does not have technical expertise, the analysis unit can also provide analysis results in easy-to-understand language. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it possible to provide analysis results using optimal technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0081] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide simple and easy-to-understand advice. For example, if the user is relaxed, the advice unit can provide detailed advice. For example, if the user is in a hurry, the advice unit can provide concise advice that focuses on the main points. This makes it possible to provide advice in an optimal way of expression depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit can be performed using AI, for example, or without AI. For example, the advice unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0082] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the business decision. For example, when the business decision is important, the advice unit provides detailed advice. For example, the advice unit can also adjust the level of detail of the advice based on the importance of the business decision. For example, the advice unit can also adjust the precision of the advice according to the importance of the business decision. This makes it possible to adjust the level of detail of the advice according to the importance of the business decision. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data on the importance of the business decision to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0083] When providing advice, the advice unit can apply different advice algorithms depending on the category of the business decision. For example, if the business decision is for a startup company, the advice unit can apply an advice algorithm that focuses on risk management. For example, if the business decision is for a large company, the advice unit can also apply an advice algorithm that focuses on efficiency. For example, if the business decision belongs to a specific industry, the advice unit can also apply an advice algorithm specialized for that industry. This makes it possible to apply the optimal advice algorithm depending on the category of the business decision. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input business decision category data into the generation AI and have the generation AI apply the advice algorithm.

[0084] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. For example, if the user is in a hurry, the advice unit can provide short, to-the-point advice. For example, if the user is relaxed, the advice unit can provide detailed advice. For example, if the user is excited, the advice unit can provide advice with visually stimulating effects. This allows advice to be provided at an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit can be performed using AI, for example, or without AI. For example, the advice unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When providing advice, the advice unit can determine the priority of the advice based on the timing of submission of the business decision. For example, if the business decision is urgent, the advice unit sets a high priority for the advice. The advice unit can also adjust the schedule of the advice based on the timing of submission of the business decision. For example, the advice unit can also determine the priority of the advice according to the timing of submission of the business decision. This makes it possible to determine the priority of the advice according to the timing of submission of the business decision. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input data on the timing of submission of the business decision into the generation AI and have the generation AI determine the priority of the advice.

[0086] When providing advice, the advice unit can adjust the order of advice based on the relevance of the business decisions. For example, if the business decisions are highly relevant, the advice unit sets the order of advice preferentially. For example, the advice unit can also adjust the order of advice based on the relevance of the business decisions. For example, the advice unit can also determine the order of advice according to the relevance of the business decisions. This makes it possible to adjust the order of advice according to the relevance of the business decisions. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input relevance data of the business decisions to the generation AI and cause the generation AI to adjust the order of advice.

[0087] When providing advice, the advice unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, if the user has technical expertise, the advice unit can provide advice that uses a lot of technical terminology. For example, if the user does not have technical expertise, the advice unit can also provide advice in easy-to-understand language. For example, the advice unit can adjust the use of technical terminology in the advice according to the user's level of expertise. This makes it possible to provide advice using optimal technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0088] The evaluation unit can estimate the user's emotions and adjust the method of evaluating the reliability of advice based on the estimated user emotions. For example, if the user is stressed, the evaluation unit uses a simple and easy-to-understand reliability evaluation method. For example, if the user is relaxed, the evaluation unit can also use a detailed reliability evaluation method. For example, if the user is in a hurry, the evaluation unit can also use a concise reliability evaluation method that focuses on the main points. This makes it possible to evaluate the reliability of advice in an optimal manner depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0089] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data when evaluating the reliability of advice. The evaluation unit, for example, optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also improve the accuracy of the reliability evaluation by referring to past evaluation data. The evaluation unit can also analyze past evaluation data and improve the evaluation algorithm, for example. This makes it possible to optimize the evaluation algorithm based on the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input past evaluation data into the generation AI and cause the generation AI to optimize the evaluation algorithm.

[0090] The evaluation unit can update the evaluation data by reflecting user feedback when evaluating the reliability of advice. The evaluation unit updates the evaluation data based on, for example, user feedback. The evaluation unit can also improve the accuracy of the reliability evaluation by reflecting, for example, user feedback. The evaluation unit can also analyze, for example, user feedback and improve the evaluation data. This makes it possible to update the evaluation data based on user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input user feedback data to the generation AI and cause the generation AI to update the evaluation data.

[0091] The evaluation unit can integrate information from different data sources to enrich the evaluation data when evaluating the reliability of advice. For example, the evaluation unit integrates information from different data sources to enrich the evaluation data. For example, the evaluation unit can also refer to information from different data sources to improve the accuracy of the reliability evaluation. For example, the evaluation unit can analyze information from different data sources to improve the evaluation data. This makes it possible to integrate information from different data sources to enrich the evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input information from different data sources into the generation AI and cause the generation AI to enrich the evaluation data.

[0092] The evaluation unit can estimate the user's emotion and adjust the frequency of the trustworthiness evaluation based on the estimated user's emotion. For example, if the user is stressed, the evaluation unit can set the frequency of the trustworthiness evaluation low. For example, if the user is relaxed, the evaluation unit can also set the frequency of the trustworthiness evaluation high. For example, if the user is in a hurry, the evaluation unit can also adjust the frequency of the trustworthiness evaluation. This makes it possible to adjust the frequency of the trustworthiness evaluation according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using an AI, for example, or without an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The evaluation unit can weight the evaluation data based on the time of submission of the business decision when evaluating the reliability of the advice. For example, if the time of submission of the business decision is close, the evaluation unit sets a high weighting of the evaluation data. For example, the evaluation unit can also adjust the weighting of the evaluation data based on the time of submission of the business decision. For example, the evaluation unit can also weight the evaluation data according to the time of submission of the business decision. This makes it possible to weight the evaluation data based on the time of submission of the business decision. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input data on the time of submission of the business decision to the generation AI and cause the generation AI to weight the evaluation data.

[0094] The evaluation unit can integrate information from different data sources to enrich the evaluation data when evaluating the reliability of advice. For example, the evaluation unit integrates information from different data sources to enrich the evaluation data. For example, the evaluation unit can also refer to information from different data sources to improve the accuracy of the reliability evaluation. For example, the evaluation unit can analyze information from different data sources to improve the evaluation data. This makes it possible to integrate information from different data sources to enrich the evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input information from different data sources into the generation AI and cause the generation AI to enrich the evaluation data.

[0095] The evaluation unit can update the evaluation data by reflecting user feedback when evaluating the reliability of advice. The evaluation unit updates the evaluation data based on, for example, user feedback. The evaluation unit can also improve the accuracy of the reliability evaluation by reflecting, for example, user feedback. The evaluation unit can also analyze, for example, user feedback and improve the evaluation data. This makes it possible to update the evaluation data based on user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input user feedback data to the generation AI and cause the generation AI to update the evaluation data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives questions and consultations from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the provided advice. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, and evaluation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives questions and consultations from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the provided advice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, and evaluation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives questions and consultations from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the advice provided. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, and evaluation 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 receives questions and consultations from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the advice provided.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] When accepting a user's question or consultation, the acceptance unit can analyze the user's past behavioral patterns and suggest the optimal acceptance method. For example, it can preferentially suggest acceptance methods that the user has frequently used in the past. It can also suggest the optimal acceptance time period based on the user's past question history. Furthermore, it can analyze the content of the user's past questions and automatically accept related questions. This makes it possible to provide the optimal acceptance method based on the user's past behavioral patterns.

[0098] The reception unit can estimate the user's emotions and adjust the timing of receiving questions and consultation contents based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive questions and consultation contents during a time when the user is able to relax. Also, if the user is in a hurry, the reception unit can receive questions and consultation contents immediately. Furthermore, if the user is relaxed, the reception unit can receive detailed questions and consultation contents. This makes it possible to receive questions and consultation contents at the optimal timing according to the user's emotions.

[0099] The reception unit can analyze the user's past question history and select a reception method. For example, it can preferentially suggest reception methods that the user has frequently used in the past. It can also suggest the optimal reception time period based on the user's past question history. Furthermore, it can analyze the content of the user's past questions and automatically accept related questions. This makes it possible to provide the optimal reception method based on the user's past question history.

[0100] When receiving questions or consultation contents, the reception unit can filter them based on the user's current business situation or areas of interest. For example, it is possible to preferentially receive questions or consultation contents related to the user's current business situation. It is also possible to filter related questions or consultation contents based on the user's areas of interest. Furthermore, it is possible to combine the user's business situation and areas of interest to receive the most appropriate questions or consultation contents. This makes it possible to preferentially receive questions or consultation contents that correspond to the user's business situation or areas of interest.

[0101] When accepting a question or consultation, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit can accept the question or consultation using voice recognition technology. If the user uses text input, the acceptance unit can accept the question or consultation using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can accept the question or consultation using image recognition technology. This makes it possible to provide the most suitable acceptance means according to the user's input method.

[0102] The reception unit can estimate the user's emotions and determine the priority of questions and consultation contents to be received based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize receiving questions and consultation contents of high importance. Also, if the user is relaxed, it can prioritize receiving detailed questions and consultation contents. Furthermore, if the user is in a hurry, it can prioritize receiving questions and consultation contents that require a quick response. In this way, it is possible to determine the priority of questions and consultation contents according to the user's emotions.

[0103] When accepting questions or consultation contents, the reception unit can prioritize accepting highly relevant contents in consideration of the user's geographical location information. For example, if the user is in a specific area, questions or consultation contents related to that area can be prioritized. Also, if the user is traveling, related questions or consultation contents can be prioritized based on the user's current location. Furthermore, if the user is interested in a specific country or area, questions or consultation contents related to that area can be prioritized. This makes it possible to prioritize accepting highly relevant questions or consultation contents based on the user's geographical location information.

[0104] When receiving a question or consultation, the reception unit can analyze the user's social media activity and receive related content. For example, it can receive related questions or consultations based on information shared by the user on social media. It can also analyze the user's social media activity and prioritize the reception of related questions or consultations. It can also receive related questions or consultations by taking into account the activity of the user's friends on social media. This makes it possible to receive related questions or consultations based on the user's social media activity.

[0105] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or consultation. For example, the reception unit can propose the optimal reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. Furthermore, the reception procedure can be optimized by reflecting the user's feedback. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0106] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple and easy-to-understand presentation can be used. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be provided. This makes it possible to provide analysis results in the most appropriate presentation method depending on the user's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit receives a question or inquiry from a user. The question or inquiry may be a technical question or a business question. The reception unit receives text data entered by the user, as well as voice input and image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as data mining and text analysis. The analysis unit uses data mining technology and text analysis technology to analyze the user's question or inquiry. Step 3: The advice unit provides advice based on the information analyzed by the analysis unit. The advice may be provided by a method such as text-based advice or audio advice. Step 4: The evaluation unit evaluates the reliability of the advice provided by the advice unit. The reliability is evaluated based on criteria such as past performance and user feedback.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 system comprising: a reception unit that receives a user's questions or consultation requests; an analysis unit that analyzes the information received by the reception unit; an advice unit that provides advice based on the information analyzed by the analysis unit; and an evaluation unit that evaluates the reliability of the advice provided by the advice unit.

2. 2. The system according to claim 1, wherein the reception unit estimates a user's emotion and adjusts the timing of receiving questions or consultations based on the estimated user's emotion.

3. 2. The system according to claim 1, wherein the reception unit analyzes a history of past questions from the user and selects a reception method.

4. 2. The system according to claim 1, wherein the reception unit filters the questions or inquiries based on the user's current business situation or areas of interest when receiving the questions or inquiries.

5. 2. The system according to claim 1, wherein the reception unit selects a reception means depending on a user's input method when receiving a question or a consultation.

6. The reception unit Estimate the user's emotions and prioritize the questions and inquiries to be accepted based on the estimated user emotions. The system of claim 1 .

7. The reception unit When accepting questions or inquiries, the system takes into account the user's geographical location information and prioritizes relevant inquiries. The system of claim 1 .

8. The reception unit When receiving questions or inquiries, analyze the user's social media activity and receive related content. The system of claim 1 .

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