System

Abstract

An object of a system according to an embodiment is to provide appropriate consulting for a problem or a consultation of a company.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The reception part inputs a problem and consultation of a company. The analysis part analyzes the information received by the reception part, and performs consulting based on the knowledge. The providing unit provides advice to the company based on the analysis result obtained by the analysis 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] Conventional technology has the problem of lacking the knowledge to provide appropriate consulting for corporate issues and problems.

[0005] The system according to the embodiment aims to provide appropriate consulting for corporate issues and inquiries. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the company's issues and inquiries. The analysis unit analyzes the information received by the reception unit and provides consulting based on the knowledge. The provision unit provides advice to the company based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate consulting services for corporate issues and inquiries. [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 consulting system according to an embodiment of the present invention automatically analyzes a company's issues and concerns and provides appropriate advice. The consulting system includes a reception unit that inputs the company's issues and concerns, an analysis unit that analyzes the information received by the reception unit and provides consulting services based on the acquired knowledge, and a provision unit that provides advice to the company based on the analysis results obtained by the analysis unit. For example, a company inputs an issue such as "I would like to conduct market research for a new product." This information is input into a generation AI. The generation AI then analyzes the input issue and concerns and provides consulting services based on the appropriate knowledge. The generation AI learns knowledge provided by specialists in various fields and provides optimal advice based on that knowledge. For example, the generation AI provides advice on market research methods and points to note. Furthermore, the company can take specific action based on the advice provided by the generation AI. For example, by conducting market research according to the generation AI's advice and then inputting the results back into the generation AI, further advice can be received. This system enables companies, even without specialized knowledge, to effectively solve their issues by receiving consulting services from the generation AI. Furthermore, knowledge providers receive a portion of the consulting fee as a return, encouraging the provision of knowledge. For example, if the information provided by a knowledge provider helps solve a company's problems, the provider will be paid a reward according to the results. This allows the consulting system to efficiently analyze the company's problems and inquiries and provide appropriate advice. For example, even if a company does not have specialized knowledge, it can effectively solve problems by receiving consulting from a generative AI. In addition, a portion of the consulting fee is returned to the knowledge provider, encouraging the provision of knowledge.

[0029] A consulting system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs a company's issues and requests for consultation. Examples of the company's issues and requests for consultation include, but are not limited to, management issues, technical problems, and marketing strategies. For example, the reception unit inputs a request such as, "I would like to conduct market research for a new product." The analysis unit analyzes the information received by the reception unit and provides consulting based on the acquired knowledge. The analysis unit performs the analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit includes a learning unit that learns knowledge provided by specialists. The learning unit learns the knowledge using, for example, machine learning algorithms. The analysis unit includes a consulting unit that provides consulting based on the learned knowledge. The consulting unit provides, for example, strategic advice, technical support, and business model proposals. The provision unit provides advice to the company based on the analysis results obtained by the analysis unit. The provision unit provides the advice in the form of, for example, a report, a presentation, or real-time feedback. The provision unit provides the advice of the generative AI to the company. The providing unit includes a feedback unit that inputs the results of the company's actions back into the generating AI. The feedback unit inputs the results of the company's actions into the generating AI, for example, as quantitative evaluations or qualitative comments. This enables the consulting system according to the embodiment to efficiently analyze the company's issues and consultations and provide appropriate advice.

[0030] The analysis unit includes a learning unit that learns knowledge provided by a specialist. The learning unit learns the knowledge provided by the specialist. Specialists include, but are not limited to, industry experts and certified consultants. The learning unit learns the knowledge using, for example, a machine learning algorithm. For example, the learning unit acquires knowledge using a database query and learns the knowledge using a machine learning algorithm. The learning unit can also build a model for the generative AI to provide consulting based on the knowledge provided by the specialist. This improves the accuracy of analysis by learning the specialist's knowledge.

[0031] The analysis unit includes a consulting unit that provides consulting based on the learned knowledge. The consulting unit provides consulting based on the learned knowledge. Consulting includes, but is not limited to, strategic advice, technical support, and business model proposals. The consulting unit uses, for example, a generative AI to provide optimal advice for a company's challenges and inquiries. For example, the generative AI provides advice on market research methods and points to note in response to a company's challenges. The consulting unit can also support the company in taking specific actions based on the advice provided by the generative AI. This allows for more appropriate advice to be provided by consulting based on the learned knowledge.

[0032] The provision unit provides the generative AI's advice to the company. The provision unit provides the generative AI's advice to the company. The generative AI includes, but is not limited to, a Transformer model. The provision unit provides the advice in the form of a report, a presentation, real-time feedback, or other methods. For example, the provision unit advises the generative AI on market research methods and points to note. The provision unit can also support the company in taking specific actions based on the advice provided by the generative AI. In this way, by providing the generative AI's advice to the company, the provision unit helps the company solve its problems.

[0033] The providing unit includes a feedback unit that inputs the results of the company's actions back into the generating AI. The feedback unit inputs the results of the company's actions back into the generating AI. The feedback includes, for example, quantitative evaluations, qualitative comments, user behavior data, etc., but is not limited to these examples. For example, the feedback unit inputs the results of the company's actions into the generating AI as quantitative evaluations. The feedback unit can also input the results of the company's actions into the generating AI as qualitative comments. The feedback unit can also input the company's behavior data into the generating AI, so that the generating AI can provide further advice based on that data. In this way, by feeding back the results of the company's actions, the accuracy of the advice provided by the generating AI is improved.

[0034] The reception unit can analyze the company's past assignment submission history and select an input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the company has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the company's past assignment submission history. The reception unit can also analyze the company's past assignment submission history and suggest the most efficient input method. This improves input efficiency by selecting the optimal input method based on past history.

[0035] When inputting a problem or consultation, the reception unit can filter the problem or consultation based on the company's current project or area of ​​interest. For example, the reception unit preferentially receives problem or consultation related to a project currently underway by the company. The reception unit can also filter related problem or consultation based on the company's area of ​​interest. The reception unit can also accept appropriate problem or consultation depending on the progress of the company's current project. In this way, by filtering based on the company's current situation, it is possible to accept more relevant problem or consultation.

[0036] When inputting an issue or consultation, the reception unit can select the optimal input means depending on the input method (voice, text, image, etc.) of the company. For example, if the company desires voice input, the reception unit can provide voice input with priority. Also, if the company desires text input, the reception unit can provide text input with priority. Also, if the company desires image input, the reception unit can provide image input with priority. This improves the convenience of input by selecting the optimal means depending on the company's input method.

[0037] When inputting a problem or consultation, the reception unit can prioritize inputting highly relevant problems by taking into account the geographical location information of the company. For example, the reception unit can prioritize accepting problems or consultations related to the location of the company. The reception unit can also prioritize accepting region-specific problems or consultations based on the geographical location information of the company. The reception unit can also suggest the optimal input method by taking into account the geographical location information of the company. In this way, region-specific problems or consultations can be prioritized by taking into account the geographical location information.

[0038] When inputting an issue or inquiry, the reception unit can analyze the company's social media activity and input related issues. For example, the reception unit can analyze the content of the company's social media posts and accept related issues or inquiries. The reception unit can also suggest the most suitable issue or inquiry based on the company's social media activity history. The reception unit can also analyze the reactions of the company's followers on social media and accept related issues or inquiries. In this way, by analyzing social media activity, it is possible to accept highly relevant issues and inquiries.

[0039] The reception unit can customize the input method by reflecting the company's past feedback when inputting an issue or consultation. For example, the reception unit can suggest the optimal input method based on feedback provided by the company in the past. The reception unit can also customize the input interface by reflecting the company's past feedback. The reception unit can also optimize the input procedure based on the company's past feedback. In this way, the input method can be optimized by reflecting past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task. For example, the analysis unit performs a detailed analysis on a task with a high level of importance. The analysis unit can also perform a concise analysis on a task with a low level of importance. The analysis unit can also adjust the depth of the analysis according to the importance of the task. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the task.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the problem. For example, the analysis unit applies a specific market research algorithm to a market research problem. The analysis unit can also apply an analysis algorithm specialized for product development to a product development problem. The analysis unit can also apply an analysis algorithm specialized for financial analysis to a financial analysis problem. In this way, the accuracy of the analysis is improved by applying an analysis algorithm according to the problem category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the company's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the company's past analysis results. The analysis unit can also analyze the company's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the assignment. For example, the analysis unit prioritizes analysis of assignments that are submitted early. The analysis unit can also postpone assignments that are submitted late. The analysis unit can also adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority based on the submission date.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. The analysis unit can also adjust the order of analysis based on the relevance of the tasks. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the tasks.

[0045] During the analysis, the analysis unit can adjust the use of technical terms in the analysis according to the company's level of expertise. For example, the analysis unit uses a lot of technical terms for companies with a high level of expertise. The analysis unit can also avoid using technical terms for companies with a low level of expertise. The analysis unit can also adjust the way the analysis results are presented according to the company's level of expertise. In this way, by adjusting the use of technical terms according to the company's level of expertise, it is possible to provide analysis results that are easier to understand.

[0046] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the task. For example, the providing unit can provide detailed advice for tasks with high importance. The providing unit can also provide concise advice for tasks with low importance. The providing unit can also adjust the depth of the advice depending on the importance of the task. This allows for efficient advice by adjusting the level of detail of the advice depending on the importance of the task.

[0047] When providing advice, the providing unit can apply different advice algorithms depending on the category of the problem. For example, the providing unit applies a specific market research advice algorithm to a market research problem. The providing unit can also apply an advice algorithm specialized for product development to a product development problem. The providing unit can also apply an advice algorithm specialized for financial analysis to a financial analysis problem. In this way, by applying an advice algorithm according to the problem category, the accuracy of advice is improved.

[0048] When providing advice, the providing unit can improve the accuracy of the advice by referring to the company's past advice results. The providing unit can improve the accuracy of current advice, for example, based on the company's past advice results. The providing unit can also optimize the advice algorithm by referring to the company's past advice results. The providing unit can also analyze the company's past advice results and improve the accuracy of the advice. In this way, the accuracy of advice is improved by referring to the past advice results.

[0049] When providing advice, the providing unit can determine the priority of advice based on the submission time of the assignment. For example, the providing unit gives priority to advice on assignments that are to be submitted early. The providing unit can also postpone assignments that are to be submitted late. The providing unit can also adjust the advice schedule based on the submission time. This allows for efficient advice by determining the priority based on the submission time.

[0050] When providing advice, the providing unit can adjust the order of advice based on the relevance of the tasks. For example, the providing unit gives priority to advice on tasks with high relevance. The providing unit can also postpone tasks with low relevance. The providing unit can also adjust the order of advice based on the relevance of the tasks. This allows for efficient advice by adjusting the order of advice based on the relevance of the tasks.

[0051] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the company's level of expertise. For example, the providing unit uses a lot of technical terms for companies with a high level of expertise. The providing unit can also avoid using technical terms for companies with a low level of expertise. The providing unit can also adjust the way the advice is expressed depending on the company's level of expertise. In this way, by adjusting the use of technical terms depending on the company's level of expertise, it is possible to provide advice that is easier to understand.

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

[0053] The analysis unit can analyze a company's past success stories and provide optimal advice for similar issues. For example, it can propose a similar strategy for a current issue based on a marketing strategy that was successful in the past. It can also provide solutions to current technical issues by referring to past methods for solving technical problems. It can also propose new business models based on past successful business models. This allows it to provide more proven advice by utilizing past success stories.

[0054] The analysis department can analyze trends specific to a company's industry and provide advice based on those trends. For example, it can propose new product development directions based on current market trends. It can also suggest the introduction of new technologies based on technological trends in the industry. It can also suggest marketing strategies based on consumer purchasing trends. This allows it to provide advice that is more in line with the times by utilizing industry-specific trends.

[0055] The analysis department can analyze the trends of a company's competitors and provide strategic advice to them. For example, it can propose a differentiation strategy for the company's own products based on the trends of competitors' new products. It can also analyze competitors' marketing strategies and propose counter-strategies. Furthermore, it can propose the direction of the company's own technological development based on the trends of competitors' technological development. This allows the company to provide more competitive advice by utilizing the trends of competitors.

[0056] The analysis department can utilize a company's internal data and provide advice based on that data. For example, it can analyze a company's sales data and propose strategies to increase sales. It can also propose measures to improve inventory management based on a company's inventory data. It can also analyze a company's employee data and propose measures to improve employee performance. In this way, by utilizing a company's internal data, it can provide more specific and practical advice.

[0057] The analysis department can analyze a company's customer data and provide advice based on that data. For example, it can analyze a customer's purchasing history and suggest products and services that meet their needs. It can also suggest areas for improving products and services based on customer feedback. It can also use customer segmentation data to suggest targeted marketing strategies. By utilizing customer data, it is possible to provide advice that is more tailored to the customer.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception department inputs the company's issues and requests for consultation. Issues and requests for consultation include, for example, management issues, technical problems, marketing strategies, etc. For example, a company may input an issue such as "We would like to conduct market research on a new product." Step 2: The analysis unit analyzes the information received by the reception unit and provides consulting based on the knowledge. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit also includes a learning unit that learns the knowledge provided by specialists and a consulting unit that provides consulting based on the learned knowledge. The consulting unit provides strategic advice, technical support, and proposes business models. Step 3: The provision unit provides advice to the company based on the analysis results obtained by the analysis unit. The provision unit provides advice in the form of a report, a presentation, real-time feedback, or other methods. The provision unit also includes a feedback unit that provides the generative AI's advice to the company and inputs the results of the company's actions back into the generative AI. The feedback unit inputs the results of the company's actions into the generative AI as quantitative evaluations and qualitative comments.

[0060] (Example 2) A consulting system according to an embodiment of the present invention automatically analyzes a company's issues and concerns and provides appropriate advice. The consulting system includes a reception unit that inputs the company's issues and concerns, an analysis unit that analyzes the information received by the reception unit and provides consulting services based on the acquired knowledge, and a provision unit that provides advice to the company based on the analysis results obtained by the analysis unit. For example, a company inputs an issue such as "I would like to conduct market research for a new product." This information is input into a generation AI. The generation AI then analyzes the input issue and concerns and provides consulting services based on the appropriate knowledge. The generation AI learns knowledge provided by specialists in various fields and provides optimal advice based on that knowledge. For example, the generation AI provides advice on market research methods and points to note. Furthermore, the company can take specific action based on the advice provided by the generation AI. For example, by conducting market research according to the generation AI's advice and then inputting the results back into the generation AI, further advice can be received. This system enables companies, even without specialized knowledge, to effectively solve their issues by receiving consulting services from the generation AI. Furthermore, knowledge providers receive a portion of the consulting fee as a return, encouraging the provision of knowledge. For example, if the information provided by a knowledge provider helps solve a company's problems, the provider will be paid a reward according to the results. This allows the consulting system to efficiently analyze the company's problems and inquiries and provide appropriate advice. For example, even if a company does not have specialized knowledge, it can effectively solve problems by receiving consulting from a generative AI. In addition, a portion of the consulting fee is returned to the knowledge provider, encouraging the provision of knowledge.

[0061] A consulting system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs a company's issues and requests for consultation. Examples of the company's issues and requests for consultation include, but are not limited to, management issues, technical problems, and marketing strategies. For example, the reception unit inputs a request such as, "I would like to conduct market research for a new product." The analysis unit analyzes the information received by the reception unit and provides consulting based on the acquired knowledge. The analysis unit performs the analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit includes a learning unit that learns knowledge provided by specialists. The learning unit learns the knowledge using, for example, machine learning algorithms. The analysis unit includes a consulting unit that provides consulting based on the learned knowledge. The consulting unit provides, for example, strategic advice, technical support, and business model proposals. The provision unit provides advice to the company based on the analysis results obtained by the analysis unit. The provision unit provides the advice in the form of, for example, a report, a presentation, or real-time feedback. The provision unit provides the advice of the generative AI to the company. The providing unit includes a feedback unit that inputs the results of the company's actions back into the generating AI. The feedback unit inputs the results of the company's actions into the generating AI, for example, as quantitative evaluations or qualitative comments. This enables the consulting system according to the embodiment to efficiently analyze the company's issues and consultations and provide appropriate advice.

[0062] The analysis unit includes a learning unit that learns knowledge provided by a specialist. The learning unit learns the knowledge provided by the specialist. Specialists include, but are not limited to, industry experts and certified consultants. The learning unit learns the knowledge using, for example, a machine learning algorithm. For example, the learning unit acquires knowledge using a database query and learns the knowledge using a machine learning algorithm. The learning unit can also build a model for the generative AI to provide consulting based on the knowledge provided by the specialist. This improves the accuracy of analysis by learning the specialist's knowledge.

[0063] The analysis unit includes a consulting unit that provides consulting based on the learned knowledge. The consulting unit provides consulting based on the learned knowledge. Consulting includes, but is not limited to, strategic advice, technical support, and business model proposals. The consulting unit uses, for example, a generative AI to provide optimal advice for a company's challenges and inquiries. For example, the generative AI provides advice on market research methods and points to note in response to a company's challenges. The consulting unit can also support the company in taking specific actions based on the advice provided by the generative AI. This allows for more appropriate advice to be provided by consulting based on the learned knowledge.

[0064] The provision unit provides the generative AI's advice to the company. The provision unit provides the generative AI's advice to the company. The generative AI includes, but is not limited to, a Transformer model. The provision unit provides the advice in the form of a report, a presentation, real-time feedback, or other methods. For example, the provision unit advises the generative AI on market research methods and points to note. The provision unit can also support the company in taking specific actions based on the advice provided by the generative AI. In this way, by providing the generative AI's advice to the company, the provision unit helps the company solve its problems.

[0065] The providing unit includes a feedback unit that inputs the results of the company's actions back into the generating AI. The feedback unit inputs the results of the company's actions back into the generating AI. The feedback includes, for example, quantitative evaluations, qualitative comments, user behavior data, etc., but is not limited to these examples. For example, the feedback unit inputs the results of the company's actions into the generating AI as quantitative evaluations. The feedback unit can also input the results of the company's actions into the generating AI as qualitative comments. The feedback unit can also input the company's behavior data into the generating AI, so that the generating AI can provide further advice based on that data. In this way, by feeding back the results of the company's actions, the accuracy of the advice provided by the generating AI is improved.

[0066] The reception unit can estimate the user's emotions and adjust the timing of inputting the problem or consultation based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit can prompt the user to input immediately, providing a smooth input experience. Furthermore, if the user is in a hurry, the reception unit can speed up the input timing to quickly accept the problem or consultation. This allows for a more appropriate input experience by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The reception unit can analyze the company's past assignment submission history and select an input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the company has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the company's past assignment submission history. The reception unit can also analyze the company's past assignment submission history and suggest the most efficient input method. This improves input efficiency by selecting the optimal input method based on past history.

[0068] When inputting a problem or consultation, the reception unit can filter the problem or consultation based on the company's current project or area of ​​interest. For example, the reception unit preferentially receives problem or consultation related to a project currently underway by the company. The reception unit can also filter related problem or consultation based on the company's area of ​​interest. The reception unit can also accept appropriate problem or consultation depending on the progress of the company's current project. In this way, by filtering based on the company's current situation, it is possible to accept more relevant problem or consultation.

[0069] When inputting an issue or consultation, the reception unit can select the optimal input means depending on the input method (voice, text, image, etc.) of the company. For example, if the company desires voice input, the reception unit can provide voice input with priority. Also, if the company desires text input, the reception unit can provide text input with priority. Also, if the company desires image input, the reception unit can provide image input with priority. This improves the convenience of input by selecting the optimal means depending on the company's input method.

[0070] The reception unit can estimate the user's emotions and determine the priority of input issues and consultations based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes receiving issues and consultations of high importance. Furthermore, when the user is relaxed, the reception unit can also accept issues and consultations of low importance. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving issues and consultations of high urgency. In this way, by determining the priority according to the user's emotions, it is possible to prioritize receiving more important issues and consultations. Emotion estimation is realized using an emotion estimation function using, 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.

[0071] When inputting a problem or consultation, the reception unit can prioritize inputting highly relevant problems by taking into account the geographical location information of the company. For example, the reception unit can prioritize accepting problems or consultations related to the location of the company. The reception unit can also prioritize accepting region-specific problems or consultations based on the geographical location information of the company. The reception unit can also suggest the optimal input method by taking into account the geographical location information of the company. In this way, region-specific problems or consultations can be prioritized by taking into account the geographical location information.

[0072] When inputting an issue or inquiry, the reception unit can analyze the company's social media activity and input related issues. For example, the reception unit can analyze the content of the company's social media posts and accept related issues or inquiries. The reception unit can also suggest the most suitable issue or inquiry based on the company's social media activity history. The reception unit can also analyze the reactions of the company's followers on social media and accept related issues or inquiries. In this way, by analyzing social media activity, it is possible to accept highly relevant issues and inquiries.

[0073] The reception unit can customize the input method by reflecting the company's past feedback when inputting an issue or consultation. For example, the reception unit can suggest the optimal input method based on feedback provided by the company in the past. The reception unit can also customize the input interface by reflecting the company's past feedback. The reception unit can also optimize the input procedure based on the company's past feedback. In this way, the input method can be optimized by reflecting past feedback.

[0074] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is feeling stressed, the analysis unit can also provide visually easy-to-understand analysis results. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task. For example, the analysis unit performs a detailed analysis on a task with a high level of importance. The analysis unit can also perform a concise analysis on a task with a low level of importance. The analysis unit can also adjust the depth of the analysis according to the importance of the task. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the task.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the problem. For example, the analysis unit applies a specific market research algorithm to a market research problem. The analysis unit can also apply an analysis algorithm specialized for product development to a product development problem. The analysis unit can also apply an analysis algorithm specialized for financial analysis to a financial analysis problem. In this way, the accuracy of the analysis is improved by applying an analysis algorithm according to the problem category.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the company's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the company's past analysis results. The analysis unit can also analyze the company's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0078] 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 to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually easy-to-understand analysis result if the user is stressed. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the assignment. For example, the analysis unit prioritizes analysis of assignments that are submitted early. The analysis unit can also postpone assignments that are submitted late. The analysis unit can also adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority based on the submission date.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. The analysis unit can also adjust the order of analysis based on the relevance of the tasks. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the tasks.

[0081] During the analysis, the analysis unit can adjust the use of technical terms in the analysis according to the company's level of expertise. For example, the analysis unit uses a lot of technical terms for companies with a high level of expertise. The analysis unit can also avoid using technical terms for companies with a low level of expertise. The analysis unit can also adjust the way the analysis results are presented according to the company's level of expertise. In this way, by adjusting the use of technical terms according to the company's level of expertise, it is possible to provide analysis results that are easier to understand.

[0082] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is in a hurry, the providing unit can provide concise advice that focuses on the main points. Furthermore, if the user is feeling stressed, the providing unit can provide visually easy-to-understand advice. In this way, by adjusting the way in which advice is expressed according to the user's emotions, more appropriate advice can be provided. 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 these examples.

[0083] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the task. For example, the providing unit can provide detailed advice for tasks with high importance. The providing unit can also provide concise advice for tasks with low importance. The providing unit can also adjust the depth of the advice depending on the importance of the task. This allows for efficient advice by adjusting the level of detail of the advice depending on the importance of the task.

[0084] When providing advice, the providing unit can apply different advice algorithms depending on the category of the problem. For example, the providing unit applies a specific market research advice algorithm to a market research problem. The providing unit can also apply an advice algorithm specialized for product development to a product development problem. The providing unit can also apply an advice algorithm specialized for financial analysis to a financial analysis problem. In this way, by applying an advice algorithm according to the problem category, the accuracy of advice is improved.

[0085] When providing advice, the providing unit can improve the accuracy of the advice by referring to the company's past advice results. The providing unit can improve the accuracy of current advice, for example, based on the company's past advice results. The providing unit can also optimize the advice algorithm by referring to the company's past advice results. The providing unit can also analyze the company's past advice results and improve the accuracy of the advice. In this way, the accuracy of advice is improved by referring to the past advice results.

[0086] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is feeling stressed, the providing unit can provide visually easy-to-understand advice. In this way, by adjusting the length of advice according to the user's emotions, more appropriate advice can be provided. 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 these examples.

[0087] When providing advice, the providing unit can determine the priority of advice based on the submission time of the assignment. For example, the providing unit gives priority to advice on assignments that are to be submitted early. The providing unit can also postpone assignments that are to be submitted late. The providing unit can also adjust the advice schedule based on the submission time. This allows for efficient advice by determining the priority based on the submission time.

[0088] When providing advice, the providing unit can adjust the order of advice based on the relevance of the tasks. For example, the providing unit gives priority to advice on tasks with high relevance. The providing unit can also postpone tasks with low relevance. The providing unit can also adjust the order of advice based on the relevance of the tasks. This allows for efficient advice by adjusting the order of advice based on the relevance of the tasks.

[0089] When providing advice, the providing unit can adjust the use of technical terms in the advice depending on the company's level of expertise. For example, the providing unit uses a lot of technical terms for companies with a high level of expertise. The providing unit can also avoid using technical terms for companies with a low level of expertise. The providing unit can also adjust the way the advice is expressed depending on the company's level of expertise. In this way, by adjusting the use of technical terms depending on the company's level of expertise, it is possible to provide advice that is easier to understand. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input a company's issues or consultations using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and provides consulting based on knowledge. The provision unit can provide advice to the company using the output device 40 of the smart device 14. For example, the generation AI can provide advice on market research methods and points to note, and the results can be input again into the generation AI to receive further advice. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input a company's issues or consultations using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and provides consulting based on knowledge. The provision unit can provide advice to the company using the speaker 240 of the smart glasses 214. For example, the generation AI can provide advice on market research methods and points to note, and the results can be input again into the generation AI to receive further advice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the company's issues and consultations using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and provides consulting based on knowledge. The provision unit can provide advice to the company using the speaker 240 of the headset-type terminal 314. For example, the generation AI can give advice on market research methods and points to note, and the results can be input again into the generation AI to receive further advice. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the company's issues and consultations using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and provides consulting based on knowledge. The provision unit can provide advice to the company using the speaker 240 of the robot 414. For example, the generation AI can give advice on market research methods and points to note, and the results can be input again into the generation AI to receive further advice.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can analyze a company's past success stories and provide optimal advice for similar issues. For example, it can propose a similar strategy for a current issue based on a marketing strategy that was successful in the past. It can also provide solutions to current technical issues by referring to past methods for solving technical problems. It can also propose new business models based on past successful business models. This allows it to provide more proven advice by utilizing past success stories.

[0092] The providing unit can estimate the user's emotions and adjust the tone of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice can be provided in a gentle tone. If the user is relaxed, the advice can be provided in a friendly tone. Furthermore, if the user is in a hurry, the advice can be provided in a concise and direct tone. In this way, by adjusting the tone of the advice according to the user's emotions, it is possible to provide advice that is more easily accepted.

[0093] The analysis department can analyze trends specific to a company's industry and provide advice based on those trends. For example, it can propose new product development directions based on current market trends. It can also suggest the introduction of new technologies based on technological trends in the industry. It can also suggest marketing strategies based on consumer purchasing trends. This allows it to provide advice that is more in line with the times by utilizing industry-specific trends.

[0094] The providing unit can estimate the user's emotions and adjust the format of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice can be provided using visually easy-to-understand graphs and diagrams. If the user is relaxed, the advice can be provided in detailed text format. Furthermore, if the user is in a hurry, the advice can be provided in concise bullet-point format. In this way, by adjusting the format of the advice according to the user's emotions, it is possible to provide advice that is easier to understand.

[0095] The analysis department can analyze the trends of a company's competitors and provide strategic advice to them. For example, it can propose a differentiation strategy for the company's own products based on the trends of competitors' new products. It can also analyze competitors' marketing strategies and propose counter-strategies. Furthermore, it can propose the direction of the company's own technological development based on the trends of competitors' technological development. This allows the company to provide more competitive advice by utilizing the trends of competitors.

[0096] The providing unit can estimate the user's emotions and adjust the frequency of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of advice can be reduced to reduce the burden. Also, if the user is relaxed, the frequency of advice can be increased to provide detailed support. Furthermore, if the user is in a hurry, the minimum amount of advice necessary can be provided. In this way, more appropriate support can be provided by adjusting the frequency of advice according to the user's emotions.

[0097] The analysis department can utilize a company's internal data and provide advice based on that data. For example, it can analyze a company's sales data and propose strategies to increase sales. It can also propose measures to improve inventory management based on a company's inventory data. It can also analyze a company's employee data and propose measures to improve employee performance. In this way, by utilizing a company's internal data, it can provide more specific and practical advice.

[0098] The providing unit can estimate the user's emotions and adjust the content of advice based on the estimated user's emotions. For example, if the user is feeling stressed, simple and easy-to-follow advice can be provided. If the user is relaxed, detailed and complex advice can be provided. Furthermore, if the user is in a hurry, advice that is effective immediately can be provided. In this way, by adjusting the content of advice according to the user's emotions, more easy-to-follow advice can be provided.

[0099] The analysis department can analyze a company's customer data and provide advice based on that data. For example, it can analyze a customer's purchasing history and suggest products and services that meet their needs. It can also suggest areas for improving products and services based on customer feedback. It can also use customer segmentation data to suggest targeted marketing strategies. By utilizing customer data, it is possible to provide advice that is more tailored to the customer.

[0100] The providing unit can estimate the user's emotions and adjust the timing of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of advice can be delayed to provide time for the user to relax. Also, if the user is relaxed, advice can be provided immediately. Furthermore, if the user is in a hurry, advice can be provided quickly. In this way, by adjusting the timing of advice according to the user's emotions, advice can be provided at more appropriate times.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception department inputs the company's issues and requests for consultation. Issues and requests for consultation include, for example, management issues, technical problems, marketing strategies, etc. For example, a company may input an issue such as "We would like to conduct market research on a new product." Step 2: The analysis unit analyzes the information received by the reception unit and provides consulting based on the knowledge. The analysis unit performs analysis using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit also includes a learning unit that learns the knowledge provided by specialists and a consulting unit that provides consulting based on the learned knowledge. The consulting unit provides strategic advice, technical support, and proposes business models. Step 3: The provision unit provides advice to the company based on the analysis results obtained by the analysis unit. The provision unit provides advice in the form of a report, a presentation, real-time feedback, or other methods. The provision unit also includes a feedback unit that provides the generative AI's advice to the company and inputs the results of the company's actions back into the generative AI. The feedback unit inputs the results of the company's actions into the generative AI as quantitative evaluations and qualitative comments.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where companies can input their issues and inquiries, an analysis unit that analyzes the information received by the reception unit and provides consulting based on the knowledge; a providing unit that provides advice to the company based on the analysis results obtained by the analysis unit. A system characterized by:

2. The analysis unit Equipping a learning department to learn knowledge provided by specialists 2. The system of claim 1.

3. The analysis unit Establish a consulting department that provides consulting based on the knowledge learned 2. The system of claim 1.

4. The providing unit Providing generative AI advice to businesses 2. The system of claim 1.

5. The providing unit Equipped with a feedback section that inputs the results of the company's actions back into the generation AI 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of inputting issues and inquiries based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the company's past assignment submission history and select the input method 2. The system of claim 1.

8. The reception unit When entering your issues or inquiries, filter them based on your company's current projects and areas of interest 2. The system of claim 1.

9. The reception unit When entering issues or inquiries, select the most appropriate input method depending on the company's input method.

2. The system of claim 1.

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