system

Abstract

The system according to the embodiment aims to automatically identify the appropriate department or person in charge when an employee does not know who to consult about a task within the company. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the consultation content. The analysis unit analyzes the consultation content received by the reception unit and identifies an appropriate department or person in charge. The provision unit provides information on the department or person in charge identified by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when you don't know who to consult about a business within the company, it can be difficult to find the appropriate department or person in charge.

[0005] The system according to the embodiment aims to automatically identify the appropriate department or person in charge when an employee does not know who to consult about a task within the company. [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 consultation content. The analysis unit analyzes the consultation content received by the reception unit and identifies an appropriate department or person in charge. The provision unit provides information on the department or person in charge identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically identify the appropriate department and person in charge when an employee does not know who to consult about a task within the company. [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 business consultation matching system according to an embodiment of the present invention uses AI to automatically find and match the appropriate department and person in charge when users are unsure of who to consult about a particular business matter. This business consultation matching system allows users to input their consultation details, and AI analyzes the details, identifies the appropriate department and person in charge, and provides the user with information about the identified department and person. For example, a user simply inputs a specific consultation detail, such as a "technical question" or an "accounting question." This information is input into the AI, which uses natural language processing technology to understand the consultation and identify the appropriate department and person in charge. For example, if the question is technical, a technical department person will be identified, and if the question is accounting, a person in the accounting department will be identified. The information about the identified department and person in charge is provided to the user, allowing the user to consult based on that information. For example, the contact information and department information of the identified person are provided. This system allows users to quickly and accurately find the appropriate department and person in charge, thereby improving work efficiency. Furthermore, because the AI ​​performs automatic matching, users can easily find someone to consult with. This allows the business consultation matching system to quickly and accurately find the appropriate department or person in charge, thereby improving business efficiency.

[0029] A business consultation matching system according to an embodiment includes a reception unit, an analysis unit, and a providing unit. The reception unit receives consultation content input by a user. The consultation content input by a user includes, but is not limited to, technical consultations, business consultations, and personal consultations. The reception unit receives the consultation content input by the user in text format, for example. The reception unit can also receive the consultation content using voice input. For example, the user dictates the consultation content using a microphone, and the reception unit receives the voice data. The reception unit can also suggest an appropriate input method based on consultation content previously input by the user. For example, the reception unit can automatically display consultation content frequently input by the user in the past as candidates. The analysis unit uses AI to analyze the consultation content received by the reception unit and identify an appropriate department or person in charge. The analysis unit analyzes the consultation content using, for example, natural language processing technology. For example, the analysis unit analyzes the text data of the consultation content and extracts appropriate keywords. The analysis unit can also identify a category of the consultation content using a machine learning algorithm. For example, the analysis unit classifies technical consultation content into a technical category and accounting consultation content into an accounting category. Furthermore, the analysis unit can search for an appropriate department or person in charge from an internal database. For example, the analysis unit accesses an internal database and identifies the department or person in charge based on past correspondence history and their area of ​​expertise. The provision unit provides the user with information about the department or person in charge identified by the analysis unit. For example, the provision unit displays the identified person's contact information and department information to the user. The provision unit can also provide profile information about the identified person in charge. For example, the provision unit displays the person's area of ​​expertise and past correspondence history to the user. Furthermore, the provision unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is nervous, the provision unit provides simple, highly visible information. This allows the business consultation matching system according to the embodiment to quickly and accurately find the appropriate department or person in charge, thereby improving work efficiency.

[0030] The reception unit can analyze the user's past consultation history and suggest an appropriate input method. For example, the reception unit can automatically display consultation contents that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest consultation contents to be used in a specific time period based on the user's past consultation history. This can improve input efficiency by suggesting the optimal input method based on the user's past consultation history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI suggest the optimal input method.

[0031] When inputting consultation content, the reception unit can filter the input content based on the user's current work situation or area of ​​interest. For example, the reception unit can prioritize displaying consultation content related to the user's current project. The reception unit can also filter related consultation content based on the user's area of ​​interest. Furthermore, the reception unit can also suggest appropriate consultation content according to the user's work situation. This can improve input efficiency by preferentially displaying consultation content according to the user's work situation or area of ​​interest. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's work situation data to the generation AI and cause the generation AI to filter appropriate consultation content.

[0032] When inputting consultation content, the reception unit can prioritize accepting consultation content that is highly relevant based on the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize accepting consultation content related to that office. Furthermore, if the user is on a business trip, the reception unit can prioritize accepting consultation content related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize accepting consultation content related to remote work. This enables more appropriate responses by prioritizing acceptance of consultation content based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize acceptance of consultation content that is highly relevant.

[0033] The reception unit can analyze the user's social media activity when inputting the consultation content and accept related consultation content. For example, the reception unit can prioritize accepting consultation content related to business shared by the user on social media. The reception unit can also prioritize accepting consultation content related to topics the user follows on social media. Furthermore, the reception unit can also prioritize accepting consultation content related to groups the user participates in on social media. This enables more appropriate responses by prioritizing acceptance of consultation content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to accept related consultation content.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the consultation content. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. The analysis unit can also perform a simplified analysis on consultation content with a low level of importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This makes it possible to take appropriate action by performing an analysis according to the importance of the consultation content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the consultation content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit can apply a technical analysis algorithm to technical consultation content. The analysis unit can also apply an accounting analysis algorithm to accounting-related consultation content. The analysis unit can also apply a human resources analysis algorithm to human resources-related consultation content. This makes it possible to perform an analysis according to the category of the consultation content and take an appropriate response. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the consultation content to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time when the consultation content was received. For example, the analysis unit prioritizes analysis of urgent consultation content. The analysis unit can also analyze normal consultation content with normal priority. Furthermore, the analysis unit can adjust the priority of analysis according to the time of submission. In this way, by setting the priority according to the time when the consultation content was submitted, consultation content with high urgency can be processed quickly. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the consultation content was received into the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the correlation of the consultation contents. For example, the analysis unit prioritizes analysis of consultation contents with high relevance. The analysis unit can also postpone analysis of consultation contents with low relevance. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the consultation contents. This makes it possible to take appropriate action by performing analysis according to the relevance of the consultation contents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input correlation data of the consultation contents to the generation AI and cause the generation AI to adjust the order of analysis.

[0038] The providing unit can adjust the level of detail of the provided information based on the priority of the identified responsible department or person in charge when providing the information. For example, the providing unit provides detailed information to responsible departments or people with high importance. The providing unit can also provide simplified information to responsible departments or people with low importance. Furthermore, the providing unit can adjust the level of detail of the provided information according to the importance. This enables appropriate responses by providing information according to the importance of the responsible department or person in charge. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the responsible department or person in charge into the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0039] When providing the information, the providing unit can apply different provision methods depending on the category of the identified responsible department or person in charge. For example, the providing unit can provide technical information to a person in the technical department. The providing unit can also provide accounting information to a person in the accounting department. The providing unit can also provide human resources information to a person in the human resources department. This enables appropriate responses by providing information according to the category of the responsible department or person in charge. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of the responsible department or person in charge into the generation AI and cause the generation AI to apply an appropriate provision method.

[0040] The providing unit can adjust the provided information based on the geographic location information of the identified responsible department or person in charge when providing the information. For example, if the responsible department or person in charge is in a different office, the providing unit can provide information related to that office. Furthermore, if the responsible department or person in charge is on a business trip, the providing unit can also provide information related to the business trip destination. Furthermore, if the responsible department or person in charge is working remotely, the providing unit can also provide information related to the remote work. This enables more appropriate responses by providing information based on the geographic location information of the responsible department or person in charge. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographic location information data of the responsible department or person in charge into the generation AI and cause the generation AI to adjust the provided information.

[0041] When providing information, the providing unit can improve the accuracy of the provided information by referring to literature related to the identified department or person in charge. The providing unit, for example, provides information by referring to literature previously written by the department or person in charge. Furthermore, if the department or person in charge is conducting related research, the providing unit can also provide information by referring to the results of that research. Furthermore, if the department or person in charge is participating in a related project, the providing unit can also provide information by referring to information about that project. By doing so, the accuracy of the provided information is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without AI. For example, the providing unit can input literature data related to the department or person in charge into a generating AI and have the generating AI improve the accuracy of the provided information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The reception unit can analyze the user's past consultation history and automatically suggest similar problems based on the problems the user has solved in the past. For example, if the user has solved a technical problem in the past, the reception unit can suggest a solution related to the current consultation based on that solution. Similarly, if the user has solved an accounting problem in the past, the reception unit can make a suggestion based on that solution. Furthermore, the reception unit can suggest that the user consult with the same person again based on information about the person the user consulted with in the past. This allows the user to quickly solve the problem by leveraging past experience.

[0044] The reception unit can automatically filter related consultation contents based on the user's current work situation and areas of interest. For example, consultation contents related to a project the user is currently working on can be preferentially displayed. The reception unit can also filter related consultation contents based on the user's areas of interest. Furthermore, it can also suggest appropriate consultation contents depending on the user's work situation. This can improve input efficiency by preferentially displaying consultation contents that correspond to the user's work situation and areas of interest.

[0045] The reception unit can prioritize receiving highly relevant consultation content based on the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize receiving consultation content related to that office. Also, if the user is on a business trip, the reception unit can prioritize receiving consultation content related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize receiving consultation content related to remote work. This allows for more appropriate responses by prioritizing receiving consultation content based on the user's geographical location information.

[0046] The analysis unit can adjust the level of detail of the analysis based on the priority of the consultation content. For example, a detailed analysis can be performed for consultation content with a high level of importance. Also, a simplified analysis can be performed for consultation content with a low level of importance. Furthermore, the depth of the analysis can be adjusted according to the level of importance. This makes it possible to respond appropriately by performing an analysis according to the level of importance of the consultation content.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, a technical analysis algorithm can be applied to technical consultation content. Also, an accounting analysis algorithm can be applied to accounting consultation content. Furthermore, a human resources analysis algorithm can be applied to human resources consultation content. This makes it possible to perform an analysis according to the category of the consultation content and respond appropriately.

[0048] The information providing department can improve the accuracy of the provided information by referring to literature related to the identified department or person in charge. For example, the information provided can refer to literature previously written by the department or person in charge. Also, if the department or person in charge is conducting related research, the information can be provided by referring to the results of that research. Furthermore, if the department or person in charge is participating in a related project, the information can be provided by referring to information about that project. In this way, by referring to related literature, the accuracy of the provided information can be improved.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The reception unit receives the consultation content input by the user. The consultation content input by the user may include, for example, technical consultation, business consultation, personal consultation, etc. The reception unit can receive the consultation content in text format or using voice input. Furthermore, the reception unit can also suggest an appropriate input method based on consultation content input by the user in the past. Step 2: The analysis unit uses AI to analyze the consultation content received by the reception unit and identify the appropriate department and person in charge. The analysis unit uses natural language processing technology to analyze the consultation content and extract appropriate keywords. It also uses machine learning algorithms to identify the category of the consultation content and search the company's internal database for the appropriate department and person in charge. Step 3: The providing unit provides the user with information about the department and person in charge identified by the analysis unit. The providing unit displays the contact information, department information, and profile information of the identified person in charge to the user. Furthermore, the providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions.

[0051] (Example 2) A business consultation matching system according to an embodiment of the present invention uses AI to automatically find and match the appropriate department and person in charge when users are unsure of who to consult about a particular business matter. This business consultation matching system allows users to input their consultation details, and AI analyzes the details, identifies the appropriate department and person in charge, and provides the user with information about the identified department and person. For example, a user simply inputs a specific consultation detail, such as a "technical question" or an "accounting question." This information is input into the AI, which uses natural language processing technology to understand the consultation and identify the appropriate department and person in charge. For example, if the question is technical, a technical department person will be identified, and if the question is accounting, a person in the accounting department will be identified. The information about the identified department and person in charge is provided to the user, allowing the user to consult based on that information. For example, the contact information and department information of the identified person are provided. This system allows users to quickly and accurately find the appropriate department and person in charge, thereby improving work efficiency. Furthermore, because the AI ​​performs automatic matching, users can easily find someone to consult with. This allows the business consultation matching system to quickly and accurately find the appropriate department or person in charge, thereby improving business efficiency.

[0052] A business consultation matching system according to an embodiment includes a reception unit, an analysis unit, and a providing unit. The reception unit receives consultation content input by a user. The consultation content input by a user includes, but is not limited to, technical consultations, business consultations, and personal consultations. The reception unit receives the consultation content input by the user in text format, for example. The reception unit can also receive the consultation content using voice input. For example, the user dictates the consultation content using a microphone, and the reception unit receives the voice data. The reception unit can also suggest an appropriate input method based on consultation content previously input by the user. For example, the reception unit can automatically display consultation content frequently input by the user in the past as candidates. The analysis unit uses AI to analyze the consultation content received by the reception unit and identify an appropriate department or person in charge. The analysis unit analyzes the consultation content using, for example, natural language processing technology. For example, the analysis unit analyzes the text data of the consultation content and extracts appropriate keywords. The analysis unit can also identify a category of the consultation content using a machine learning algorithm. For example, the analysis unit classifies technical consultation content into a technical category and accounting consultation content into an accounting category. Furthermore, the analysis unit can search for an appropriate department or person in charge from an internal database. For example, the analysis unit accesses an internal database and identifies the department or person in charge based on past correspondence history and their area of ​​expertise. The provision unit provides the user with information about the department or person in charge identified by the analysis unit. For example, the provision unit displays the identified person's contact information and department information to the user. The provision unit can also provide profile information about the identified person in charge. For example, the provision unit displays the person's area of ​​expertise and past correspondence history to the user. Furthermore, the provision unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is nervous, the provision unit provides simple, highly visible information. This allows the business consultation matching system according to the embodiment to quickly and accurately find the appropriate department or person in charge, thereby improving work efficiency.

[0053] The reception unit can estimate the user's emotions and adjust the consultation content input interface based on the user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the consultation content. This provides an interface tailored to the user's emotions, reducing the user's stress and improving input efficiency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0054] The reception unit can analyze the user's past consultation history and suggest an appropriate input method. For example, the reception unit can automatically display consultation contents that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest consultation contents to be used in a specific time period based on the user's past consultation history. This can improve input efficiency by suggesting the optimal input method based on the user's past consultation history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI suggest the optimal input method.

[0055] When inputting consultation content, the reception unit can filter the input content based on the user's current work situation or area of ​​interest. For example, the reception unit can prioritize displaying consultation content related to the user's current project. The reception unit can also filter related consultation content based on the user's area of ​​interest. Furthermore, the reception unit can also suggest appropriate consultation content according to the user's work situation. This can improve input efficiency by preferentially displaying consultation content according to the user's work situation or area of ​​interest. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's work situation data to the generation AI and cause the generation AI to filter appropriate consultation content.

[0056] The reception unit can estimate the user's emotions and determine the priority of the consultation contents input based on the user's emotions. For example, if the user is nervous, the reception unit can prioritize consultation contents with a high level of urgency. Furthermore, if the user is relaxed, the reception unit can also prioritize consultation contents that require a quick response if the user is in a hurry. By setting priorities according to the user's emotions, it is possible to quickly process consultation contents with a high level of urgency. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0057] When inputting consultation content, the reception unit can prioritize accepting consultation content that is highly relevant based on the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize accepting consultation content related to that office. Furthermore, if the user is on a business trip, the reception unit can prioritize accepting consultation content related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize accepting consultation content related to remote work. This enables more appropriate responses by prioritizing acceptance of consultation content based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize acceptance of consultation content that is highly relevant.

[0058] The reception unit can analyze the user's social media activity when inputting the consultation content and accept related consultation content. For example, the reception unit can prioritize accepting consultation content related to business shared by the user on social media. The reception unit can also prioritize accepting consultation content related to topics the user follows on social media. Furthermore, the reception unit can also prioritize accepting consultation content related to groups the user participates in on social media. This enables more appropriate responses by prioritizing acceptance of consultation content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to accept related consultation content.

[0059] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This can facilitate user understanding by providing analysis results that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the consultation content. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. The analysis unit can also perform a simplified analysis on consultation content with a low level of importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This makes it possible to take appropriate action by performing an analysis according to the importance of the consultation content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the consultation content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0061] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit can apply a technical analysis algorithm to technical consultation content. The analysis unit can also apply an accounting analysis algorithm to accounting-related consultation content. The analysis unit can also apply a human resources analysis algorithm to human resources-related consultation content. This makes it possible to perform an analysis according to the category of the consultation content and take an appropriate response. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the consultation content to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0062] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This can promote user understanding by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0063] During analysis, the analysis unit can determine the priority of analysis based on the time when the consultation content was received. For example, the analysis unit prioritizes analysis of urgent consultation content. The analysis unit can also analyze normal consultation content with normal priority. Furthermore, the analysis unit can adjust the priority of analysis according to the time of submission. In this way, by setting the priority according to the time when the consultation content was submitted, consultation content with high urgency can be processed quickly. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the consultation content was received into the generation AI and have the generation AI determine the priority of analysis.

[0064] During analysis, the analysis unit can adjust the order of analysis based on the correlation of the consultation contents. For example, the analysis unit prioritizes analysis of consultation contents with high relevance. The analysis unit can also postpone analysis of consultation contents with low relevance. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the consultation contents. This makes it possible to take appropriate action by performing analysis according to the relevance of the consultation contents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input correlation data of the consultation contents to the generation AI and cause the generation AI to adjust the order of analysis.

[0065] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can also provide detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide information that focuses on the main points. This can promote user understanding by providing information according to the user's emotions. 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0066] The providing unit can adjust the level of detail of the provided information based on the priority of the identified responsible department or person in charge when providing the information. For example, the providing unit provides detailed information to responsible departments or people with high importance. The providing unit can also provide simplified information to responsible departments or people with low importance. Furthermore, the providing unit can adjust the level of detail of the provided information according to the importance. This enables appropriate responses by providing information according to the importance of the responsible department or person in charge. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the responsible department or person in charge into the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0067] When providing the information, the providing unit can apply different provision methods depending on the category of the identified responsible department or person in charge. For example, the providing unit can provide technical information to a person in the technical department. The providing unit can also provide accounting information to a person in the accounting department. The providing unit can also provide human resources information to a person in the human resources department. This enables appropriate responses by providing information according to the category of the responsible department or person in charge. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of the responsible department or person in charge into the generation AI and cause the generation AI to apply an appropriate provision method.

[0068] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the user's emotions. For example, if the user is nervous, the providing unit can prioritize providing information with a high level of urgency. Furthermore, if the user is relaxed, the providing unit can also prioritize providing information with a normal priority. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information that requires a quick response. This allows for the prompt provision of information with a high level of urgency by setting priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0069] The providing unit can adjust the provided information based on the geographic location information of the identified responsible department or person in charge when providing the information. For example, if the responsible department or person in charge is in a different office, the providing unit can provide information related to that office. Furthermore, if the responsible department or person in charge is on a business trip, the providing unit can also provide information related to the business trip destination. Furthermore, if the responsible department or person in charge is working remotely, the providing unit can also provide information related to the remote work. This enables more appropriate responses by providing information based on the geographic location information of the responsible department or person in charge. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographic location information data of the responsible department or person in charge into the generation AI and cause the generation AI to adjust the provided information.

[0070] When providing information, the providing unit can improve the accuracy of the provided information by referring to literature related to the identified department or person in charge. The providing unit, for example, provides information by referring to literature previously written by the department or person in charge. Furthermore, if the department or person in charge is conducting related research, the providing unit can also provide information by referring to the results of that research. Furthermore, if the department or person in charge is participating in a related project, the providing unit can also provide information by referring to information about that project. By doing so, the accuracy of the provided information is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without AI. For example, the providing unit can input literature data related to the department or person in charge into a generating AI and have the generating AI improve the accuracy of the provided information. === 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 is realized by the reception device 38 of the smart device 14 and receives the consultation content entered by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using AI and identifies the appropriate department or person in charge. The provision unit is realized by the output device 40 of the smart device 14 and provides the user with information on the identified department or person in charge. === 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 is realized by the microphone 238 of the smart glasses 214 and receives the consultation content entered by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using AI and identifies the appropriate department or person in charge. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the user with information on the identified department or person in charge. === 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 terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives the consultation content input by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using AI and identifies the appropriate department or person in charge. The provision unit is realized by the display 343 of the headset terminal 314 and provides the user with information on the identified department or person in charge. === 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 is realized by the microphone 238 of the robot 414 and receives the consultation content input by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using AI and identifies the appropriate department or person in charge. The provision unit is realized by the speaker 240 of the robot 414 and provides the user with information on the identified department or person in charge.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reception unit can analyze the user's past consultation history and automatically suggest similar problems based on the problems the user has solved in the past. For example, if the user has solved a technical problem in the past, the reception unit can suggest a solution related to the current consultation based on that solution. Similarly, if the user has solved an accounting problem in the past, the reception unit can make a suggestion based on that solution. Furthermore, the reception unit can suggest that the user consult with the same person again based on information about the person the user consulted with in the past. This allows the user to quickly solve the problem by leveraging past experience.

[0073] The reception unit can estimate the user's emotions and customize the consultation content input method based on the user's emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the consultation content to be input quickly. In this way, by providing an interface that corresponds to the user's emotions, it is possible to reduce the user's stress and improve input efficiency.

[0074] The reception unit can automatically filter related consultation contents based on the user's current work situation and areas of interest. For example, consultation contents related to a project the user is currently working on can be preferentially displayed. The reception unit can also filter related consultation contents based on the user's areas of interest. Furthermore, it can also suggest appropriate consultation contents depending on the user's work situation. This can improve input efficiency by preferentially displaying consultation contents that correspond to the user's work situation and areas of interest.

[0075] The reception unit can prioritize receiving highly relevant consultation content based on the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize receiving consultation content related to that office. Also, if the user is on a business trip, the reception unit can prioritize receiving consultation content related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize receiving consultation content related to remote work. This allows for more appropriate responses by prioritizing receiving consultation content based on the user's geographical location information.

[0076] The reception unit can estimate the user's emotions and determine the priority of the consultation contents input based on the user's emotions. For example, if the user is nervous, the consultation contents with a high level of urgency can be given priority. Also, if the user is relaxed, the consultation contents can be processed with normal priority. Furthermore, if the user is in a hurry, the consultation contents that require a quick response can be processed with priority. In this way, by setting the priority according to the user's emotions, the consultation contents with a high level of urgency can be processed quickly.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. This can promote user understanding by providing analysis results that correspond to the user's emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the priority of the consultation content. For example, a detailed analysis can be performed for consultation content with a high level of importance. Also, a simplified analysis can be performed for consultation content with a low level of importance. Furthermore, the depth of the analysis can be adjusted according to the level of importance. This makes it possible to respond appropriately by performing an analysis according to the level of importance of the consultation content.

[0079] The analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, a technical analysis algorithm can be applied to technical consultation content. Also, an accounting analysis algorithm can be applied to accounting consultation content. Furthermore, a human resources analysis algorithm can be applied to human resources consultation content. This makes it possible to perform an analysis according to the category of the consultation content and respond appropriately.

[0080] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that focuses on the main points can be provided. In this way, by providing information according to the user's emotions, it is possible to promote the user's understanding.

[0081] The information providing department can improve the accuracy of the provided information by referring to literature related to the identified department or person in charge. For example, the information provided can refer to literature previously written by the department or person in charge. Also, if the department or person in charge is conducting related research, the information can be provided by referring to the results of that research. Furthermore, if the department or person in charge is participating in a related project, the information can be provided by referring to information about that project. In this way, by referring to related literature, the accuracy of the provided information can be improved.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The reception unit receives the consultation content input by the user. The consultation content input by the user may include, for example, technical consultation, business consultation, personal consultation, etc. The reception unit can receive the consultation content in text format or using voice input. Furthermore, the reception unit can also suggest an appropriate input method based on consultation content input by the user in the past. Step 2: The analysis unit uses AI to analyze the consultation content received by the reception unit and identify the appropriate department and person in charge. The analysis unit uses natural language processing technology to analyze the consultation content and extract appropriate keywords. It also uses machine learning algorithms to identify the category of the consultation content and search the company's internal database for the appropriate department and person in charge. Step 3: The providing unit provides the user with information about the department and person in charge identified by the analysis unit. The providing unit displays the contact information, department information, and profile information of the identified person in charge to the user. Furthermore, the providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0086] 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.

[0087] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section where consultation details are entered; an analysis unit that analyzes the consultation content received by the reception unit and identifies an appropriate department or person in charge; a providing unit that provides information on the responsible department or person in charge identified by the analysis unit; Equipped with A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the consultation content input interface based on the user's emotions.

2. The system of claim 1.

3. The reception unit Analyzes the user's past consultation history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit As users type their questions, filter their input based on their current work situation or areas of interest.

2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the consultation content based on the user's emotions.

2. The system of claim 1.

6. The reception unit When entering a consultation, the system prioritizes relevant consultations based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When a user enters a consultation, the system analyzes the user's social media activity and accepts related consultations.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

9. The analysis unit During analysis, adjust the level of detail of the analysis based on the priority of the consultation content.

2. The system of claim 1.

10. The analysis unit During analysis, different analysis algorithms are applied depending on the category of the consultation content.

2. The system of claim 1.

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