system

Abstract

The system according to the embodiment aims to identify and provide an appropriate person to consult with based on the content of the user's consultation. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a list-up unit, and a provision unit. The reception unit receives consultation content from a user. The analysis unit analyzes the consultation content received by the reception unit and identifies a category. The list-up unit lists consultation partners selected based on the consultation content, based on the category identified by the analysis unit. The provision unit provides the consultation partners listed by the list-up unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to quickly identify an appropriate person to consult with in response to the content of a user's consultation.

[0005] The system according to the embodiment aims to identify and provide an appropriate person to consult with based on the content of the user's consultation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a list-up unit, and a provision unit. The reception unit receives consultation content from a user. The analysis unit analyzes the consultation content received by the reception unit and identifies a category. The list-up unit lists consultation partners selected based on the consultation content, based on the category identified by the analysis unit. The provision unit provides the consultation partners listed by the list-up unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can identify and provide an appropriate person to consult with based on the content of the user's consultation. [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 consultation partner listing system according to an embodiment of the present invention analyzes input content and lists appropriate consultation partners. This system analyzes the consultation content entered by a user and lists appropriate consultation partners based on the consultation content. For example, if a user enters "I would like to consult about the progress of a new project," this information is entered into the system. Next, the system uses natural language processing technology to analyze the input consultation content and identify the category of the consultation content. For example, it classifies the consultation content into categories such as "project management," "technical issues," and "legal issues." Based on the analyzed consultation content, the system lists appropriate consultation partners. For example, if the consultation content is related to "project management," project managers and project leaders are listed. If the consultation content is related to "technical issues," technicians and engineers are listed. If the consultation content is related to "legal issues," legal department staff and lawyers are listed. This mechanism allows users to quickly find appropriate consultation partners. For example, if a user wants to consult about the progress of a new project, project managers and project leaders are listed, allowing the user to quickly access an appropriate consultation partner. Furthermore, if you want to consult about a technical issue, a list of technicians and engineers will be displayed, allowing you to receive professional advice. Furthermore, if you need advice about legal matters, a list of legal department staff and lawyers will be displayed, allowing you to receive legal advice. In this way, a system that analyzes the input content and lists appropriate people to consult with is an effective means for users to quickly find the right person to consult with. This allows the consultation list system to efficiently analyze the content of the user's consultation and provide a list of appropriate people to consult with.

[0029] A consultation partner listing system according to an embodiment includes a reception unit, an analysis unit, a list-up unit, and a provision unit. The reception unit receives consultation content from a user. For example, if a user inputs "I would like to consult about the progress of a new project," this information is input to the reception unit. The reception unit can receive the consultation content, for example, through a web interface or a mobile app. The analysis unit analyzes the consultation content received by the reception unit and identifies a category. The analysis unit analyzes the consultation content and identifies a category using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit classifies the consultation content into categories such as "project management," "technical issues," and "legal issues." The list-up unit lists appropriate consultation partners based on the category identified by the analysis unit. For example, the list-up unit searches a database for appropriate consultation partners and lists them. The database includes a relational database, a NoSQL database, and the like. For example, if the consultation content is about project management, the list-up unit lists project managers and project leaders. The providing unit provides the consultation partners listed by the listing unit to the user. For example, the providing unit displays the listed consultation partners to the user so that the user can select one. The providing unit can display the consultation partners through a web interface or a mobile app. This allows the consultation partner listing system according to the embodiment to efficiently analyze the consultation content of the user and list and provide appropriate consultation partners.

[0030] The analysis unit can analyze the consultation content and identify a category using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit uses morphological analysis to divide words in the consultation content, uses grammatical analysis to analyze the structure of the sentence, and uses semantic analysis to understand the meaning of the sentence. This allows the analysis unit to classify the consultation content into categories such as "project management," "technical issues," and "legal affairs." For example, if a user inputs "I would like to consult about the progress of a new project," the analysis unit classifies this consultation content into the "project management" category. In this way, the use of natural language processing technology improves the accuracy of the analysis of the consultation content.

[0031] The list-up unit can search for and list appropriate people to consult from a database. Databases include relational databases and NoSQL databases. For example, the list-up unit can use a relational database to search for people to consult related to the consultation content. The list-up unit can also use a NoSQL database to quickly search for appropriate people to consult from large amounts of data. For example, if the consultation content is about project management, the list-up unit will list project managers and project leaders. In this way, by using a database, appropriate people to consult can be quickly listed.

[0032] The providing unit can display the listed consultants to the user and enable selection. The providing unit can display the consultants through, for example, a web interface or a mobile app. For example, the providing unit can display the listed consultants to the user and enable the user to select one. The providing unit can also display detailed information about the listed consultants. For example, the providing unit can display information such as the consultant's job title, field of expertise, and years of experience. This allows the user to select one of the listed consultants.

[0033] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, retrieves and analyzes the user's past consultation history from a database. For example, the reception unit prioritizes and suggests reception methods (such as voice and text) that the user has frequently used in the past. The reception unit can also prioritize reception of consultations related to a specific category based on the user's past consultation history. For example, if the user has made many consultations about "technical issues" in the past, the reception unit prioritizes reception of consultations related to technical issues. The reception unit can also analyze the user's past consultation history and suggest the optimal reception time slot. For example, the reception unit analyzes the time slots during which the user made past consultations and suggests the optimal reception time slot based on those time slots. This makes it possible to provide the optimal reception method based on the user's past consultation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI select the optimal reception method.

[0034] The reception unit may filter the consultation content based on the user's current project or area of ​​interest when receiving the consultation content. The reception unit, for example, retrieves the user's current project information from a database and filters the consultation content when receiving the consultation content. For example, the reception unit may prioritize receiving consultation content related to the user's current project. The reception unit may also filter and receive related consultation content based on the user's area of ​​interest. For example, the reception unit may identify the user's area of ​​interest based on the user's past search history or survey results, and prioritize receiving consultation content related to that area. The reception unit may also refer to the user's project history and prioritize receiving related consultation content. For example, the reception unit may prioritize receiving related consultation content based on information about projects the user has been involved in in the past. This allows appropriate consultation content to be received based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's project information and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0035] When accepting a consultation content, the reception unit can prioritize accepting highly relevant consultation content by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and takes the information into consideration when accepting the consultation content. For example, if the user is in a specific area, the reception unit prioritizes accepting consultation content related to that area. The reception unit can also prioritize accepting consultation content related to issues specific to the area based on the user's geographical location information. For example, if the user is traveling, the reception unit accepts the most appropriate consultation content based on the user's current location. This allows for prioritized acceptance of highly relevant 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 a generation AI and cause the generation AI to prioritize highly relevant consultation content.

[0036] When receiving a consultation content, the reception unit can analyze the user's social media activity and receive related consultation content. The reception unit, for example, analyzes the user's social media posts and prioritizes receiving related consultation content. For example, the reception unit receives related consultation content based on the user's social media interests. The reception unit can also receive related consultation content by referring to the user's social media activity history. For example, the reception unit analyzes the content of the user's social media posts, the number of likes, the number of followers, etc., to identify related consultation content. This makes it possible to receive related consultation content based on the user's social media activity. Some or all of the above-described processing by 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 social media data into a generation AI and cause the generation AI to identify related consultation content.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. The analysis unit, for example, evaluates the importance of the consultation content and adjusts the level of detail of the analysis based on the evaluation. For example, the analysis unit performs a detailed analysis for consultation content with a high level of importance. The analysis unit can also perform a concise analysis for consultation content with a low level of importance. For example, the analysis unit adjusts the depth of the analysis according to the importance. This makes it possible to adjust the level of detail of the 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.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit identifies the category of the consultation content and applies an appropriate analysis algorithm depending on the category. For example, the analysis unit applies a technical analysis algorithm to technical issues. The analysis unit can also apply a legal analysis algorithm to legal issues. For example, the analysis unit applies a project management analysis algorithm to issues related to project management. This makes it possible to apply an appropriate analysis algorithm depending on the category 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 category data of the consultation content to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the consultation content. For example, the analysis unit obtains the time of submission of the consultation content from a database and determines the priority of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of the consultation content that was submitted most recently. The analysis unit can also postpone analysis of consultation content that was submitted earlier. For example, the analysis unit adjusts the order of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission 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 data on the time of submission of the consultation content to the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. The analysis unit, for example, evaluates the relevance of the consultation contents and adjusts the order of analysis based on the evaluation. For example, the analysis unit performs simultaneous analysis when the consultation contents are related. Furthermore, the analysis unit can also perform separate analysis when the consultation contents are unrelated. For example, the analysis unit adjusts the order of analysis based on the relevance of the consultation contents. This makes it possible to adjust the order of analysis based on the relevance of the consultation contents. Some or all of the above-described 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 relevance data of the consultation contents to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] When listing, the list-up unit can improve the accuracy of the list by taking into account the interrelationships between the consultation contents. The list-up unit, for example, evaluates the interrelationships between the consultation contents and improves the accuracy of the list based on the evaluation. For example, when the consultation contents are related, the list-up unit simultaneously lists related consultation partners. Furthermore, when the consultation contents are unrelated, the list-up unit can also list separate consultation partners. For example, the list-up unit improves the accuracy of the list based on the interrelationships between the consultation contents. This makes it possible to improve the accuracy of the list based on the interrelationships between the consultation contents. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input interrelationship data between the consultation contents to a generation AI and cause the generation AI to improve the accuracy of the list.

[0042] When creating a list, the list-up unit can take into consideration attribute information of the person who submitted the consultation content. For example, the list-up unit obtains attribute information of the person who submitted the consultation content from a database and creates a list based on that information. For example, the list-up unit lists appropriate people to consult with based on the job position of the person who submitted the consultation content. The list-up unit can also list relevant people to consult with based on the specialty field of the person who submitted the consultation content. For example, the list-up unit lists appropriate people to consult with based on the years of experience of the person who submitted the consultation content. This makes it possible to create a list of appropriate people to consult with based on the attribute information of the person who submitted the consultation content. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input attribute information data of the person who submitted the consultation content to a generation AI and have the generation AI perform the list-up.

[0043] The list-up unit can take the geographic distribution of the consultation content into consideration when making the list. The list-up unit, for example, obtains the geographic distribution of the consultation content from a database and makes the list based on that information. For example, if the consultation content relates to a specific region, the list-up unit prioritizes listing consultants in that region. Furthermore, if the consultation content relates to a wide region, the list-up unit can also list consultants from multiple regions. For example, the list-up unit improves the accuracy of the list based on the geographic distribution of the consultation content. This makes it possible to list appropriate consultants based on the geographic distribution of the consultation content. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input geographic distribution data of the consultation content to a generation AI and have the generation AI perform the list-up.

[0044] When making a list, the list-up unit can improve the accuracy of the list by referring to literature related to the consultation content. For example, the list-up unit obtains literature related to the consultation content from a database and makes a list based on that information. For example, the list-up unit refers to literature related to the consultation content and lists appropriate consultants. The list-up unit can also list professional consultants based on research papers related to the consultation content. For example, the list-up unit refers to patent information related to the consultation content and lists appropriate consultants. This improves the accuracy of the list based on the literature related to the consultation content. Some or all of the above-described processing in the list-up unit may be performed using, or without, AI. For example, the list-up unit may input literature data related to the consultation content into a generation AI and cause the generation AI to improve the accuracy of the list.

[0045] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, acquires the user's past operation history from a database and selects the optimal display method based on that information. For example, the providing unit prioritizes providing display methods that the user has used in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and selects the optimal display method. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generation AI and cause the generation AI to select the optimal display method.

[0046] The providing unit can customize the display content of the information based on the user's current situation when providing the information. The providing unit, for example, obtains the user's current situation from a database and customizes the display content of the information based on that information. For example, when the user is on the move, the providing unit provides a concise and highly visible display method. The providing unit can also provide a display method including detailed information when the user is at their desk. For example, when the user is in a meeting, the providing unit provides a display method that focuses on the main points. This allows the display content of the information to be customized according to the user's current situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to the generating AI and cause the generating AI to customize the display content of the information.

[0047] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. The providing unit, for example, acquires the user's device information from a database and selects the optimal display method based on that information. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0048] The providing unit can analyze the user's social media activities and provide the relevant information at the time of providing. The providing unit, for example, analyzes the user's social media posts and provides the relevant information. For example, the providing unit provides the relevant information based on the user's social media interests. The providing unit can also refer to the user's social media activity history to provide the relevant information. For example, the providing unit analyzes the content of the user's social media posts, the number of likes, the number of followers, etc., to identify the relevant information. This makes it possible to provide the relevant information based on the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to identify the relevant information.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can translate the user's input in real time and list people who can speak multiple languages. For example, if a user inputs their consultation content in English, the reception unit translates the content into Japanese and lists people who can speak Japanese. Also, if a user inputs their consultation content in French, the reception unit can list people who can speak French. This allows users who speak different languages ​​to find someone who is suitable for them to consult.

[0051] The analysis unit can evaluate the urgency of the consultation content based on the user's input and prioritize analysis of consultation content with a high level of urgency. For example, if the user explicitly states "urgent," the analysis unit will give top priority to analyzing that content. The analysis unit can also estimate the urgency from the context of the consultation content and prioritize processing content with a high level of urgency. This allows for a prompt response to urgent consultation content.

[0052] The list-up unit can refer to the user's past consultation history and prioritize listing people with whom the user has previously consulted. For example, the list-up unit can re-list a project manager with whom the user previously consulted. The list-up unit can also evaluate compatibility with specific people from the past consultation history and prioritize listing people with good compatibility. This allows the user to access trusted people for consultation again.

[0053] The providing unit can select the optimal display format based on the usage status of the user's device. For example, if the user is using a smartphone, the providing unit can provide a mobile-friendly display format. Also, if the user is using a desktop, the providing unit can provide a display format including detailed information. This allows the user to receive information optimized for the device they are using.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit accepts the consultation content from the user. For example, if the user inputs "I would like to consult about the progress of a new project," this information is entered into the reception unit. The reception unit can accept the consultation content through a web interface or a mobile app. Step 2: The analysis unit analyzes the consultation content received by the reception unit and identifies a category. The analysis unit analyzes the consultation content and identifies a category using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit classifies the consultation content into categories such as "project management," "technical issues," and "legal issues." Step 3: The listing unit creates a list of appropriate consultants based on the categories identified by the analysis unit. The listing unit searches a database for appropriate consultants and creates a list. Databases include relational databases and NoSQL databases. For example, if the consultation is about project management, the listing unit will create a list of project managers and project leaders. Step 4: The providing unit provides the consultation partners listed by the listing unit to the user. The providing unit displays the listed consultation partners to the user so that the user can select one. The providing unit can display the consultation partners through a web interface or a mobile app.

[0056] (Example 2) A consultation partner listing system according to an embodiment of the present invention analyzes input content and lists appropriate consultation partners. This system analyzes the consultation content entered by a user and lists appropriate consultation partners based on the consultation content. For example, if a user enters "I would like to consult about the progress of a new project," this information is entered into the system. Next, the system uses natural language processing technology to analyze the input consultation content and identify the category of the consultation content. For example, it classifies the consultation content into categories such as "project management," "technical issues," and "legal issues." Based on the analyzed consultation content, the system lists appropriate consultation partners. For example, if the consultation content is related to "project management," project managers and project leaders are listed. If the consultation content is related to "technical issues," technicians and engineers are listed. If the consultation content is related to "legal issues," legal department staff and lawyers are listed. This mechanism allows users to quickly find appropriate consultation partners. For example, if a user wants to consult about the progress of a new project, project managers and project leaders are listed, allowing the user to quickly access an appropriate consultation partner. Furthermore, if you want to consult about a technical issue, a list of technicians and engineers will be displayed, allowing you to receive professional advice. Furthermore, if you need advice about legal matters, a list of legal department staff and lawyers will be displayed, allowing you to receive legal advice. In this way, a system that analyzes the input content and lists appropriate people to consult with is an effective means for users to quickly find the right person to consult with. This allows the consultation list system to efficiently analyze the content of the user's consultation and provide a list of appropriate people to consult with.

[0057] A consultation partner listing system according to an embodiment includes a reception unit, an analysis unit, a list-up unit, and a provision unit. The reception unit receives consultation content from a user. For example, if a user inputs "I would like to consult about the progress of a new project," this information is input to the reception unit. The reception unit can receive the consultation content, for example, through a web interface or a mobile app. The analysis unit analyzes the consultation content received by the reception unit and identifies a category. The analysis unit analyzes the consultation content and identifies a category using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit classifies the consultation content into categories such as "project management," "technical issues," and "legal issues." The list-up unit lists appropriate consultation partners based on the category identified by the analysis unit. For example, the list-up unit searches a database for appropriate consultation partners and lists them. The database includes a relational database, a NoSQL database, and the like. For example, if the consultation content is about project management, the list-up unit lists project managers and project leaders. The providing unit provides the consultation partners listed by the listing unit to the user. For example, the providing unit displays the listed consultation partners to the user so that the user can select one. The providing unit can display the consultation partners through a web interface or a mobile app. This allows the consultation partner listing system according to the embodiment to efficiently analyze the consultation content of the user and list and provide appropriate consultation partners.

[0058] The analysis unit can analyze the consultation content and identify a category using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit uses morphological analysis to divide words in the consultation content, uses grammatical analysis to analyze the structure of the sentence, and uses semantic analysis to understand the meaning of the sentence. This allows the analysis unit to classify the consultation content into categories such as "project management," "technical issues," and "legal affairs." For example, if a user inputs "I would like to consult about the progress of a new project," the analysis unit classifies this consultation content into the "project management" category. In this way, the use of natural language processing technology improves the accuracy of the analysis of the consultation content.

[0059] The list-up unit can search for and list appropriate people to consult from a database. Databases include relational databases and NoSQL databases. For example, the list-up unit can use a relational database to search for people to consult related to the consultation content. The list-up unit can also use a NoSQL database to quickly search for appropriate people to consult from large amounts of data. For example, if the consultation content is about project management, the list-up unit will list project managers and project leaders. In this way, by using a database, appropriate people to consult can be quickly listed.

[0060] The providing unit can display the listed consultants to the user and enable selection. The providing unit can display the consultants through, for example, a web interface or a mobile app. For example, the providing unit can display the listed consultants to the user and enable the user to select one. The providing unit can also display detailed information about the listed consultants. For example, the providing unit can display information such as the consultant's job title, field of expertise, and years of experience. This allows the user to select one of the listed consultants.

[0061] The reception unit can estimate the user's emotions and adjust the timing of accepting consultation requests based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the reception unit to accept consultation requests at an appropriate time according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0062] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, retrieves and analyzes the user's past consultation history from a database. For example, the reception unit prioritizes and suggests reception methods (such as voice and text) that the user has frequently used in the past. The reception unit can also prioritize reception of consultations related to a specific category based on the user's past consultation history. For example, if the user has made many consultations about "technical issues" in the past, the reception unit prioritizes reception of consultations related to technical issues. The reception unit can also analyze the user's past consultation history and suggest the optimal reception time slot. For example, the reception unit analyzes the time slots during which the user made past consultations and suggests the optimal reception time slot based on those time slots. This makes it possible to provide the optimal reception method based on the user's past consultation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI select the optimal reception method.

[0063] The reception unit may filter the consultation content based on the user's current project or area of ​​interest when receiving the consultation content. The reception unit, for example, retrieves the user's current project information from a database and filters the consultation content when receiving the consultation content. For example, the reception unit may prioritize receiving consultation content related to the user's current project. The reception unit may also filter and receive related consultation content based on the user's area of ​​interest. For example, the reception unit may identify the user's area of ​​interest based on the user's past search history or survey results, and prioritize receiving consultation content related to that area. The reception unit may also refer to the user's project history and prioritize receiving related consultation content. For example, the reception unit may prioritize receiving related consultation content based on information about projects the user has been involved in in the past. This allows appropriate consultation content to be received based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's project information and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0064] The reception unit can estimate the user's emotions and determine the priority of consultation requests to be received based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the priority of consultation requests to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0065] When accepting a consultation content, the reception unit can prioritize accepting highly relevant consultation content by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and takes the information into consideration when accepting the consultation content. For example, if the user is in a specific area, the reception unit prioritizes accepting consultation content related to that area. The reception unit can also prioritize accepting consultation content related to issues specific to the area based on the user's geographical location information. For example, if the user is traveling, the reception unit accepts the most appropriate consultation content based on the user's current location. This allows for prioritized acceptance of highly relevant 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 a generation AI and cause the generation AI to prioritize highly relevant consultation content.

[0066] When receiving a consultation content, the reception unit can analyze the user's social media activity and receive related consultation content. The reception unit, for example, analyzes the user's social media posts and prioritizes receiving related consultation content. For example, the reception unit receives related consultation content based on the user's social media interests. The reception unit can also receive related consultation content by referring to the user's social media activity history. For example, the reception unit analyzes the content of the user's social media posts, the number of likes, the number of followers, etc., to identify related consultation content. This makes it possible to receive related consultation content based on the user's social media activity. Some or all of the above-described processing by 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 social media data into a generation AI and cause the generation AI to identify related consultation content.

[0067] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the expression method of the analysis to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0068] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. The analysis unit, for example, evaluates the importance of the consultation content and adjusts the level of detail of the analysis based on the evaluation. For example, the analysis unit performs a detailed analysis for consultation content with a high level of importance. The analysis unit can also perform a concise analysis for consultation content with a low level of importance. For example, the analysis unit adjusts the depth of the analysis according to the importance. This makes it possible to adjust the level of detail of the 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.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit identifies the category of the consultation content and applies an appropriate analysis algorithm depending on the category. For example, the analysis unit applies a technical analysis algorithm to technical issues. The analysis unit can also apply a legal analysis algorithm to legal issues. For example, the analysis unit applies a project management analysis algorithm to issues related to project management. This makes it possible to apply an appropriate analysis algorithm depending on the category 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 category data of the consultation content to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0070] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the length of the analysis to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0071] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the consultation content. For example, the analysis unit obtains the time of submission of the consultation content from a database and determines the priority of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of the consultation content that was submitted most recently. The analysis unit can also postpone analysis of consultation content that was submitted earlier. For example, the analysis unit adjusts the order of analysis based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission 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 data on the time of submission of the consultation content to the generation AI and have the generation AI determine the priority of analysis.

[0072] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. The analysis unit, for example, evaluates the relevance of the consultation contents and adjusts the order of analysis based on the evaluation. For example, the analysis unit performs simultaneous analysis when the consultation contents are related. Furthermore, the analysis unit can also perform separate analysis when the consultation contents are unrelated. For example, the analysis unit adjusts the order of analysis based on the relevance of the consultation contents. This makes it possible to adjust the order of analysis based on the relevance of the consultation contents. Some or all of the above-described 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 relevance data of the consultation contents to the generation AI and cause the generation AI to adjust the order of analysis.

[0073] The list-up unit can estimate the user's emotion and adjust the listing criteria based on the estimated user's emotion. For example, the list-up unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the list-up unit can calculate an emotion score based on changes in facial expression. The list-up unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the list-up unit can analyze the tone and speed of the voice and calculate an emotion score. The list-up unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the list-up unit can calculate an emotion score based on heart rate fluctuations. This allows the listing criteria to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] When listing, the list-up unit can improve the accuracy of the list by taking into account the interrelationships between the consultation contents. The list-up unit, for example, evaluates the interrelationships between the consultation contents and improves the accuracy of the list based on the evaluation. For example, when the consultation contents are related, the list-up unit simultaneously lists related consultation partners. Furthermore, when the consultation contents are unrelated, the list-up unit can also list separate consultation partners. For example, the list-up unit improves the accuracy of the list based on the interrelationships between the consultation contents. This makes it possible to improve the accuracy of the list based on the interrelationships between the consultation contents. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input interrelationship data between the consultation contents to a generation AI and cause the generation AI to improve the accuracy of the list.

[0075] When creating a list, the list-up unit can take into consideration attribute information of the person who submitted the consultation content. For example, the list-up unit obtains attribute information of the person who submitted the consultation content from a database and creates a list based on that information. For example, the list-up unit lists appropriate people to consult with based on the job position of the person who submitted the consultation content. The list-up unit can also list relevant people to consult with based on the specialty field of the person who submitted the consultation content. For example, the list-up unit lists appropriate people to consult with based on the years of experience of the person who submitted the consultation content. This makes it possible to create a list of appropriate people to consult with based on the attribute information of the person who submitted the consultation content. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input attribute information data of the person who submitted the consultation content to a generation AI and have the generation AI perform the list-up.

[0076] The list-up unit can estimate the user's emotion and adjust the order in which the list results are displayed based on the estimated user's emotion. For example, the list-up unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the list-up unit can calculate an emotion score based on changes in facial expression. The list-up unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the list-up unit can analyze the tone and speed of the voice and calculate an emotion score. The list-up unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the list-up unit can calculate an emotion score based on heart rate fluctuations. This makes it possible to adjust the order in which the list results are displayed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The list-up unit can take the geographic distribution of the consultation content into consideration when making the list. The list-up unit, for example, obtains the geographic distribution of the consultation content from a database and makes the list based on that information. For example, if the consultation content relates to a specific region, the list-up unit prioritizes listing consultants in that region. Furthermore, if the consultation content relates to a wide region, the list-up unit can also list consultants from multiple regions. For example, the list-up unit improves the accuracy of the list based on the geographic distribution of the consultation content. This makes it possible to list appropriate consultants based on the geographic distribution of the consultation content. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input geographic distribution data of the consultation content to a generation AI and have the generation AI perform the list-up.

[0078] When making a list, the list-up unit can improve the accuracy of the list by referring to literature related to the consultation content. For example, the list-up unit obtains literature related to the consultation content from a database and makes a list based on that information. For example, the list-up unit refers to literature related to the consultation content and lists appropriate consultants. The list-up unit can also list professional consultants based on research papers related to the consultation content. For example, the list-up unit refers to patent information related to the consultation content and lists appropriate consultants. This improves the accuracy of the list based on the literature related to the consultation content. Some or all of the above-described processing in the list-up unit may be performed using, or without, AI. For example, the list-up unit may input literature data related to the consultation content into a generation AI and cause the generation AI to improve the accuracy of the list.

[0079] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This makes it possible to adjust the display method of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of a user taken with a camera into the generating AI and have the generating AI estimate the user's emotions.

[0080] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit, for example, acquires the user's past operation history from a database and selects the optimal display method based on that information. For example, the providing unit prioritizes providing display methods that the user has used in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and selects the optimal display method. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generation AI and cause the generation AI to select the optimal display method.

[0081] The providing unit can customize the display content of the information based on the user's current situation when providing the information. The providing unit, for example, obtains the user's current situation from a database and customizes the display content of the information based on that information. For example, when the user is on the move, the providing unit provides a concise and highly visible display method. The providing unit can also provide a display method including detailed information when the user is at their desk. For example, when the user is in a meeting, the providing unit provides a display method that focuses on the main points. This allows the display content of the information to be customized according to the user's current situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to the generating AI and cause the generating AI to customize the display content of the information.

[0082] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates the emotion score based on heart rate fluctuations. This allows the priority of information to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of a user taken with a camera into the generating AI and have the generating AI estimate the user's emotions.

[0083] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. The providing unit, for example, acquires the user's device information from a database and selects the optimal display method based on that information. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible display method. This makes it possible to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0084] The providing unit can analyze the user's social media activities and provide the relevant information at the time of providing. The providing unit, for example, analyzes the user's social media posts and provides the relevant information. For example, the providing unit provides the relevant information based on the user's social media interests. The providing unit can also refer to the user's social media activity history to provide the relevant information. For example, the providing unit analyzes the content of the user's social media posts, the number of likes, the number of followers, etc., to identify the relevant information. This makes it possible to provide the relevant information based on the user's social media activities. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to identify the relevant information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, listing unit, and providing 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 consultation content from a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using natural language processing technology and identifies a category. The listing unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and searches for appropriate consultation partners from the database 24 and lists them. The providing unit is realized, for example, by the output device 40 of the smart device 14 and displays the listed consultation partners to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, listing unit, and providing 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 from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content and identifies a category using natural language processing technology. The listing unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and searches for and lists appropriate consultation partners from the database 24. The providing unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the listed consultation partners to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, listing unit, and providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the consultation content from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using natural language processing technology and identifies a category. The listing unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and searches for and lists appropriate consultation partners from the database 24. The providing unit is realized, for example, by the display 343 of the headset-type terminal 314 and displays the listed consultation partners to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, listing unit, and providing 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 from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using natural language processing technology and identifies a category. The listing unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and searches for and lists appropriate consultation partners from the database 24. The providing unit is realized, for example, by the speaker 240 of the robot 414 and provides the listed consultation partners to the user.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The reception unit can translate the user's input in real time and list people who can speak multiple languages. For example, if a user inputs their consultation content in English, the reception unit translates the content into Japanese and lists people who can speak Japanese. Also, if a user inputs their consultation content in French, the reception unit can list people who can speak French. This allows users who speak different languages ​​to find someone who is suitable for them to consult.

[0087] The analysis unit can evaluate the urgency of the consultation content based on the user's input and prioritize analysis of consultation content with a high level of urgency. For example, if the user explicitly states "urgent," the analysis unit will give top priority to analyzing that content. The analysis unit can also estimate the urgency from the context of the consultation content and prioritize processing content with a high level of urgency. This allows for a prompt response to urgent consultation content.

[0088] The list-up unit can refer to the user's past consultation history and prioritize listing people with whom the user has previously consulted. For example, the list-up unit can re-list a project manager with whom the user previously consulted. The list-up unit can also evaluate compatibility with specific people from the past consultation history and prioritize listing people with good compatibility. This allows the user to access trusted people for consultation again.

[0089] The providing unit can select the optimal display format based on the usage status of the user's device. For example, if the user is using a smartphone, the providing unit can provide a mobile-friendly display format. Also, if the user is using a desktop, the providing unit can provide a display format including detailed information. This allows the user to receive information optimized for the device they are using.

[0090] The reception unit can estimate the user's emotions and adjust the reception method for the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive the consultation content in the form of a simple question. On the other hand, if the user is relaxed, the reception unit can receive the consultation content in the form of a detailed question. This makes it possible to provide an appropriate reception method according to the user's emotions.

[0091] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can use a concise and reassuring way of expression. Alternatively, if the user is excited, the analysis unit can use a detailed and specific way of expression. This makes it possible to provide appropriate analysis results according to the user's emotions.

[0092] The list-up unit can estimate the user's emotions and adjust the display order of the listed consultants based on the estimated user's emotions. For example, if the user is feeling impatient, the list-up unit can display consultants who can respond most quickly at the top. Also, if the user is calm, the list-up unit can display consultants with high expertise at the top. This makes it possible to provide a consultant appropriate to the user's emotions.

[0093] The providing unit can estimate the user's emotions and adjust the level of detail of the information to be provided based on the estimated user's emotions. For example, if the user is tired, the providing unit can provide concise, to-the-point information. Also, if the user is interested, the providing unit can provide detailed, in-depth information. This makes it possible to provide appropriate information according to the user's emotions.

[0094] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can prioritize displaying the most important information. Also, if the user is relaxed, the providing unit can sequentially display detailed information. This makes it possible to provide appropriate information according to the user's emotions.

[0095] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can use a display method that is visually relaxing. Also, if the user is excited, the providing unit can use a display method that is visually stimulating. This makes it possible to provide appropriate information display according to the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The reception unit accepts the consultation content from the user. For example, if the user inputs "I would like to consult about the progress of a new project," this information is entered into the reception unit. The reception unit can accept the consultation content through a web interface or a mobile app. Step 2: The analysis unit analyzes the consultation content received by the reception unit and identifies a category. The analysis unit analyzes the consultation content and identifies a category using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit classifies the consultation content into categories such as "project management," "technical issues," and "legal issues." Step 3: The listing unit creates a list of appropriate consultants based on the categories identified by the analysis unit. The listing unit searches a database for appropriate consultants and creates a list. Databases include relational databases and NoSQL databases. For example, if the consultation is about project management, the listing unit will create a list of project managers and project leaders. Step 4: The providing unit provides the consultation partners listed by the listing unit to the user. The providing unit displays the listed consultation partners to the user so that the user can select one. The providing unit can display the consultation partners through a web interface or a mobile app.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives inquiries from users; an analysis unit that analyzes the consultation content received by the reception unit and identifies a category; a list-up unit that lists counselors selected based on the content of the consultation, based on the category identified by the analysis unit; a providing unit that provides the consultation partners listed by the listing unit to a user. A system characterized by:

2. The analysis unit Analyzes the content of inquiries using natural language processing technology and identifies categories The system of claim 1 .

3. The list-up unit Search and list appropriate consultants from the database The system of claim 1 .

4. The providing unit Display the list of consultants to the user and allow them to select The system of claim 1 .

5. The reception unit Estimates the user's emotions and adjusts the timing of accepting consultations based on the estimated user emotions. The system of claim 1 .

6. The reception unit Analyze the user's past consultation history and select the appropriate reception method based on the content of the consultation. The system of claim 1 .

7. The reception unit Filtering enquiries based on the user's current projects and interests The system of claim 1 .

8. The reception unit Estimate the user's emotions and prioritize the consultation content to be accepted based on the estimated user emotions. The system of claim 1 .

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