system

Abstract

The system according to the embodiment aims to enable experts to promptly and appropriately consult on specialized knowledge. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content from an expert. The analysis unit analyzes the consultation content received by the reception unit. The provision unit provides related expertise based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide a means for experts to promptly and appropriately consult on specialized knowledge, and there is room for improvement.

[0005] The system according to the embodiment aims to enable experts to promptly and appropriately consult on specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content from an expert. The analysis unit analyzes the consultation content received by the reception unit. The provision unit provides related expertise based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment may enable experts to be consulted quickly and appropriately for their expertise. [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 generative AI service according to an embodiment of the present invention is a system that allows experts to consult on specialized knowledge. In this system, experts input their consultation details into a generative AI, which then analyzes the consultation details and provides relevant specialized knowledge. For example, when a doctor consults on a diagnostic method for a specific case, the doctor inputs the details of the case into the generative AI. The generative AI then analyzes the input information and provides relevant specialized knowledge. The generative AI then analyzes the input consultation details. The generative AI then searches for knowledge related to the input information based on a vast database of previously trained specialized knowledge and provides appropriate information. For example, when a doctor consults on a diagnostic method for a specific case, the generative AI provides diagnostic methods and treatments related to that case. Furthermore, the information provided by the generative AI can be used as reference information for experts to supplement their own knowledge. For example, when a lawyer consults on the interpretation of a specific legal issue, the generative AI provides relevant laws and precedents, providing reference information for the lawyer to make a more appropriate decision. This system allows experts to supplement their own knowledge and make more appropriate decisions. For example, a doctor can use the generative AI to confirm a diagnostic method for a specific case, leading to a more accurate diagnosis. In addition, lawyers can use generative AI to confirm interpretations of specific legal issues, allowing them to provide more appropriate legal advice. In this way, generative AI services that allow experts to consult on specialized knowledge are a mechanism that supplements the experts' own knowledge and provides reference information for making more appropriate decisions. In this way, generative AI services can supplement the experts' specialized knowledge and allow them to make more appropriate decisions.

[0029] A generation AI service according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content from an expert. The consultation content from the expert includes, but is not limited to, technical consultations, business consultations, and the like. The reception unit provides, for example, an interface through which the expert inputs the consultation content to the generation AI. The reception unit can also collect necessary information when the expert inputs the consultation content and save it in an appropriate format. For example, the reception unit saves the consultation content entered by the expert in text format and transmits it to the analysis unit. The analysis unit uses the generation AI to analyze the consultation content received by the reception unit. The analysis can be performed using, for example, text analysis or data mining, but is not limited to, these examples. For example, the analysis unit searches for knowledge related to the input information based on a vast database of specialized knowledge that the generation AI has previously learned, and provides appropriate information. The analysis unit can also use the generation AI to extract key points from the input consultation content and organize related information. For example, when the generation AI receives a prompt such as, "Please provide information related to this consultation content," the analysis unit searches for related information and creates a summary. The providing unit provides related specialized knowledge based on the information analyzed by the analyzing unit. Examples of the specialized knowledge provided include, but are not limited to, medical knowledge and legal knowledge. For example, the providing unit provides reference information for experts to supplement their own knowledge based on the information analyzed by the generating AI. The providing unit can also verify the reliability of the provided information using the generating AI. For example, the providing unit evaluates the reliability of the information provided by the generating AI and provides reliable information to the experts. This allows the generating AI service according to the embodiment to supplement the experts' specialized knowledge and make more appropriate decisions. Some or all of the above-described processing by the providing unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the providing unit can provide reliable information to experts based on the information analyzed by the generating AI.

[0030] The generative AI service includes a database construction unit that constructs and updates a specialized knowledge database. The database construction unit constructs and updates the specialized knowledge database. Examples of specialized knowledge databases include, but are not limited to, medical knowledge, legal knowledge, and technical knowledge. The database construction unit can automatically construct and update the specialized knowledge database using, for example, a generative AI. For example, the database construction unit allows the generative AI to collect public data and academic papers on the Internet and add them to the specialized knowledge database. The database construction unit can also update the specialized knowledge database based on information provided by experts. For example, the database construction unit adds the latest research results and practical experience provided by experts to the database. This improves the accuracy of the information provided by building and updating the specialized knowledge database. Some or all of the above-described processing in the database construction unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the database construction unit can automatically update the specialized knowledge database based on data collected by the generative AI.

[0031] The generation AI service includes a verification unit that verifies the reliability of the provided information. The verification unit verifies the reliability of the provided information. Verification of reliability includes, but is not limited to, the reliability of the information source and the accuracy of the data. The verification unit can automatically evaluate the reliability of the provided information, for example, using the generation AI. For example, the verification unit causes the generation AI to evaluate the reliability of the information source and select highly reliable information. The verification unit can also verify the accuracy of the provided information. For example, the verification unit compares the information provided by the generation AI with other highly reliable information and evaluates the accuracy. This verifies the reliability of the provided information, allowing experts to use the information with confidence. Some or all of the above-described processing in the verification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the verification unit causes the generation AI to evaluate the reliability of the information source and select highly reliable information.

[0032] The analysis unit can search for knowledge related to the input information based on multiple specialized knowledge databases previously trained by the generation AI and provide appropriate information. The analysis unit can search for knowledge related to the input information based on multiple specialized knowledge databases previously trained by the generation AI and provide appropriate information. Examples of multiple specialized knowledge databases include, but are not limited to, medical databases, legal databases, and technical databases. For example, the analysis unit can analyze the input consultation content and search for related specialized knowledge. The analysis unit can also use the generation AI to extract key points from the input information and organize related information. For example, when the generation AI receives a prompt such as "Please provide information related to this consultation content," the analysis unit can search for related information and create a summary. This allows relevant knowledge to be quickly provided by analyzing the input information based on the pre-trained databases. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can search for knowledge related to the input information based on the database previously trained by the generation AI and provide appropriate information.

[0033] The providing unit can provide reference information for experts to supplement their knowledge. For example, the providing unit provides reference information for experts to supplement their knowledge based on information analyzed by the generation AI. Examples of reference information include, but are not limited to, literature information and past cases. For example, the providing unit provides reference information for experts to supplement their knowledge based on information provided by the generation AI. The providing unit can also verify the reliability of the provided information using the generation AI. For example, the providing unit evaluates the reliability of the information provided by the generation AI and provides reliable information to the expert. This allows the expert to make more appropriate decisions by providing reference information to supplement their knowledge. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can provide reliable information to the expert based on information analyzed by the generation AI.

[0034] The reception unit can analyze the expert's past consultation history and select an appropriate reception method. For example, the reception unit preferentially suggests reception methods that the expert has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the expert's past consultation history. Furthermore, the reception unit can automatically select a related reception method based on the content of the expert's past consultation. In this way, the optimal reception method can be provided to the expert by analyzing the past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the expert's past consultation history and select the optimal reception method.

[0035] When receiving consultation content, the reception unit can filter the consultation content based on the expert's current project or area of ​​interest. For example, the reception unit can preferentially receive consultation content related to the project the expert is currently working on. The reception unit can also filter and receive related consultation content based on the expert's area of ​​interest. Furthermore, the reception unit can select and receive appropriate consultation content based on the expert's current work content. In this way, by filtering based on the expert's current project or area of ​​interest, it is possible to preferentially receive highly relevant consultation content. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can filter related consultation content by using a generation AI to analyze the expert's current project or area of ​​interest.

[0036] When receiving consultation content, the reception unit can prioritize receiving consultation content that is highly relevant by taking into account the geographical location information of the expert. For example, the reception unit can prioritize receiving consultation content related to the expert's current location. The reception unit can also prioritize receiving consultation content that is specific to a region based on the geographical location information of the expert. Furthermore, when the expert is traveling, the reception unit can also prioritize receiving consultation content that is close to the expert's current location. In this way, by taking into account the geographical location information of the expert, it is possible to prioritize receiving consultation content that is specific to a region. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can use the generation AI to analyze the geographical location information of the expert and prioritize receiving related consultation content.

[0037] The reception unit can analyze the social media activity of the expert when receiving the consultation content and receive related consultation content. For example, the reception unit can prioritize reception of consultation content related to topics mentioned by the expert on social media. The reception unit can also prioritize reception of consultation content related to areas of high interest from the expert's social media activity. Furthermore, the reception unit can prioritize reception of consultation content related to accounts followed by the expert on social media. In this way, by analyzing the expert's social media activity, consultation content related to areas of high interest can be prioritized. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the expert's social media activity and receive related consultation content.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. 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. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the urgency of the consultation content. In this way, by adjusting the level of detail of the analysis based on the importance of the consultation content, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation AI can evaluate the importance of the consultation content, and the analysis unit can adjust the level of detail of the analysis based on the importance.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit can apply a medical-specialized analysis algorithm to medical-related consultation content. The analysis unit can also apply a legal-specialized analysis algorithm to legal-related consultation content. The analysis unit can also apply a technical-specialized analysis algorithm to technical-related consultation content. In this way, by applying different analysis algorithms depending on the category of the consultation content, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze the category of the consultation content and apply an appropriate analysis algorithm depending on the category.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the consultation content was submitted. For example, if the consultation content is urgent, the analysis unit performs analysis with priority. The analysis unit can also perform analysis in the order in which the consultation content was submitted. Furthermore, the analysis unit can adjust the priority of analysis based on the time when the consultation content was submitted. In this way, by determining the priority of analysis based on the time when the consultation content was submitted, consultation content with high urgency can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze the time when the consultation content was submitted and determine the priority of analysis based on the submission time.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. For example, if the consultation contents are highly relevant, the analysis unit performs the analysis preferentially. Also, if the consultation contents are less relevant, the analysis unit can postpone the analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. In this way, by adjusting the order of analysis based on the relevance of the consultation contents, it is possible to prioritize the analysis of highly relevant consultation contents. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relevance by using a generation AI to analyze the relevance of the consultation contents.

[0042] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for information with a high level of importance. The providing unit can also provide a concise explanation for information with a low level of importance. Furthermore, the providing unit can adjust the level of detail of the information provided according to the urgency of the information. In this way, by adjusting the level of detail of the information provided based on the importance of the information, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can have the generation AI evaluate the importance of the information and adjust the level of detail of the information provided based on the importance.

[0043] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply a medical-specialized providing algorithm to medical-related information. The providing unit can also apply a legal-specialized providing algorithm to legal-related information. The providing unit can also apply a technical-specialized providing algorithm to technical-related information. This allows more appropriate information to be provided by applying an appropriate providing algorithm depending on the category of information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can use a generation AI to analyze the category of information and apply an appropriate providing algorithm depending on the category.

[0044] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, if the information is urgent, the providing unit provides it preferentially. The providing unit can also provide the information in the order in which it was submitted. Furthermore, the providing unit can adjust the priority of provision based on the time of submission of information. In this way, by determining the priority of provision based on the time of submission of information, it is possible to provide information with higher urgency preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can determine the priority of provision based on the time of submission of information using a generation AI that analyzes the time of submission of information.

[0045] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, if the information is highly relevant, the providing unit provides it preferentially. Also, if the information is less relevant, the providing unit can provide it later. Furthermore, the providing unit can adjust the order of provision based on the relevance of the information. In this way, by adjusting the order of provision based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can adjust the order of provision based on the relevance by having the generation AI analyze the relevance of the information.

[0046] When constructing a database, the database construction unit can select the optimal update method by referring to past database update history. The database construction unit, for example, selects the most effective update method from the past update history. The database construction unit can also analyze the past update history and optimize the update frequency. Furthermore, the database construction unit can also optimize the update content based on the past update history. In this way, the optimal update method can be selected by referring to the past update history. Some or all of the above-mentioned processing in the database construction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the database construction unit can select the optimal update method by having the generation AI analyze the past database update history.

[0047] When constructing the database, the database construction unit can prioritize updating highly relevant data by taking into account the geographical location information of the expert. For example, the database construction unit prioritizes updating data related to the area where the expert is currently located. The database construction unit can also prioritize updating area-specific data based on the geographical location information of the expert. Furthermore, if the expert is traveling, the database construction unit can also prioritize updating data related to the expert's current location. In this way, area-specific data can be prioritized by taking into account the geographical location information of the expert. Some or all of the above-described processing in the database construction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the database construction unit can use a generation AI to analyze the geographical location information of the expert and prioritize updating related data.

[0048] During verification, the verification unit can select the optimal verification method by referring to past verification history. The verification unit, for example, selects the most effective verification method from past verification history. The verification unit can also analyze past verification history and optimize verification frequency. Furthermore, the verification unit can also optimize verification content based on past verification history. In this way, the optimal verification method can be selected by referring to past verification history. Some or all of the above-mentioned processing in the verification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the verification unit can select the optimal verification method by having the generation AI analyze past verification history.

[0049] During verification, the verification unit can prioritize verification of highly relevant information by taking into account the geographical location information of the expert. For example, the verification unit prioritizes verification of information related to the area where the expert is currently located. The verification unit can also prioritize verification of area-specific information based on the geographical location information of the expert. Furthermore, if the expert is moving, the verification unit can also prioritize verification of information related to the expert's current location. In this way, by taking into account the geographical location information of the expert, area-specific information can be prioritized for verification. Some or all of the above-described processing in the verification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the verification unit can use a generation AI to analyze the geographical location information of the expert and prioritize verification of relevant information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the expert's past consultation content and automatically suggest similar consultation content. For example, if a doctor has previously consulted about a specific case, the reception unit can suggest new research or treatments related to that case. Similarly, if a lawyer has previously consulted about a specific legal issue, the reception unit can provide information on new precedents and legal changes related to that issue. Furthermore, the reception unit can prioritize and suggest consultation content that the expert has frequently used in the past. This allows the expert to quickly obtain more appropriate information based on the content of past consultations.

[0052] The database construction unit can collect expert feedback and improve the contents of the database based on that feedback. For example, if a doctor provides feedback on provided information, the database construction unit can improve the accuracy of the information based on that feedback. Also, if a lawyer provides feedback on provided legal information, the database construction unit can update the legal database based on that feedback. Furthermore, the database construction unit can analyze expert feedback and optimize the database structure and search algorithm. This makes it possible to utilize expert feedback to provide more accurate information.

[0053] The verification unit can cross-check multiple sources of information when verifying the reliability of the provided information. For example, in the case of medical information, the accuracy of the information can be confirmed by referring to multiple medical databases and academic papers. In the case of legal information, the reliability of the information can be evaluated by referring to multiple legal databases and case law collections. Furthermore, the verification unit can regularly update the sources of information to ensure that the provided information is up-to-date. This increases the reliability of the provided information and allows experts to use it with confidence.

[0054] The analysis unit can learn from the content of past consultations with experts and provide personalized analysis results. For example, if a doctor has previously consulted about a specific case, the analysis unit can prioritize providing information related to that case. Similarly, if a lawyer has previously consulted about a specific legal issue, the analysis unit can prioritize providing information related to that issue. Furthermore, the analysis unit can automatically suggest new relevant information based on the content of the expert's past consultations. This allows experts to quickly obtain more appropriate information based on the content of their past consultations.

[0055] The provision unit can analyze the expert's past usage history and select the optimal information provision method. For example, if a doctor has preferred to receive information in a specific format in the past, the provision unit can provide information in that format. Also, if a lawyer has trusted and used a specific information source in the past, the provision unit can preferentially provide information from that source. Furthermore, the provision unit can automatically suggest new related information based on the expert's past usage history. This allows the expert to quickly obtain more appropriate information based on their past usage history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit accepts consultation requests from experts. Consultation requests from experts include technical and business-related inquiries. The reception unit provides an interface for experts to input their consultation requests to the generation AI, collects the necessary information, and saves it in an appropriate format. For example, the reception unit saves the consultation requests entered by the experts in text format and sends it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the consultation content received by the reception unit. The analysis is performed using methods such as text analysis and data mining. For example, the analysis unit searches for knowledge related to the input information based on a vast database of specialized knowledge that the generation AI has previously learned, and provides appropriate information. The analysis unit also uses the generation AI to extract the main points of the input consultation content and organize related information. Step 3: The provision unit provides relevant expertise based on the information analyzed by the analysis unit. The provided expertise includes medical knowledge, legal knowledge, and so on. For example, the provision unit provides reference information for experts to supplement their own knowledge based on the information analyzed by the generation AI. The provision unit also uses the generation AI to verify the reliability of the provided information and provide reliable information to experts.

[0058] (Example 2) A generative AI service according to an embodiment of the present invention is a system that allows experts to consult on specialized knowledge. In this system, experts input their consultation details into a generative AI, which then analyzes the consultation details and provides relevant specialized knowledge. For example, when a doctor consults on a diagnostic method for a specific case, the doctor inputs the details of the case into the generative AI. The generative AI then analyzes the input information and provides relevant specialized knowledge. The generative AI then analyzes the input consultation details. The generative AI then searches for knowledge related to the input information based on a vast database of previously trained specialized knowledge and provides appropriate information. For example, when a doctor consults on a diagnostic method for a specific case, the generative AI provides diagnostic methods and treatments related to that case. Furthermore, the information provided by the generative AI can be used as reference information for experts to supplement their own knowledge. For example, when a lawyer consults on the interpretation of a specific legal issue, the generative AI provides relevant laws and precedents, providing reference information for the lawyer to make a more appropriate decision. This system allows experts to supplement their own knowledge and make more appropriate decisions. For example, a doctor can use the generative AI to confirm a diagnostic method for a specific case, leading to a more accurate diagnosis. In addition, lawyers can use generative AI to confirm interpretations of specific legal issues, allowing them to provide more appropriate legal advice. In this way, generative AI services that allow experts to consult on specialized knowledge are a mechanism that supplements the experts' own knowledge and provides reference information for making more appropriate decisions. In this way, generative AI services can supplement the experts' specialized knowledge and allow them to make more appropriate decisions.

[0059] A generation AI service according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content from an expert. The consultation content from the expert includes, but is not limited to, technical consultations, business consultations, and the like. The reception unit provides, for example, an interface through which the expert inputs the consultation content to the generation AI. The reception unit can also collect necessary information when the expert inputs the consultation content and save it in an appropriate format. For example, the reception unit saves the consultation content entered by the expert in text format and transmits it to the analysis unit. The analysis unit uses the generation AI to analyze the consultation content received by the reception unit. The analysis can be performed using, for example, text analysis or data mining, but is not limited to, these examples. For example, the analysis unit searches for knowledge related to the input information based on a vast database of specialized knowledge that the generation AI has previously learned, and provides appropriate information. The analysis unit can also use the generation AI to extract key points from the input consultation content and organize related information. For example, when the generation AI receives a prompt such as, "Please provide information related to this consultation content," the analysis unit searches for related information and creates a summary. The providing unit provides related specialized knowledge based on the information analyzed by the analyzing unit. Examples of the specialized knowledge provided include, but are not limited to, medical knowledge and legal knowledge. For example, the providing unit provides reference information for experts to supplement their own knowledge based on the information analyzed by the generating AI. The providing unit can also verify the reliability of the provided information using the generating AI. For example, the providing unit evaluates the reliability of the information provided by the generating AI and provides reliable information to the experts. This allows the generating AI service according to the embodiment to supplement the experts' specialized knowledge and make more appropriate decisions. Some or all of the above-described processing by the providing unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the providing unit can provide reliable information to experts based on the information analyzed by the generating AI.

[0060] The generative AI service includes a database construction unit that constructs and updates a specialized knowledge database. The database construction unit constructs and updates the specialized knowledge database. Examples of specialized knowledge databases include, but are not limited to, medical knowledge, legal knowledge, and technical knowledge. The database construction unit can automatically construct and update the specialized knowledge database using, for example, a generative AI. For example, the database construction unit allows the generative AI to collect public data and academic papers on the Internet and add them to the specialized knowledge database. The database construction unit can also update the specialized knowledge database based on information provided by experts. For example, the database construction unit adds the latest research results and practical experience provided by experts to the database. This improves the accuracy of the information provided by building and updating the specialized knowledge database. Some or all of the above-described processing in the database construction unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the database construction unit can automatically update the specialized knowledge database based on data collected by the generative AI.

[0061] The generation AI service includes a verification unit that verifies the reliability of the provided information. The verification unit verifies the reliability of the provided information. Verification of reliability includes, but is not limited to, the reliability of the information source and the accuracy of the data. The verification unit can automatically evaluate the reliability of the provided information, for example, using the generation AI. For example, the verification unit causes the generation AI to evaluate the reliability of the information source and select highly reliable information. The verification unit can also verify the accuracy of the provided information. For example, the verification unit compares the information provided by the generation AI with other highly reliable information and evaluates the accuracy. This verifies the reliability of the provided information, allowing experts to use the information with confidence. Some or all of the above-described processing in the verification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the verification unit causes the generation AI to evaluate the reliability of the information source and select highly reliable information.

[0062] The analysis unit can search for knowledge related to the input information based on multiple specialized knowledge databases previously trained by the generation AI and provide appropriate information. The analysis unit can search for knowledge related to the input information based on multiple specialized knowledge databases previously trained by the generation AI and provide appropriate information. Examples of multiple specialized knowledge databases include, but are not limited to, medical databases, legal databases, and technical databases. For example, the analysis unit can analyze the input consultation content and search for related specialized knowledge. The analysis unit can also use the generation AI to extract key points from the input information and organize related information. For example, when the generation AI receives a prompt such as "Please provide information related to this consultation content," the analysis unit can search for related information and create a summary. This allows relevant knowledge to be quickly provided by analyzing the input information based on the pre-trained databases. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can search for knowledge related to the input information based on the database previously trained by the generation AI and provide appropriate information.

[0063] The providing unit can provide reference information for experts to supplement their knowledge. For example, the providing unit provides reference information for experts to supplement their knowledge based on information analyzed by the generation AI. Examples of reference information include, but are not limited to, literature information and past cases. For example, the providing unit provides reference information for experts to supplement their knowledge based on information provided by the generation AI. The providing unit can also verify the reliability of the provided information using the generation AI. For example, the providing unit evaluates the reliability of the information provided by the generation AI and provides reliable information to the expert. This allows the expert to make more appropriate decisions by providing reference information to supplement their knowledge. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can provide reliable information to the expert based on information analyzed by the generation AI.

[0064] The reception unit can estimate the expert's emotions and adjust the timing of accepting the consultation content based on the estimated expert's emotions. For example, if the expert is feeling stressed, the reception unit can delay the timing of accepting the consultation content to provide the expert with time to relax. Furthermore, if the expert is relaxed, the reception unit can immediately accept the consultation content and respond quickly. Furthermore, if the expert is in a hurry, the reception unit can prioritize accepting the consultation content and start processing quickly. By adjusting the timing of accepting the consultation content according to the expert's emotions, the consultation content can be accepted at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can estimate the expert's emotions using the generation AI and adjust the timing of accepting the consultation content based on the estimated emotions.

[0065] The reception unit can analyze the expert's past consultation history and select an appropriate reception method. For example, the reception unit preferentially suggests reception methods that the expert has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the expert's past consultation history. Furthermore, the reception unit can automatically select a related reception method based on the content of the expert's past consultation. In this way, the optimal reception method can be provided to the expert by analyzing the past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the expert's past consultation history and select the optimal reception method.

[0066] When receiving consultation content, the reception unit can filter the consultation content based on the expert's current project or area of ​​interest. For example, the reception unit can preferentially receive consultation content related to the project the expert is currently working on. The reception unit can also filter and receive related consultation content based on the expert's area of ​​interest. Furthermore, the reception unit can select and receive appropriate consultation content based on the expert's current work content. In this way, by filtering based on the expert's current project or area of ​​interest, it is possible to preferentially receive highly relevant consultation content. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can filter related consultation content by using a generation AI to analyze the expert's current project or area of ​​interest.

[0067] The reception unit can estimate the expert's emotions and determine the priority of the consultation contents to be received based on the estimated expert's emotions. For example, if the expert is feeling stressed, the reception unit can postpone consultation contents of lower importance. Furthermore, if the expert is relaxed, the reception unit can prioritize consultation contents of higher importance. Furthermore, if the expert is in a hurry, the reception unit can prioritize consultation contents of higher urgency. In this way, by determining the priority of consultation contents according to the expert's emotions, the consultation contents can be processed in a more appropriate order. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can use the generation AI to estimate the expert's emotions and determine the priority of the consultation contents based on the estimated emotions.

[0068] When receiving consultation content, the reception unit can prioritize receiving consultation content that is highly relevant by taking into account the geographical location information of the expert. For example, the reception unit can prioritize receiving consultation content related to the expert's current location. The reception unit can also prioritize receiving consultation content that is specific to a region based on the geographical location information of the expert. Furthermore, when the expert is traveling, the reception unit can also prioritize receiving consultation content that is close to the expert's current location. In this way, by taking into account the geographical location information of the expert, it is possible to prioritize receiving consultation content that is specific to a region. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can use the generation AI to analyze the geographical location information of the expert and prioritize receiving related consultation content.

[0069] The reception unit can analyze the social media activity of the expert when receiving the consultation content and receive related consultation content. For example, the reception unit can prioritize reception of consultation content related to topics mentioned by the expert on social media. The reception unit can also prioritize reception of consultation content related to areas of high interest from the expert's social media activity. Furthermore, the reception unit can prioritize reception of consultation content related to accounts followed by the expert on social media. In this way, by analyzing the expert's social media activity, consultation content related to areas of high interest can be prioritized. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the expert's social media activity and receive related consultation content.

[0070] The analysis unit can estimate the expert's emotions and adjust the presentation method of the analysis based on the estimated expert's emotions. For example, if the expert is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the expert is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the expert is stressed, the analysis unit can provide analysis results using visually easy-to-understand graphs or diagrams. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis based on the expert's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can estimate the expert's emotions using the generation AI and adjust the presentation method of the analysis based on the estimated emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. 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. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the urgency of the consultation content. In this way, by adjusting the level of detail of the analysis based on the importance of the consultation content, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation AI can evaluate the importance of the consultation content, and the analysis unit can adjust the level of detail of the analysis based on the importance.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit can apply a medical-specialized analysis algorithm to medical-related consultation content. The analysis unit can also apply a legal-specialized analysis algorithm to legal-related consultation content. The analysis unit can also apply a technical-specialized analysis algorithm to technical-related consultation content. In this way, by applying different analysis algorithms depending on the category of the consultation content, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze the category of the consultation content and apply an appropriate analysis algorithm depending on the category.

[0073] The analysis unit can estimate the expert's emotions and adjust the length of the analysis based on the estimated expert's emotions. For example, if the expert is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the expert is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the expert is stressed, the analysis unit can provide a short, visually easy-to-understand analysis result. By adjusting the length of the analysis according to the expert's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can estimate the expert's emotions using the generation AI and adjust the length of the analysis based on the estimated emotions.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the consultation content was submitted. For example, if the consultation content is urgent, the analysis unit performs analysis with priority. The analysis unit can also perform analysis in the order in which the consultation content was submitted. Furthermore, the analysis unit can adjust the priority of analysis based on the time when the consultation content was submitted. In this way, by determining the priority of analysis based on the time when the consultation content was submitted, consultation content with high urgency can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze the time when the consultation content was submitted and determine the priority of analysis based on the submission time.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. For example, if the consultation contents are highly relevant, the analysis unit performs the analysis preferentially. Also, if the consultation contents are less relevant, the analysis unit can postpone the analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. In this way, by adjusting the order of analysis based on the relevance of the consultation contents, it is possible to prioritize the analysis of highly relevant consultation contents. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relevance by using a generation AI to analyze the relevance of the consultation contents.

[0076] The providing unit can estimate the expert's emotions and adjust the presentation method of the information to be provided based on the estimated expert's emotions. For example, if the expert is relaxed, the providing unit can provide detailed information. Furthermore, if the expert is in a hurry, the providing unit can provide concise information that focuses on the main points. Furthermore, if the expert is stressed, the providing unit can provide information using visually easy-to-understand graphs and diagrams. This allows for adjusting the presentation method of information according to the expert's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can estimate the expert's emotions using the generation AI and adjust the presentation method of information based on the estimated emotions.

[0077] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for information with a high level of importance. The providing unit can also provide a concise explanation for information with a low level of importance. Furthermore, the providing unit can adjust the level of detail of the information provided according to the urgency of the information. In this way, by adjusting the level of detail of the information provided based on the importance of the information, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can have the generation AI evaluate the importance of the information and adjust the level of detail of the information provided based on the importance.

[0078] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply a medical-specialized providing algorithm to medical-related information. The providing unit can also apply a legal-specialized providing algorithm to legal-related information. The providing unit can also apply a technical-specialized providing algorithm to technical-related information. This allows more appropriate information to be provided by applying an appropriate providing algorithm depending on the category of information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can use a generation AI to analyze the category of information and apply an appropriate providing algorithm depending on the category.

[0079] The providing unit can estimate the expert's emotions and adjust the length of the information to be provided based on the estimated expert's emotions. For example, if the expert is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, if the expert is relaxed, the providing unit can also provide detailed information. Furthermore, if the expert is stressed, the providing unit can also provide short, visually easy-to-understand information. By adjusting the length of the information according to the expert's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can estimate the expert's emotions using the generation AI and adjust the length of the information based on the estimated emotions.

[0080] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, if the information is urgent, the providing unit provides it preferentially. The providing unit can also provide the information in the order in which it was submitted. Furthermore, the providing unit can adjust the priority of provision based on the time of submission of information. In this way, by determining the priority of provision based on the time of submission of information, it is possible to provide information with higher urgency preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can determine the priority of provision based on the time of submission of information using a generation AI that analyzes the time of submission of information.

[0081] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, if the information is highly relevant, the providing unit provides it preferentially. Also, if the information is less relevant, the providing unit can provide it later. Furthermore, the providing unit can adjust the order of provision based on the relevance of the information. In this way, by adjusting the order of provision based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can adjust the order of provision based on the relevance by having the generation AI analyze the relevance of the information.

[0082] The database construction unit can estimate the expert's emotions and adjust the database update frequency based on the estimated expert's emotions. For example, if the expert is stressed, the database construction unit can reduce the update frequency to reduce the burden on the expert. Furthermore, if the expert is relaxed, the database construction unit can increase the update frequency to provide the latest information. Furthermore, if the expert is in a hurry, the database construction unit can prioritize updating only important information. This allows the database update frequency to be adjusted according to the expert's emotions, enabling the database to be updated at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the database construction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the database construction unit can estimate the expert's emotions using the generation AI and adjust the database update frequency based on the estimated emotions.

[0083] When constructing a database, the database construction unit can select the optimal update method by referring to past database update history. The database construction unit, for example, selects the most effective update method from the past update history. The database construction unit can also analyze the past update history and optimize the update frequency. Furthermore, the database construction unit can also optimize the update content based on the past update history. In this way, the optimal update method can be selected by referring to the past update history. Some or all of the above-mentioned processing in the database construction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the database construction unit can select the optimal update method by having the generation AI analyze the past database update history.

[0084] The database construction unit can estimate the expert's emotions and adjust the database update content based on the estimated expert's emotions. For example, if the expert is stressed, the database construction unit can postpone updating less important information. Furthermore, if the expert is relaxed, the database construction unit can also prioritize updating detailed information. Furthermore, if the expert is in a hurry, the database construction unit can also prioritize updating only important information. This allows for adjusting the database update content according to the expert's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the database construction unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the database construction unit can estimate the expert's emotions using the generation AI and adjust the database update content based on the estimated emotions.

[0085] When constructing the database, the database construction unit can prioritize updating highly relevant data by taking into account the geographical location information of the expert. For example, the database construction unit prioritizes updating data related to the area where the expert is currently located. The database construction unit can also prioritize updating area-specific data based on the geographical location information of the expert. Furthermore, if the expert is traveling, the database construction unit can also prioritize updating data related to the expert's current location. In this way, area-specific data can be prioritized by taking into account the geographical location information of the expert. Some or all of the above-described processing in the database construction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the database construction unit can use a generation AI to analyze the geographical location information of the expert and prioritize updating related data.

[0086] The verification unit can estimate the expert's emotions and adjust the method for verifying the reliability of the provided information based on the estimated expert's emotions. For example, if the expert is relaxed, the verification unit can perform detailed verification. Furthermore, if the expert is in a hurry, the verification unit can also perform a concise verification that focuses on the main points. Furthermore, if the expert is stressed, the verification unit can provide visually easy-to-understand verification results. This allows for more appropriate reliability verification by adjusting the verification method according to the expert's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the verification unit can be performed using, for example, the generation AI, or without the generation AI. For example, the verification unit can estimate the expert's emotions using the generation AI and adjust the verification method based on the estimated emotions.

[0087] During verification, the verification unit can select the optimal verification method by referring to past verification history. The verification unit, for example, selects the most effective verification method from past verification history. The verification unit can also analyze past verification history and optimize verification frequency. Furthermore, the verification unit can also optimize verification content based on past verification history. In this way, the optimal verification method can be selected by referring to past verification history. Some or all of the above-mentioned processing in the verification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the verification unit can select the optimal verification method by having the generation AI analyze past verification history.

[0088] The verification unit can estimate the emotion of the expert and determine the priority of verification based on the estimated emotion of the expert. For example, if the expert is stressed, the verification unit can postpone less important verification. Furthermore, if the expert is relaxed, the verification unit can prioritize more important verification. Furthermore, if the expert is in a hurry, the verification unit can prioritize more urgent verification. This allows verification to be performed in a more appropriate order by determining the priority of verification based on the emotion of the expert. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the verification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the verification unit can estimate the emotion of the expert using the generation AI and determine the priority of verification based on the estimated emotion.

[0089] During verification, the verification unit can prioritize verification of highly relevant information by taking into account the geographical location information of the expert. For example, the verification unit prioritizes verification of information related to the area where the expert is currently located. The verification unit can also prioritize verification of area-specific information based on the geographical location information of the expert. Furthermore, if the expert is moving, the verification unit can also prioritize verification of information related to the expert's current location. In this way, by taking into account the geographical location information of the expert, area-specific information can be prioritized for verification. Some or all of the above-described processing in the verification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the verification unit can use a generation AI to analyze the geographical location information of the expert and prioritize verification of relevant information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, database construction unit, and verification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for experts to input consultation details. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input consultation details. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related expertise based on the analyzed information. The database construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and builds and updates a specialized knowledge database. The verification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the provided information. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, database construction unit, and verification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for experts to input consultation details. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input consultation details. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related expertise based on the analyzed information. The database construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and builds and updates a specialized knowledge database. The verification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the provided information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, database construction unit, and verification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface through which the expert inputs the consultation content. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input consultation content. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related expertise based on the analyzed information. The database construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and constructs and updates a specialized knowledge database. The verification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the provided information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, database construction unit, and verification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for experts to input consultation details. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input consultation details. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related expertise based on the analyzed information. The database construction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and builds and updates a specialized knowledge database. The verification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the provided information.

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

[0091] The reception unit can analyze the expert's past consultation content and automatically suggest similar consultation content. For example, if a doctor has previously consulted about a specific case, the reception unit can suggest new research or treatments related to that case. Similarly, if a lawyer has previously consulted about a specific legal issue, the reception unit can provide information on new precedents and legal changes related to that issue. Furthermore, the reception unit can prioritize and suggest consultation content that the expert has frequently used in the past. This allows the expert to quickly obtain more appropriate information based on the content of past consultations.

[0092] The database construction unit can collect expert feedback and improve the contents of the database based on that feedback. For example, if a doctor provides feedback on provided information, the database construction unit can improve the accuracy of the information based on that feedback. Also, if a lawyer provides feedback on provided legal information, the database construction unit can update the legal database based on that feedback. Furthermore, the database construction unit can analyze expert feedback and optimize the database structure and search algorithm. This makes it possible to utilize expert feedback to provide more accurate information.

[0093] The verification unit can cross-check multiple sources of information when verifying the reliability of the provided information. For example, in the case of medical information, the accuracy of the information can be confirmed by referring to multiple medical databases and academic papers. In the case of legal information, the reliability of the information can be evaluated by referring to multiple legal databases and case law collections. Furthermore, the verification unit can regularly update the sources of information to ensure that the provided information is up-to-date. This increases the reliability of the provided information and allows experts to use it with confidence.

[0094] The analysis unit can learn from the content of past consultations with experts and provide personalized analysis results. For example, if a doctor has previously consulted about a specific case, the analysis unit can prioritize providing information related to that case. Similarly, if a lawyer has previously consulted about a specific legal issue, the analysis unit can prioritize providing information related to that issue. Furthermore, the analysis unit can automatically suggest new relevant information based on the content of the expert's past consultations. This allows experts to quickly obtain more appropriate information based on the content of their past consultations.

[0095] The provision unit can analyze the expert's past usage history and select the optimal information provision method. For example, if a doctor has preferred to receive information in a specific format in the past, the provision unit can provide information in that format. Also, if a lawyer has trusted and used a specific information source in the past, the provision unit can preferentially provide information from that source. Furthermore, the provision unit can automatically suggest new related information based on the expert's past usage history. This allows the expert to quickly obtain more appropriate information based on their past usage history.

[0096] The reception unit can estimate the emotion of the expert and adjust the method of receiving the consultation content based on the estimated emotion. For example, if the expert is feeling stressed, the reception unit can provide an interface that allows the expert to relax. Also, if the expert is relaxed, the reception unit can quickly receive the consultation content. Furthermore, if the expert is in a hurry, the reception unit can provide a simple interface to quickly receive the consultation content. In this way, by adjusting the reception method according to the expert's emotion, the consultation content can be received at a more appropriate time.

[0097] The analysis unit can estimate the expert's emotions and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the expert is relaxed, detailed analysis results can be provided. If the expert is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the expert is stressed, analysis results can be provided using graphs and diagrams that are easy to understand visually. In this way, by adjusting the way in which the analysis results are presented according to the expert's emotions, more appropriate analysis results can be provided.

[0098] The providing unit can estimate the emotion of the expert and adjust the level of detail of the information to be provided based on the estimated emotion. For example, if the expert is relaxed, detailed information can be provided. If the expert is in a hurry, concise information that focuses on the main points can be provided. Furthermore, if the expert is stressed, short information that is easy to understand visually can be provided. In this way, by adjusting the level of detail of the information according to the emotion of the expert, more appropriate information can be provided.

[0099] The database construction unit can estimate the emotions of the expert and adjust the timing of updating the database based on the estimated emotions. For example, if the expert is feeling stressed, the update timing can be delayed to reduce the burden on the expert. Also, if the expert is relaxed, the update timing can be accelerated to provide the latest information. Furthermore, if the expert is in a hurry, only important information can be updated preferentially. In this way, by adjusting the timing of updating the database according to the expert's emotions, the database can be updated at more appropriate times.

[0100] The verification unit can estimate the emotion of the expert and adjust the method for verifying the reliability of the provided information based on the estimated emotion. For example, if the expert is relaxed, a detailed verification can be performed. If the expert is in a hurry, a concise verification that focuses on the main points can be performed. Furthermore, if the expert is stressed, a visually easy-to-understand verification result can be provided. This allows for more appropriate reliability verification by adjusting the verification method according to the expert's emotion.

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

[0102] Step 1: The reception unit accepts consultation requests from experts. Consultation requests from experts include technical and business-related inquiries. The reception unit provides an interface for experts to input their consultation requests to the generation AI, collects the necessary information, and saves it in an appropriate format. For example, the reception unit saves the consultation requests entered by the experts in text format and sends it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the consultation content received by the reception unit. The analysis is performed using methods such as text analysis and data mining. For example, the analysis unit searches for knowledge related to the input information based on a vast database of specialized knowledge that the generation AI has previously learned, and provides appropriate information. The analysis unit also uses the generation AI to extract the main points of the input consultation content and organize related information. Step 3: The provision unit provides relevant expertise based on the information analyzed by the analysis unit. The provided expertise includes medical knowledge, legal knowledge, and so on. For example, the provision unit provides reference information for experts to supplement their own knowledge based on the information analyzed by the generation AI. The provision unit also uses the generation AI to verify the reliability of the provided information and provide reliable information to experts.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0140] 7, 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

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

Claims

1. A reception department that accepts consultations from experts, an analysis unit that analyzes the consultation content received by the reception unit; a providing unit that provides related expert knowledge based on the information analyzed by the analyzing unit; Equipped with A system characterized by:

2. Equipped with a database construction department that builds and updates a database of specialized knowledge 2. The system of claim 1.

3. Equipped with a verification unit that verifies the reliability of the provided information 2. The system of claim 1.

4. The analysis unit Based on multiple databases of pre-trained expertise, the system searches for knowledge related to the input information and provides appropriate information 2. The system of claim 1.

5. The providing unit Providing reference material for experts to supplement their own knowledge 2. The system of claim 1.

6. The reception unit Estimate the emotions of experts and adjust the timing of accepting consultations based on the estimated emotions of experts 2. The system of claim 1.

7. The reception unit Analyze the specialist's past consultation history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit Filtering enquiries based on current projects and areas of interest of experts 2. The system of claim 1.

9. The reception unit Estimate the emotions of experts and prioritize the consultations to be accepted based on the estimated emotions of experts.

2. The system of claim 1.

10. The reception unit When accepting consultation requests, the geographic location information of the expert is taken into consideration to prioritize the most relevant consultation requests.

2. The system of claim 1.

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