system

Abstract

The system according to the embodiment aims to provide prompt and appropriate advice to users who are unsure where to turn for help with their concerns. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives consultation content from a user. The analysis unit analyzes the content received by the reception unit. The generation unit generates advice based on the specialty field identified by the analysis unit. The provision unit provides the advice generated by the generation unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide appropriate advice quickly when users have concerns and do not know where to turn for help.

[0005] The system according to the embodiment aims to provide prompt and appropriate advice to users who are unsure where to turn for help with their concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives consultation content from a user. The analysis unit analyzes the content received by the reception unit. The generation unit generates advice based on the specialty field identified by the analysis unit. The provision unit provides the advice generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate advice to users who are unsure where to turn for help with their concerns. [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) The expert advice providing system according to an embodiment of the present invention utilizes a generation AI to provide real-time expert advice to users. This system operates 24 hours a day and covers multiple fields, including medical, legal, and financial. When a user has concerns about something and doesn't know where to turn for advice, the generation AI provides appropriate advice, helping the user resolve their concerns and issues. For example, a user inputs a question, such as, "I'm concerned about my recent health condition." This input is sent to the generation AI, which analyzes the input and identifies an appropriate field of expertise. The generation AI then generates advice in real time based on the identified field of expertise. The generated advice is provided to the user in real time, allowing the user to take specific action. This system allows users to receive expert advice 24 hours a day and supports the resolution of their concerns and issues. This allows the expert advice providing system to analyze the user's consultation content in real time and provide appropriate advice.

[0029] The expert advice providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a consultation request from a user. The consultation request from the user may include, but is not limited to, technical consultation, business consultation, personal consultation, etc. The reception unit may receive the consultation request input by the user in natural language, for example. The analysis unit analyzes the content received by the reception unit. The analysis unit may analyze keywords and context of the consultation request using, for example, text analysis, sentiment analysis, keyword extraction, or other methods, and identify a field of expertise. The generation unit generates advice based on the identified field of expertise. The generation unit generates advice in real time using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and provides expert advice according to the user's consultation request. The provision unit provides the user with the advice generated by the generation unit. The provision unit may provide the user with the generated advice in real time, for example. This allows the expert advice providing system according to the embodiment to analyze the user's consultation request in real time and provide appropriate advice.

[0030] The reception unit can receive the consultation content from the user in natural language. Natural languages ​​include, but are not limited to, Japanese, English, and other languages. The reception unit can receive the consultation content input by the user in natural language. For example, if the user inputs "I'm worried about my recent health condition," the reception unit can receive the content in natural language. The reception unit can also handle cases where the user inputs the consultation content using voice input. For example, if the user inputs "I'm worried about my recent health condition" by voice, the reception unit can receive the content in natural language. This allows the user to input the consultation content in natural language.

[0031] The analysis unit analyzes the content received by the reception unit and can identify the specialty field based on keywords and context. The analysis unit analyzes the content received by the reception unit using, for example, text analysis. For example, the analysis unit can extract keywords from the consultation content input by the user and identify the specialty field based on the keywords. The analysis unit can also analyze the emotions of the consultation content input by the user using emotion analysis. For example, the analysis unit can analyze the emotions of the consultation content input by the user and identify the specialty field based on the emotions. The analysis unit can also extract keywords from the consultation content input by the user using keyword extraction and identify the specialty field based on the keywords. For example, the analysis unit can extract keywords from the consultation content input by the user and identify the specialty field based on the keywords. In this way, the specialty field can be identified by analyzing the keywords and context of the consultation content.

[0032] The generation unit can generate advice in real time based on the identified specialty field. The generation unit, for example, uses a generation AI to generate advice in real time based on the identified specialty field. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and provides specialized advice according to the user's consultation content. For example, if a user inputs, "I'm worried about my recent health condition," the generation unit can use the generation AI to generate specialized health advice in real time. Furthermore, if a user inputs, "I would like to consult about legal matters," the generation unit can also use the generation AI to generate specialized legal advice in real time. Furthermore, if a user inputs, "I would like to consult about financial matters," the generation unit can also use the generation AI to generate specialized financial advice in real time. This allows specialized advice to be generated in real time.

[0033] The providing unit can provide the generated advice to the user in real time. For example, the providing unit provides the advice generated by the generating unit to the user in real time. For example, the providing unit can provide the generated advice to the user in text format. The providing unit can also provide the generated advice to the user in audio format. Furthermore, the providing unit can also provide the generated advice to the user in video format. For example, the providing unit can provide the generated advice to the user in text format. The providing unit can also provide the generated advice to the user in audio format. Furthermore, the providing unit can also provide the generated advice to the user in video format. This makes it possible to provide the generated advice to the user in real time.

[0034] The reception unit can select an appropriate reception method by referring to the user's past consultation history when receiving a call. The reception unit, for example, can select an appropriate reception method by referring to the user's past consultation history when receiving a call. For example, the reception unit can automatically display as candidates the contents of consultations that the user has frequently made in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the contents of consultations to be made in a specific time period based on the user's past consultation history. This makes it possible to select the optimal reception method based on the user's past consultation history.

[0035] The reception unit can perform filtering based on the user's current situation and areas of interest at the time of reception. The reception unit performs filtering based on the user's current situation and areas of interest at the time of reception, for example. For example, if the user inputs a consultation about their current health condition, the reception unit can preferentially display options in the related medical field. Furthermore, if the user inputs a consultation about law, the reception unit can preferentially display options in the related legal field. Furthermore, if the user inputs a consultation about finance, the reception unit can preferentially display options in the related financial field. In this way, by filtering based on the user's current situation and areas of interest, it is possible to receive appropriate consultation content.

[0036] The reception unit can prioritize reception of highly relevant consultation content based on the user's geographical location information at the time of reception. For example, the reception unit prioritizes reception of highly relevant consultation content based on the user's geographical location information at the time of reception. For example, when the user is in a specific area, the reception unit can prioritize reception of consultation content related to that area. Furthermore, when the user is traveling, the reception unit can prioritize reception of consultation content related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize reception of consultation content related to the area around the user's home. This makes it possible to prioritize reception of highly relevant consultation content based on the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity at the time of reception and receive related consultation content. The reception unit, for example, analyzes the user's social media activity at the time of reception and receives related consultation content. For example, if the user posts about health on social media, the reception unit can prioritize receiving related consultation content. Furthermore, if the user posts about law on social media, the reception unit can prioritize receiving related consultation content. Furthermore, if the user posts about finance on social media, the reception unit can prioritize receiving related consultation content. In this way, related consultation content can be received based on the user's social media activity.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit can perform a detailed analysis for consultation content with high importance. Furthermore, the analysis unit can perform a concise analysis for consultation content with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for consultation content with medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the consultation content.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, for consultation content related to medical care, the analysis unit can apply an analysis algorithm specialized for medical care. For consultation content related to legal care, the analysis unit can apply an analysis algorithm specialized for legal care. For consultation content related to financial care, the analysis unit can apply an analysis algorithm specialized for financial care. This makes it possible to apply an appropriate analysis algorithm depending on the category of the consultation content.

[0040] The analysis unit can adjust the order of analysis based on the time of submission of the consultation content during analysis. For example, the analysis unit can adjust the order of analysis based on the time of submission of the consultation content during analysis. For example, the analysis unit can prioritize the analysis of consultation content that has been submitted recently. Also, the analysis unit can postpone the analysis of consultation content that has been submitted recently. Furthermore, the analysis unit can appropriately analyze consultation content that has been submitted recently. This makes it possible to adjust the order of analysis based on the time of submission of the consultation content.

[0041] The analysis unit can improve the accuracy of the analysis by referring to literature related to the consultation content during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the consultation content during analysis, for example. For example, for consultation content related to medical care, the analysis unit can analyze by referring to the latest medical literature. For consultation content related to law, the analysis unit can analyze by referring to the latest legal literature. For consultation content related to finance, the analysis unit can analyze by referring to the latest financial literature. In this way, the accuracy of the analysis is improved by referring to related literature.

[0042] The generation unit can adjust the level of detail of the advice based on the importance of the specified specialty field at the time of generation. The generation unit, for example, adjusts the level of detail of the advice based on the importance of the specified specialty field at the time of generation. For example, for a specialty field with high importance, the generation unit can generate detailed advice. Furthermore, for a specialty field with low importance, the generation unit can generate concise advice. Furthermore, for a specialty field with medium importance, the generation unit can generate advice with an appropriate level of detail. In this way, the level of detail of the advice can be adjusted based on the importance of the specialty field.

[0043] The generation unit can apply different generation algorithms depending on the identified specialty at the time of generation. For example, the generation unit can apply different generation algorithms depending on the identified specialty at the time of generation. For example, for medical advice, the generation unit can apply a medical-specialized generation algorithm. For legal advice, the generation unit can apply a legal-specialized generation algorithm. For financial advice, the generation unit can apply a financial-specialized generation algorithm. This allows an appropriate generation algorithm to be applied depending on the specialty.

[0044] The generation unit can determine the priority of advice based on the submission time of the specified specialty field at the time of generation. The generation unit, for example, determines the priority of advice based on the submission time of the specified specialty field at the time of generation. For example, advice in a specialty field that was submitted recently can be generated preferentially. Also, advice in a specialty field that was submitted recently can be postponed. Furthermore, advice in a specialty field that was submitted at a medium time can be generated appropriately. This makes it possible to determine the priority of advice based on the submission time of the specialty field.

[0045] The generation unit can improve the accuracy of the advice by referring to related literature in the specified specialty field when generating the advice. For example, the generation unit can improve the accuracy of the advice by referring to related literature in the specified specialty field when generating the advice. For example, for medical advice, the generation unit can generate the advice by referring to the latest medical literature. For legal advice, the generation unit can generate the advice by referring to the latest legal literature. For financial advice, the generation unit can generate the advice by referring to the latest financial literature. In this way, the accuracy of the advice is improved by referring to related literature.

[0046] The providing unit can select an appropriate providing method by referring to the user's past advice history when providing advice. For example, the providing unit can select an appropriate providing method by referring to the user's past advice history when providing advice. For example, it can provide preferentially a providing method (text, audio, etc.) that the user has used favorably in the past. It can also suggest providing advice in a specific format based on the user's past advice history. Furthermore, it can select the optimal providing method by referring to the content of advice the user has received in the past. This makes it possible to select the optimal providing method based on the user's past advice history.

[0047] The providing unit can customize the means for providing advice based on the user's current situation at the time of providing. The providing unit, for example, customizes the means for providing advice based on the user's current situation at the time of providing. For example, if the user is on the move, the providing unit can prioritize providing advice by voice. Also, if the user is in a desktop environment, the providing unit can provide advice in the form of detailed text. Furthermore, if the user is in a meeting, the providing unit can provide advice in the form of a brief notification. In this way, the means for providing advice can be customized based on the user's current situation.

[0048] The providing unit can provide appropriate advice based on the user's geographical location information at the time of providing. The providing unit, for example, provides appropriate advice based on the user's geographical location information at the time of providing. For example, when the user is in a specific area, the providing unit can preferentially provide advice related to that area. Furthermore, when the user is traveling, the providing unit can preferentially provide advice related to the travel destination. Furthermore, when the user is at home, the providing unit can preferentially provide advice related to the area around the user's home. This makes it possible to provide optimal advice based on the user's geographical location information.

[0049] The providing unit can analyze the user's social media activity at the time of providing advice and suggest a means of providing the advice. For example, the providing unit can analyze the user's social media activity at the time of providing advice and suggest a means of providing the advice. For example, if the user posts about health on social media, the providing unit can prioritize providing related advice. Also, if the user posts about law on social media, the providing unit can prioritize providing related advice. Furthermore, if the user posts about finance on social media, the providing unit can prioritize providing related advice. This makes it possible to suggest the optimal means of providing advice based on the user's social media activity.

[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 expert advice providing system may further include a history analysis unit that analyzes the user's past advice history. The history analysis unit analyzes the content of advice the user has received in the past and the user's subsequent actions, and generates reference information for providing more appropriate advice for the current consultation content. For example, if the user has received health advice in the past and the subsequent actions were successful, the history analysis unit can generate new advice based on that success story. Also, if the user has received legal advice in the past and the subsequent actions were unsuccessful, the history analysis unit can tailor new advice based on the failure story. Furthermore, if the user has received financial advice in the past and the subsequent actions resulted in a neutral outcome, the history analysis unit can provide new advice based on that neutral example. This allows for more accurate advice to be provided based on the user's past advice history.

[0052] The reception unit may include a health monitoring unit that monitors the user's current health condition. The health monitoring unit can acquire the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time and provide them to the reception unit. For example, if the user inputs, "I'm concerned about my recent health condition," the health monitoring unit can measure the user's heart rate and blood pressure and send the data to the reception unit. Also, if the user inputs, "I feel stressed," the health monitoring unit can measure the user's body temperature and heart rate variability and send the data to the reception unit. Furthermore, if the user inputs, "I get tired easily," the health monitoring unit can analyze the user's sleep patterns and send the data to the reception unit. This allows for more appropriate advice to be provided based on the user's current health condition.

[0053] The analysis unit may include a news gathering unit that gathers the latest news and trends related to the user's consultation. The news gathering unit can gather related information from online news sites and social networking sites and provide the information to the analysis unit. For example, if a user inputs, "I'm worried about my recent health condition," the news gathering unit can gather the latest health-related news and provide the information to the analysis unit. Also, if a user inputs, "I would like to seek legal advice," the news gathering unit can gather the latest legal-related news and provide the information to the analysis unit. Furthermore, if a user inputs, "I would like to seek financial advice," the news gathering unit can gather the latest financial-related news and provide the information to the analysis unit. This makes it possible to provide more appropriate advice based on the latest information related to the user's consultation.

[0054] The generation unit may include an expert opinion aggregation unit that aggregates opinions from multiple experts based on the user's consultation content. The expert opinion aggregation unit can collect opinions from experts in various fields, such as medicine, law, and finance, and provide them to the generation unit. For example, if a user inputs, "I'm worried about my recent health condition," the expert opinion aggregation unit can collect opinions from medical experts and provide the information to the generation unit. Also, if a user inputs, "I would like to consult about legal matters," the expert opinion aggregation unit can collect opinions from legal experts and provide the information to the generation unit. Furthermore, if a user inputs, "I would like to consult about financial matters," the expert opinion aggregation unit can collect opinions from financial experts and provide the information to the generation unit. This makes it possible to provide more accurate advice based on opinions from multiple experts.

[0055] The providing unit may include a customization providing unit that customizes the format of advice provision according to the content of the user's consultation. The customization providing unit can provide advice in a format such as text, audio, or video according to the content of the user's consultation and preferences. For example, if the user inputs "I'm worried about my recent health condition," the customization providing unit can provide health advice in text format. Also, if the user inputs "I would like to consult about legal matters," the customization providing unit can provide legal advice in audio format. Furthermore, if the user inputs "I would like to consult about financial matters," the customization providing unit can provide financial advice in video format. This makes it possible to provide advice in the optimal format according to the content of the user's consultation and preferences.

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

[0057] Step 1: The reception unit receives the consultation content from the user. The consultation content from the user may include technical consultation, business consultation, personal consultation, etc. The reception unit can receive the consultation content input by the user in natural language. Step 2: The analysis unit analyzes the content received by the reception unit. The analysis unit analyzes the keywords and context of the consultation content using methods such as text analysis, sentiment analysis, and keyword extraction, and identifies the area of ​​expertise. Step 3: The generator generates advice based on the identified areas of expertise. The generator generates advice in real time using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and provides specialized advice tailored to the user's inquiry. Step 4: The providing unit provides the advice generated by the generating unit to the user. The providing unit can provide the generated advice to the user in real time.

[0058] (Example 2) The expert advice providing system according to an embodiment of the present invention utilizes a generation AI to provide real-time expert advice to users. This system operates 24 hours a day and covers multiple fields, including medical, legal, and financial. When a user has concerns about something and doesn't know where to turn for advice, the generation AI provides appropriate advice, helping the user resolve their concerns and issues. For example, a user inputs a question, such as, "I'm concerned about my recent health condition." This input is sent to the generation AI, which analyzes the input and identifies an appropriate field of expertise. The generation AI then generates advice in real time based on the identified field of expertise. The generated advice is provided to the user in real time, allowing the user to take specific action. This system allows users to receive expert advice 24 hours a day and supports the resolution of their concerns and issues. This allows the expert advice providing system to analyze the user's consultation content in real time and provide appropriate advice.

[0059] The expert advice providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a consultation request from a user. The consultation request from the user may include, but is not limited to, technical consultation, business consultation, personal consultation, etc. The reception unit may receive the consultation request input by the user in natural language, for example. The analysis unit analyzes the content received by the reception unit. The analysis unit may analyze keywords and context of the consultation request using, for example, text analysis, sentiment analysis, keyword extraction, or other methods, and identify a field of expertise. The generation unit generates advice based on the identified field of expertise. The generation unit generates advice in real time using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and provides expert advice according to the user's consultation request. The provision unit provides the user with the advice generated by the generation unit. The provision unit may provide the user with the generated advice in real time, for example. This allows the expert advice providing system according to the embodiment to analyze the user's consultation request in real time and provide appropriate advice.

[0060] The reception unit can receive the consultation content from the user in natural language. Natural languages ​​include, but are not limited to, Japanese, English, and other languages. The reception unit can receive the consultation content input by the user in natural language. For example, if the user inputs "I'm worried about my recent health condition," the reception unit can receive the content in natural language. The reception unit can also handle cases where the user inputs the consultation content using voice input. For example, if the user inputs "I'm worried about my recent health condition" by voice, the reception unit can receive the content in natural language. This allows the user to input the consultation content in natural language.

[0061] The analysis unit analyzes the content received by the reception unit and can identify the specialty field based on keywords and context. The analysis unit analyzes the content received by the reception unit using, for example, text analysis. For example, the analysis unit can extract keywords from the consultation content input by the user and identify the specialty field based on the keywords. The analysis unit can also analyze the emotions of the consultation content input by the user using emotion analysis. For example, the analysis unit can analyze the emotions of the consultation content input by the user and identify the specialty field based on the emotions. The analysis unit can also extract keywords from the consultation content input by the user using keyword extraction and identify the specialty field based on the keywords. For example, the analysis unit can extract keywords from the consultation content input by the user and identify the specialty field based on the keywords. In this way, the specialty field can be identified by analyzing the keywords and context of the consultation content.

[0062] The generation unit can generate advice in real time based on the identified specialty field. The generation unit, for example, uses a generation AI to generate advice in real time based on the identified specialty field. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and provides specialized advice according to the user's consultation content. For example, if a user inputs, "I'm worried about my recent health condition," the generation unit can use the generation AI to generate specialized health advice in real time. Furthermore, if a user inputs, "I would like to consult about legal matters," the generation unit can also use the generation AI to generate specialized legal advice in real time. Furthermore, if a user inputs, "I would like to consult about financial matters," the generation unit can also use the generation AI to generate specialized financial advice in real time. This allows specialized advice to be generated in real time.

[0063] The providing unit can provide the generated advice to the user in real time. For example, the providing unit provides the advice generated by the generating unit to the user in real time. For example, the providing unit can provide the generated advice to the user in text format. The providing unit can also provide the generated advice to the user in audio format. Furthermore, the providing unit can also provide the generated advice to the user in video format. For example, the providing unit can provide the generated advice to the user in text format. The providing unit can also provide the generated advice to the user in audio format. Furthermore, the providing unit can also provide the generated advice to the user in video format. This makes it possible to provide the generated advice to the user in real time.

[0064] The reception unit can estimate the user's emotions and adjust the method of receiving the consultation content based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the method of receiving the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the consultation content to be entered quickly. This allows the method of receiving the consultation content to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The reception unit can select an appropriate reception method by referring to the user's past consultation history when receiving a call. The reception unit, for example, can select an appropriate reception method by referring to the user's past consultation history when receiving a call. For example, the reception unit can automatically display as candidates the contents of consultations that the user has frequently made in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the contents of consultations to be made in a specific time period based on the user's past consultation history. This makes it possible to select the optimal reception method based on the user's past consultation history.

[0066] The reception unit can perform filtering based on the user's current situation and areas of interest at the time of reception. The reception unit performs filtering based on the user's current situation and areas of interest at the time of reception, for example. For example, if the user inputs a consultation about their current health condition, the reception unit can preferentially display options in the related medical field. Furthermore, if the user inputs a consultation about law, the reception unit can preferentially display options in the related legal field. Furthermore, if the user inputs a consultation about finance, the reception unit can preferentially display options in the related financial field. In this way, by filtering based on the user's current situation and areas of interest, it is possible to receive appropriate consultation content.

[0067] The reception unit can estimate the user's emotions and determine the priority of the consultation contents to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the consultation contents to be received based on the estimated user emotions. For example, if the user inputs an urgent consultation, the reception unit can receive the consultation with priority. Also, if the user is feeling stressed, the reception unit can increase the priority to respond quickly. Furthermore, if the user is relaxed, the reception unit can receive the consultation with normal priority. This makes it possible to determine the priority of the consultation contents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The reception unit can prioritize reception of highly relevant consultation content based on the user's geographical location information at the time of reception. For example, the reception unit prioritizes reception of highly relevant consultation content based on the user's geographical location information at the time of reception. For example, when the user is in a specific area, the reception unit can prioritize reception of consultation content related to that area. Furthermore, when the user is traveling, the reception unit can prioritize reception of consultation content related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize reception of consultation content related to the area around the user's home. This makes it possible to prioritize reception of highly relevant consultation content based on the user's geographical location information.

[0069] The reception unit can analyze the user's social media activity at the time of reception and receive related consultation content. The reception unit, for example, analyzes the user's social media activity at the time of reception and receives related consultation content. For example, if the user posts about health on social media, the reception unit can prioritize receiving related consultation content. Furthermore, if the user posts about law on social media, the reception unit can prioritize receiving related consultation content. Furthermore, if the user posts about finance on social media, the reception unit can prioritize receiving related consultation content. In this way, related consultation content can be received based on the user's social media activity.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can perform a quick and concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can perform an analysis that focuses on the most important points. This allows the analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit can perform a detailed analysis for consultation content with high importance. Furthermore, the analysis unit can perform a concise analysis for consultation content with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for consultation content with medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the consultation content.

[0072] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, for consultation content related to medical care, the analysis unit can apply an analysis algorithm specialized for medical care. For consultation content related to legal care, the analysis unit can apply an analysis algorithm specialized for legal care. For consultation content related to financial care, the analysis unit can apply an analysis algorithm specialized for financial care. This makes it possible to apply an appropriate analysis algorithm depending on the category of the consultation content.

[0073] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priority based on the estimated user emotions. For example, if the user inputs an urgent consultation, the analysis unit can prioritize the analysis. Also, if the user is feeling stressed, the analysis unit can increase the priority for quick analysis. Furthermore, if the user is relaxed, the analysis unit can perform analysis at normal priority. This allows the analysis priority to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The analysis unit can adjust the order of analysis based on the time of submission of the consultation content during analysis. For example, the analysis unit can adjust the order of analysis based on the time of submission of the consultation content during analysis. For example, the analysis unit can prioritize the analysis of consultation content that has been submitted recently. Also, the analysis unit can postpone the analysis of consultation content that has been submitted recently. Furthermore, the analysis unit can appropriately analyze consultation content that has been submitted recently. This makes it possible to adjust the order of analysis based on the time of submission of the consultation content.

[0075] The analysis unit can improve the accuracy of the analysis by referring to literature related to the consultation content during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the consultation content during analysis, for example. For example, for consultation content related to medical care, the analysis unit can analyze by referring to the latest medical literature. For consultation content related to law, the analysis unit can analyze by referring to the latest legal literature. For consultation content related to finance, the analysis unit can analyze by referring to the latest financial literature. In this way, the accuracy of the analysis is improved by referring to related literature.

[0076] The generation unit can estimate the user's emotions and adjust the advice generation method based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the advice generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit can generate concise and specific advice. If the user is relaxed, the generation unit can generate detailed and comprehensive advice. Furthermore, if the user is in a hurry, the generation unit can generate advice that can be quickly implemented. This allows the advice generation method to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The generation unit can adjust the level of detail of the advice based on the importance of the specified specialty field at the time of generation. The generation unit, for example, adjusts the level of detail of the advice based on the importance of the specified specialty field at the time of generation. For example, for a specialty field with high importance, the generation unit can generate detailed advice. Furthermore, for a specialty field with low importance, the generation unit can generate concise advice. Furthermore, for a specialty field with medium importance, the generation unit can generate advice with an appropriate level of detail. In this way, the level of detail of the advice can be adjusted based on the importance of the specialty field.

[0078] The generation unit can apply different generation algorithms depending on the identified specialty at the time of generation. For example, the generation unit can apply different generation algorithms depending on the identified specialty at the time of generation. For example, for medical advice, the generation unit can apply a medical-specialized generation algorithm. For legal advice, the generation unit can apply a legal-specialized generation algorithm. For financial advice, the generation unit can apply a financial-specialized generation algorithm. This allows an appropriate generation algorithm to be applied depending on the specialty.

[0079] The generation unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the advice based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can generate short, to-the-point advice. If the user is relaxed, the generation unit can generate longer advice with detailed explanations. Furthermore, if the user is excited, the generation unit can generate advice with visually stimulating effects. This allows the length of the advice to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The generation unit can determine the priority of advice based on the submission time of the specified specialty field at the time of generation. The generation unit, for example, determines the priority of advice based on the submission time of the specified specialty field at the time of generation. For example, advice in a specialty field that was submitted recently can be generated preferentially. Also, advice in a specialty field that was submitted recently can be postponed. Furthermore, advice in a specialty field that was submitted at a medium time can be generated appropriately. This makes it possible to determine the priority of advice based on the submission time of the specialty field.

[0081] The generation unit can improve the accuracy of the advice by referring to related literature in the specified specialty field when generating the advice. For example, the generation unit can improve the accuracy of the advice by referring to related literature in the specified specialty field when generating the advice. For example, for medical advice, the generation unit can generate the advice by referring to the latest medical literature. For legal advice, the generation unit can generate the advice by referring to the latest legal literature. For financial advice, the generation unit can generate the advice by referring to the latest financial literature. In this way, the accuracy of the advice is improved by referring to related literature.

[0082] The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the method of providing advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible method of providing advice. If the user is relaxed, the providing unit can provide a method of providing advice that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a method of providing advice that focuses on the main points. This makes it possible to adjust the method of providing advice according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The providing unit can select an appropriate providing method by referring to the user's past advice history when providing advice. For example, the providing unit can select an appropriate providing method by referring to the user's past advice history when providing advice. For example, it can provide preferentially a providing method (text, audio, etc.) that the user has used favorably in the past. It can also suggest providing advice in a specific format based on the user's past advice history. Furthermore, it can select the optimal providing method by referring to the content of advice the user has received in the past. This makes it possible to select the optimal providing method based on the user's past advice history.

[0084] The providing unit can customize the means for providing advice based on the user's current situation at the time of providing. The providing unit, for example, customizes the means for providing advice based on the user's current situation at the time of providing. For example, if the user is on the move, the providing unit can prioritize providing advice by voice. Also, if the user is in a desktop environment, the providing unit can provide advice in the form of detailed text. Furthermore, if the user is in a meeting, the providing unit can provide advice in the form of a brief notification. In this way, the means for providing advice can be customized based on the user's current situation.

[0085] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of advice based on the estimated user emotions. For example, if the user inputs an urgent consultation, the providing unit can provide advice with priority. Also, if the user is feeling stressed, the providing unit can increase the priority to respond quickly. Furthermore, if the user is relaxed, the providing unit can provide advice with normal priority. This makes it possible to determine the priority of advice according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The providing unit can provide appropriate advice based on the user's geographical location information at the time of providing. The providing unit, for example, provides appropriate advice based on the user's geographical location information at the time of providing. For example, when the user is in a specific area, the providing unit can preferentially provide advice related to that area. Furthermore, when the user is traveling, the providing unit can preferentially provide advice related to the travel destination. Furthermore, when the user is at home, the providing unit can preferentially provide advice related to the area around the user's home. This makes it possible to provide optimal advice based on the user's geographical location information.

[0087] The providing unit can analyze the user's social media activity at the time of providing advice and suggest a means of providing the advice. For example, the providing unit can analyze the user's social media activity at the time of providing advice and suggest a means of providing the advice. For example, if the user posts about health on social media, the providing unit can prioritize providing related advice. Also, if the user posts about law on social media, the providing unit can prioritize providing related advice. Furthermore, if the user posts about finance on social media, the providing unit can prioritize providing related advice. This makes it possible to suggest the optimal means of providing advice based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives consultation content from a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received content. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates advice based on the identified specialty field. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated advice to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the consultation content from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received content. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates advice based on the identified specialty field. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated advice to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives the consultation content from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received content. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates advice based on the identified specialty field. The provision unit is realized, for example, by the speaker 240 of the headset type terminal 314 and provides the generated advice to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the consultation content from the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received content. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates advice based on the identified specialty field. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated advice to the user.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The expert advice providing system may further include a history analysis unit that analyzes the user's past advice history. The history analysis unit analyzes the content of advice the user has received in the past and the user's subsequent actions, and generates reference information for providing more appropriate advice for the current consultation content. For example, if the user has received health advice in the past and the subsequent actions were successful, the history analysis unit can generate new advice based on that success story. Also, if the user has received legal advice in the past and the subsequent actions were unsuccessful, the history analysis unit can tailor new advice based on the failure story. Furthermore, if the user has received financial advice in the past and the subsequent actions resulted in a neutral outcome, the history analysis unit can provide new advice based on that neutral example. This allows for more accurate advice to be provided based on the user's past advice history.

[0090] The reception unit may include a health monitoring unit that monitors the user's current health condition. The health monitoring unit can acquire the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time and provide them to the reception unit. For example, if the user inputs, "I'm concerned about my recent health condition," the health monitoring unit can measure the user's heart rate and blood pressure and send the data to the reception unit. Also, if the user inputs, "I feel stressed," the health monitoring unit can measure the user's body temperature and heart rate variability and send the data to the reception unit. Furthermore, if the user inputs, "I get tired easily," the health monitoring unit can analyze the user's sleep patterns and send the data to the reception unit. This allows for more appropriate advice to be provided based on the user's current health condition.

[0091] The analysis unit may include a news gathering unit that gathers the latest news and trends related to the user's consultation. The news gathering unit can gather related information from online news sites and social networking sites and provide the information to the analysis unit. For example, if a user inputs, "I'm worried about my recent health condition," the news gathering unit can gather the latest health-related news and provide the information to the analysis unit. Also, if a user inputs, "I would like to seek legal advice," the news gathering unit can gather the latest legal-related news and provide the information to the analysis unit. Furthermore, if a user inputs, "I would like to seek financial advice," the news gathering unit can gather the latest financial-related news and provide the information to the analysis unit. This makes it possible to provide more appropriate advice based on the latest information related to the user's consultation.

[0092] The generation unit may include an expert opinion aggregation unit that aggregates opinions from multiple experts based on the user's consultation content. The expert opinion aggregation unit can collect opinions from experts in various fields, such as medicine, law, and finance, and provide them to the generation unit. For example, if a user inputs, "I'm worried about my recent health condition," the expert opinion aggregation unit can collect opinions from medical experts and provide the information to the generation unit. Also, if a user inputs, "I would like to consult about legal matters," the expert opinion aggregation unit can collect opinions from legal experts and provide the information to the generation unit. Furthermore, if a user inputs, "I would like to consult about financial matters," the expert opinion aggregation unit can collect opinions from financial experts and provide the information to the generation unit. This makes it possible to provide more accurate advice based on opinions from multiple experts.

[0093] The providing unit may include a customization providing unit that customizes the format of advice provision according to the content of the user's consultation. The customization providing unit can provide advice in a format such as text, audio, or video according to the content of the user's consultation and preferences. For example, if the user inputs "I'm worried about my recent health condition," the customization providing unit can provide health advice in text format. Also, if the user inputs "I would like to consult about legal matters," the customization providing unit can provide legal advice in audio format. Furthermore, if the user inputs "I would like to consult about financial matters," the customization providing unit can provide financial advice in video format. This makes it possible to provide advice in the optimal format according to the content of the user's consultation and preferences.

[0094] The reception unit can estimate the user's emotions and provide a relaxing effect when receiving the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide relaxing music or images so that the user can enter the consultation content in a relaxed state. If the user is feeling nervous, the reception unit can provide deep breathing guidance or advice on how to relax. Furthermore, if the user is feeling anxious, the reception unit can provide messages or images that give a sense of security. This can provide a relaxing effect according to the user's emotions and provide a more comfortable consultation environment.

[0095] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide concise, positive feedback to reduce the user's stress. If the user is relaxed, the analysis unit can provide detailed, comprehensive feedback to enable the user to gain a deeper understanding. Furthermore, if the user is in a hurry, the analysis unit can provide quick, actionable feedback to enable the user to take action immediately. This allows the method of providing feedback on the analysis results to be adjusted according to the user's emotions, thereby providing more effective feedback.

[0096] The generation unit can estimate the user's emotions and adjust the tone and style of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate advice in a gentle tone to reduce the user's stress. If the user is relaxed, the generation unit can generate advice in a friendly and casual tone to allow the user to receive the advice in a relaxed state. Furthermore, if the user is in a hurry, the generation unit can generate advice in a concise and direct tone to enable the user to take action quickly. This allows the tone and style of the advice to be adjusted according to the user's emotions, making it possible to provide more effective advice.

[0097] The providing unit can estimate the user's emotions and adjust the timing of providing advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can adjust the timing of providing advice so that the user can receive the advice in a relaxed state. Furthermore, if the user is relaxed, the providing unit can adjust the timing of providing advice so that the advice can be provided at a timing that is most convenient for the user. Furthermore, if the user is in a hurry, the providing unit can quickly provide advice so that the user can take action immediately. In this way, the timing of providing advice can be adjusted according to the user's emotions, and more effective advice can be provided.

[0098] The providing unit can estimate the user's emotions and adjust the format of the advice provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a simple, highly visible format to reduce the user's stress. If the user is relaxed, the providing unit can provide advice in a format including detailed information to enable the user to understand more deeply. Furthermore, if the user is in a hurry, the providing unit can provide advice in a format that focuses on the main points to enable the user to take action quickly. In this way, the format of the advice provided can be adjusted according to the user's emotions, and more effective advice can be provided.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives the consultation content from the user. The consultation content from the user may include technical consultation, business consultation, personal consultation, etc. The reception unit can receive the consultation content input by the user in natural language. Step 2: The analysis unit analyzes the content received by the reception unit. The analysis unit analyzes the keywords and context of the consultation content using methods such as text analysis, sentiment analysis, and keyword extraction, and identifies the area of ​​expertise. Step 3: The generator generates advice based on the identified areas of expertise. The generator generates advice in real time using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and provides specialized advice tailored to the user's inquiry. Step 4: The providing unit provides the advice generated by the generating unit to the user. The providing unit can provide the generated advice to the user in real time.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0135] The data processing system 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.

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

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives inquiries from users; an analysis unit that analyzes the content received by the reception unit; a generation unit that generates advice based on the specialty field identified by the analysis unit; a providing unit that provides the advice generated by the generating unit to a user; Equipped with A system characterized by:

2. The reception unit Accepting inquiries from users in natural language The system of claim 1 .

3. The analysis unit Analyzing the content received by the reception unit and identifying a field of expertise based on keywords and context The system of claim 1 .

4. The generation unit Generate real-time advice based on identified areas of expertise The system of claim 1 .

5. The providing unit Providing generated advice to users in real time The system of claim 1 .

6. The reception unit Estimate the user's emotions and adjust the way the consultation is received based on the estimated user emotions The system of claim 1 .

7. The reception unit When accepting a call, the appropriate reception method is selected by referring to the user's past consultation history. The system of claim 1 .

8. The reception unit At the time of check-in, filtering is performed based on the user's current situation and interests. The system of claim 1 .

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