system

Abstract

The system according to the embodiment aims to provide prompt and appropriate advice and services in response to inquiries from users. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a recording unit. The reception unit receives consultation content from a user. The generation unit analyzes the consultation content received by the reception unit and generates advice and / or services. The provision unit provides the advice and / or services generated by the generation unit to the user. The recording unit records the history of the advice and / or services provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide prompt and appropriate advice and services in response to inquiries from users.

[0005] The system according to the embodiment aims to provide prompt and appropriate advice and services in response to inquiries from users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a recording unit. The reception unit receives consultation content from a user. The generation unit analyzes the consultation content received by the reception unit and generates advice and / or services. The provision unit provides the advice and / or services generated by the generation unit to the user. The recording unit records the history of the advice and / or services provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate advice and services in response to inquiries from users. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to solve users' financial worries and provide financial knowledge. In this system, a user inputs their consultation details via chat or email, and the generative AI analyzes the input consultation details, generates appropriate advice and services, and provides them to the user. For example, when a user inputs their consultation details via chat or email, the generative AI analyzes the content and generates appropriate advice and services. The generated advice and services are provided to the user. The generative AI can also provide personalized advice tailored to individual needs based on the user's consultation details. Furthermore, the generative AI can record the user's past consultation history and provide consistent advice for future consultations. For example, if a user previously inquired about "how to choose a credit card," the generative AI can use that information to provide appropriate advice for future credit card inquiries. This allows the system to efficiently solve users' financial worries and provide financial knowledge. For example, when a user inputs their consultation details via chat or email, the generative AI analyzes the content, generates appropriate advice and services, and provides them to the user. The generative AI can also provide personalized advice tailored to individual needs based on the user's consultation details. Furthermore, the generative AI records the user's past consultation history and can provide consistent advice for future consultations. This allows the system to efficiently solve users' money worries and provide financial knowledge.

[0029] A financial consultation system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a recording unit. The reception unit receives consultation requests from users. Examples of consultation requests from users include, but are not limited to, financial consultations, technical consultations, and lifestyle consultations. The reception unit receives consultation requests via chat or email, for example. The reception unit can also estimate a user's emotions and adjust the timing of receiving consultation requests based on the estimated user emotions. For example, if a user is feeling stressed, the reception unit can adjust the timing of receiving consultation requests so that the consultation requests are received immediately. The generation unit uses a generation AI to analyze the consultation requests received by the reception unit and generate appropriate advice or services. The generation unit understands the user's questions using, for example, natural language processing technology and generates appropriate answers. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. The generation unit can also estimate a user's emotions and adjust the way advice is presented based on the estimated user emotions. For example, if a user is feeling stressed, the generation unit can generate concise and easy-to-understand advice. The providing unit provides the user with the advice and services generated by the generating unit. The providing unit provides the user with the generated advice and service introductions via chat or email, for example. The providing unit can also estimate the user's emotions and adjust the way the advice is provided based on the estimated user emotions. For example, if the user is feeling stressed, the advice can be provided in a concise and easy-to-understand format. The recording unit records the history of the advice and services provided by the providing unit. For example, the recording unit stores the user's past consultation history in a database and refers to that data during future consultations. The recording unit can also estimate the user's emotions and select record data based on the estimated user emotions. For example, if the user is feeling stressed, important data can be preferentially recorded. As a result, the financial consultation system according to the embodiment can efficiently accept and analyze the content of a user's consultation, provide appropriate advice and services, and record the history.

[0030] The reception unit can accept consultation content via chat or email. Examples of chat or email include, but are not limited to, web chat, SMS, and email. The reception unit, for example, accepts consultation content input by a user via web chat. The reception unit can also accept consultation content input by a user via SMS. Furthermore, the reception unit can also accept consultation content input by a user via email. This allows the user to input consultation content via chat or email. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the content of the chat or email input by the user into a generation AI, which can analyze the content and select an appropriate reception method.

[0031] The generation unit can understand the user's question and generate an appropriate answer using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The generation unit can understand the user's question using morphological analysis, for example. The generation unit can also understand the user's question using grammatical analysis. Furthermore, the generation unit can also understand the user's question using semantic analysis. In this way, by using natural language processing technology, the user's question can be accurately understood and an appropriate answer can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's question into a generation AI, which can analyze the question and generate an appropriate answer.

[0032] The providing unit can provide the generated advice or service introduction to the user via chat or email. Examples of chat or email include, but are not limited to, web chat, SMS, and email. For example, the providing unit can provide the generated advice to the user via web chat. The providing unit can also provide the generated advice to the user via SMS. Furthermore, the providing unit can also provide the generated advice to the user via email. This allows the generated advice or service introduction to be provided to the user via chat or email. Some or all of the above-described processing in the providing unit can be performed using, or without using, a generation AI. For example, the providing unit can input the generated advice or service introduction to the generation AI, which can analyze the content and select an appropriate provision method.

[0033] The recording unit stores the user's past consultation history in a database and can refer to the data during future consultations. Examples of databases include, but are not limited to, relational databases and NoSQL databases. For example, the recording unit stores the user's past consultation history in a relational database. The recording unit can also store the user's past consultation history in a NoSQL database. Furthermore, the recording unit can store the user's past consultation history in a database and refer to the data during future consultations. This makes it possible to provide consistent advice by storing the user's past consultation history and referring to the data during future consultations. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recording unit can input the user's past consultation history into a generation AI, which can analyze the data and select an appropriate recording method.

[0034] The reception unit can analyze the user's past consultation history and select a reception method. For example, the reception unit prioritizes and suggests consultation methods (chat, email, etc.) that the user has frequently used in the past. The reception unit can also automatically suggest related topics based on the content of the user's past consultations. Furthermore, the reception unit can also suggest the optimal reception time based on the user's past consultation history. In this way, the optimal reception method can be selected by analyzing the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past consultation history into a generation AI, which then analyzes the data and selects an appropriate reception method.

[0035] When receiving consultation content, the reception unit can filter the consultation content based on the user's current financial situation or areas of interest. The reception unit, for example, analyzes the user's current financial situation and prioritizes receiving related consultation content. The reception unit can also filter related consultation content based on the user's areas of interest (investment, savings, etc.). Furthermore, the reception unit can suggest appropriate consultation content according to the user's financial situation. In this way, by filtering the consultation content based on the user's current financial situation and areas of interest, it is possible to prioritize receiving highly relevant consultation content. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's financial situation and areas of interest into the generation AI, and the generation AI can analyze the data and perform appropriate filtering.

[0036] When receiving the consultation content, the reception unit can select a reception means according to the user's input method. For example, when the user uses voice input, the reception unit receives the consultation content using voice recognition technology. Furthermore, when the user uses text input, the reception unit can analyze the consultation content using natural language processing technology. Furthermore, when the user sends an image, the reception unit can understand the consultation content using image analysis technology. This improves user convenience by selecting the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input data into a generation AI, which can analyze the data and select an appropriate reception means.

[0037] When receiving an inquiry, the reception unit can prioritize inquiries that are highly relevant based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize inquiries about financial services related to that region. The reception unit can also prioritize inquiries about financial issues specific to the region based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can prioritize inquiries about financial services at the user's travel destination. In this way, by taking the user's geographical location information into consideration, inquiries about financial issues specific to the region can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then analyze the data and select an appropriate reception method.

[0038] When receiving a consultation request, the reception unit can analyze the user's social media activity and receive relevant consultation requests. For example, the reception unit can analyze the user's social media posts and prioritize receiving related financial consultation requests. The reception unit can also refer to the activities of the user's friends on social media to suggest related financial consultation requests. Furthermore, the reception unit can prioritize receiving related financial consultation requests based on the user's social media check-in information. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving related financial consultation requests. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI, which can analyze the data and select an appropriate reception method.

[0039] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving the consultation content. The reception unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. Furthermore, the reception unit can also optimize the reception procedure based on the user's past feedback. In this way, the optimal reception method can be proposed by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, and the generation AI can analyze the data and select an appropriate reception method.

[0040] When generating advice, the generation unit can adjust the level of detail of the advice based on the priority of the consultation content. For example, the generation unit generates detailed advice for consultation content with a high level of importance. The generation unit can also generate concise advice for consultation content with a low level of importance. Furthermore, the generation unit can gradually adjust the level of detail of the advice according to the importance. In this way, by adjusting the level of detail of the advice based on the importance of the consultation content, detailed advice can be provided for important consultation content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the consultation content to the generation AI, and the generation AI can analyze the data and select an appropriate level of detail of the advice.

[0041] When generating advice, the generation unit can apply different generation algorithms depending on the type of consultation content. For example, the generation unit can apply a generation algorithm specialized for investment to a consultation content regarding investment. Furthermore, the generation unit can also apply a generation algorithm specialized for savings to a consultation content regarding savings. Furthermore, the generation unit can apply a generation algorithm specialized for credit cards to a consultation content regarding credit cards. In this way, by applying different generation algorithms depending on the category of the consultation content, more appropriate advice can be provided. Examples of generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the consultation content into the generation AI, which can analyze the data and select an appropriate generation algorithm.

[0042] When generating advice, the generation unit can improve the accuracy of generation based on the user's past advice results. The generation unit, for example, analyzes the results of advice the user has received in the past and improves the accuracy of generation. The generation unit can also generate optimal advice based on the user's past advice results. Furthermore, the generation unit can also optimize the generation algorithm by referring to the user's past advice results. In this way, the accuracy of generation can be improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past advice result data into the generation AI, and the generation AI can analyze the data and select an appropriate generation method.

[0043] When generating advice, the generation unit can determine the priority of advice based on the submission date and time of the consultation content. For example, if the consultation content is urgent, the generation unit generates advice preferentially. Also, if the consultation content is normal, the generation unit can generate advice in the order of submission. Furthermore, the generation unit can gradually adjust the priority of advice depending on the time of submission of the consultation content. In this way, by determining the priority of advice based on the time of submission of the consultation content, advice can be provided quickly for consultation content with high urgency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission date and time data of the consultation content to the generation AI, and the generation AI can analyze the data and determine an appropriate priority.

[0044] When generating advice, the generation unit can adjust the order of advice based on the relevance of the consultation content. For example, if the consultation content is highly relevant, the generation unit can generate advice preferentially. Also, if the consultation content is low relevance, the generation unit can generate advice later. Furthermore, the generation unit can gradually adjust the order of advice according to the relevance of the consultation content. In this way, by adjusting the order of advice based on the relevance of the consultation content, advice can be provided preferentially for highly relevant consultation content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the consultation content to the generation AI, and the generation AI can analyze the data and determine an appropriate order of advice.

[0045] When generating advice, the generation unit can adjust the use of technical terms in the advice according to the user's knowledge level. For example, if the user is a beginner, the generation unit can generate easy-to-understand advice by avoiding technical terms. Furthermore, if the user is an intermediate expert, the generation unit can generate advice using appropriate technical terms. Furthermore, if the user is an advanced expert, the generation unit can generate detailed advice using a lot of technical terms. By adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Knowledge level evaluation criteria include, for example, survey results, past consultation details, etc., but are not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's knowledge level data into the generation AI, which can analyze the data and determine the appropriate use of technical terms in the advice.

[0046] When providing advice, the providing unit can select a delivery method by referring to the user's past feedback. The providing unit, for example, suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also customize the delivery interface by reflecting the user's past feedback. Furthermore, the providing unit can optimize the delivery procedure based on the user's past feedback. This makes it possible to select an optimal delivery method by referring to the user's past feedback. Methods for collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then analyze the data and select an appropriate delivery method.

[0047] When providing advice, the providing unit can adjust the content of the advice based on the user's current financial situation. For example, the providing unit can analyze the user's current financial situation and provide relevant advice. The providing unit can also suggest appropriate services depending on the user's financial situation. Furthermore, the providing unit can customize optimal advice based on the user's financial situation. This allows for more appropriate advice to be provided by customizing the content of the advice based on the user's current financial situation. Criteria for evaluating the financial situation include, but are not limited to, income, expenses, and asset status. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's financial situation data into the generation AI, which can then analyze the data and select appropriate content to be provided.

[0048] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. The providing unit can improve the advice providing method, for example, based on feedback previously provided by the user. The providing unit can also optimize the advice providing interface by reflecting user feedback. Furthermore, the providing unit can improve the advice providing procedure based on user feedback. This allows the advice providing method to be improved by reflecting user feedback. Methods for collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input user feedback data into a generation AI, which can then analyze the data and select an appropriate advice providing method.

[0049] When providing advice, the providing unit can select a provision method based on the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide advice on financial services related to that region. The providing unit can also provide advice on financial issues specific to the region based on the user's geographical location information. Furthermore, if the user is traveling, the providing unit can provide advice on financial services in the user's travel destination. This makes it possible to provide advice on financial issues specific to the region by taking the user's geographical location information into consideration. Methods for acquiring geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which can then analyze the data and select an appropriate provision method.

[0050] When providing advice, the providing unit can adjust the content of the advice by analyzing the user's social media activity. For example, the providing unit can analyze the user's social media posts and provide relevant financial advice. The providing unit can also provide relevant financial advice by referring to the activities of the user's friends on social media. Furthermore, the providing unit can provide relevant financial advice based on the user's social media check-in information. In this way, relevant financial advice can be provided by analyzing the user's social media activity. Methods for analyzing social media activity include, but are not limited to, post content, number of likes, number of followers, etc. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's social media data into a generation AI, which can analyze the data and select appropriate content to provide.

[0051] When providing advice, the providing unit can adjust the advice providing method by reflecting the user's past feedback. The providing unit can, for example, propose an optimal advice providing method based on feedback provided by the user in the past. The providing unit can also customize the advice providing interface by reflecting the user's past feedback. Furthermore, the providing unit can also optimize the advice providing procedure based on the user's past feedback. This allows the optimal advice providing method to be proposed by reflecting the user's past feedback. Methods for collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then analyze the data and select an appropriate advice providing method.

[0052] The recording unit can adjust the recording algorithm by referring to the past consultation history when recording. The recording unit, for example, selects an optimal recording algorithm based on the user's past consultation history. The recording unit can also optimize the format of the recorded data by referring to the user's past consultation history. Furthermore, the recording unit can analyze the user's past consultation history and improve the recording algorithm. In this way, the recording algorithm can be optimized by referring to the past consultation history. Examples of recording algorithms include, but are not limited to, database indexing and data compression. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input the user's past consultation history data into the generation AI, which can analyze the data and select an appropriate recording algorithm.

[0053] The recording unit can modify the recorded data by reflecting user feedback during recording. For example, the recording unit updates the recorded data based on feedback provided by the user. The recording unit can also improve the format of the recorded data by reflecting user feedback. Furthermore, the recording unit can optimize the recording algorithm based on user feedback. This allows the recorded data to be updated by reflecting user feedback. Methods of collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the recording unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recording unit can input user feedback data into a generation AI, which can then analyze the data and select appropriate record data.

[0054] The recording unit can weight the recorded data based on the submission date and time of the consultation content when recording. For example, if the consultation content is urgent, the recording unit can record it with a higher weight. Furthermore, if the consultation content is normal, the recording unit can weight and record it in the order of submission. Furthermore, the recording unit can record the weighting in stages depending on the time of submission of the consultation content. In this way, by weighting the recorded data based on the time of submission of the consultation content, data with a high degree of urgency can be recorded preferentially. Methods for obtaining the submission date and time include, but are not limited to, timestamps, calendar information, etc. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recording unit can input the submission date and time data of the consultation content to a generation AI, which can analyze the data and determine an appropriate weighting.

[0055] During recording, the recording unit can integrate information from different data sources to increase the recorded data. For example, the recording unit can expand the recorded data by referring to the user's social media activity. The recording unit can also expand the recorded data by referring to the user's financial transaction history. Furthermore, the recording unit can expand the recorded data by referring to the user's past consultation history. This allows the recorded data to be expanded by integrating information from different data sources. Types of data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input information from different data sources into the generation AI, which can then analyze the data and select an appropriate recording method.

[0056] During recording, the recording unit can perform recording based on a schedule based on the user's calendar information. The recording unit, for example, performs recording by referring to a schedule registered in the user's calendar. The recording unit can also record data related to a specific event from the user's calendar information. Furthermore, the recording unit can perform recording based on the schedule based on the user's calendar information. This allows recording based on the schedule by referring to the user's calendar information. Methods for acquiring calendar information include, but are not limited to, Google (registered trademark) Calendar and Outlook (registered trademark) Calendar. Some or all of the above-described processing in the recording unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recording unit can input the user's calendar information data into the generation AI, which can then analyze the data and select an appropriate recording method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past consultation history and select a reception method. For example, it can prioritize and suggest consultation methods (chat, email, etc.) that the user has frequently used in the past. The reception unit can also automatically suggest related topics based on the content of the user's past consultations. Furthermore, the reception unit can also suggest the optimal reception time based on the user's past consultation history. In this way, the optimal reception method can be selected by analyzing the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past consultation history into a generation AI, which can analyze the data and select an appropriate reception method.

[0059] When generating advice, the generation unit can adjust the level of detail of the advice based on the priority of the consultation content. For example, detailed advice is generated for consultation content with a high level of importance. The generation unit can also generate concise advice for consultation content with a low level of importance. Furthermore, the generation unit can gradually adjust the level of detail of the advice according to the importance. In this way, by adjusting the level of detail of the advice based on the importance of the consultation content, detailed advice can be provided for important consultation content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the consultation content to the generation AI, which can analyze the data and select an appropriate level of detail of the advice.

[0060] When providing advice, the providing unit can adjust the content of the advice based on the user's current financial situation. For example, the providing unit can analyze the user's current financial situation and provide relevant advice. The providing unit can also suggest appropriate services depending on the user's financial situation. Furthermore, the providing unit can customize optimal advice based on the user's financial situation. This allows for more appropriate advice to be provided by customizing the content of the advice based on the user's current financial situation. Criteria for evaluating the financial situation include, but are not limited to, income, expenses, and asset status. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's financial situation data into the generation AI, which can then analyze the data and select appropriate content to be provided.

[0061] During recording, the recording unit can integrate information from different data sources to increase the recorded data. For example, the recording unit can expand the recorded data by referring to the user's social media activity. The recording unit can also expand the recorded data by referring to the user's financial transaction history. Furthermore, the recording unit can expand the recorded data by referring to the user's past consultation history. This allows the recorded data to be expanded by integrating information from different data sources. Types of data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input information from different data sources into the generation AI, which can then analyze the data and select an appropriate recording method.

[0062] When generating advice, the generation unit can adjust the use of technical terms in the advice according to the user's knowledge level. For example, if the user is a beginner, the generation unit can generate easy-to-understand advice by avoiding technical terms. Furthermore, if the user is an intermediate expert, the generation unit can generate advice using appropriate technical terms. Furthermore, if the user is an advanced expert, the generation unit can generate detailed advice using a lot of technical terms. By adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Knowledge level evaluation criteria include, for example, survey results, past consultation details, etc., but are not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's knowledge level data into the generation AI, and the generation AI can analyze the data to determine the appropriate use of technical terms in the advice.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives the consultation content from the user. The consultation content from the user includes, but is not limited to, for example, financial consultation, technical consultation, and lifestyle consultation. The reception unit receives the consultation content via chat or email, for example. The reception unit can also estimate the user's emotions and adjust the timing of receiving the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can adjust the timing to receive the consultation content immediately. Step 2: The generation unit uses a generation AI to analyze the consultation content received by the reception unit and generate appropriate advice and services. The generation unit uses, for example, natural language processing technology to understand the user's question and generate an appropriate answer. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can also estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. For example, if the user is feeling stressed, concise and easy-to-understand advice can be generated. Step 3: The providing unit provides the user with the advice or service generated by the generating unit. For example, the providing unit provides the user with the generated advice or service introduction via chat or email. The providing unit can also estimate the user's emotions and adjust the way in which the advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a concise and easy-to-understand format. Step 4: The recording unit records the history of advice and services provided by the providing unit. For example, the recording unit stores the user's past consultation history in a database and refers to that data during future consultations. The recording unit can also estimate the user's emotions and select recorded data based on the estimated user emotions. For example, if the user is feeling stressed, important data can be recorded with priority.

[0065] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to solve users' financial worries and provide financial knowledge. In this system, a user inputs their consultation details via chat or email, and the generative AI analyzes the input consultation details, generates appropriate advice and services, and provides them to the user. For example, when a user inputs their consultation details via chat or email, the generative AI analyzes the content and generates appropriate advice and services. The generated advice and services are provided to the user. The generative AI can also provide personalized advice tailored to individual needs based on the user's consultation details. Furthermore, the generative AI can record the user's past consultation history and provide consistent advice for future consultations. For example, if a user previously inquired about "how to choose a credit card," the generative AI can use that information to provide appropriate advice for future credit card inquiries. This allows the system to efficiently solve users' financial worries and provide financial knowledge. For example, when a user inputs their consultation details via chat or email, the generative AI analyzes the content, generates appropriate advice and services, and provides them to the user. The generative AI can also provide personalized advice tailored to individual needs based on the user's consultation details. Furthermore, the generative AI records the user's past consultation history and can provide consistent advice for future consultations. This allows the system to efficiently solve users' money worries and provide financial knowledge.

[0066] A financial consultation system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a recording unit. The reception unit receives consultation requests from users. Examples of consultation requests from users include, but are not limited to, financial consultations, technical consultations, and lifestyle consultations. The reception unit receives consultation requests via chat or email, for example. The reception unit can also estimate a user's emotions and adjust the timing of receiving consultation requests based on the estimated user emotions. For example, if a user is feeling stressed, the reception unit can adjust the timing of receiving consultation requests so that the consultation requests are received immediately. The generation unit uses a generation AI to analyze the consultation requests received by the reception unit and generate appropriate advice or services. The generation unit understands the user's questions using, for example, natural language processing technology and generates appropriate answers. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. The generation unit can also estimate a user's emotions and adjust the way advice is presented based on the estimated user emotions. For example, if a user is feeling stressed, the generation unit can generate concise and easy-to-understand advice. The providing unit provides the user with the advice and services generated by the generating unit. The providing unit provides the user with the generated advice and service introductions via chat or email, for example. The providing unit can also estimate the user's emotions and adjust the way the advice is provided based on the estimated user emotions. For example, if the user is feeling stressed, the advice can be provided in a concise and easy-to-understand format. The recording unit records the history of the advice and services provided by the providing unit. For example, the recording unit stores the user's past consultation history in a database and refers to that data during future consultations. The recording unit can also estimate the user's emotions and select record data based on the estimated user emotions. For example, if the user is feeling stressed, important data can be preferentially recorded. As a result, the financial consultation system according to the embodiment can efficiently accept and analyze the content of a user's consultation, provide appropriate advice and services, and record the history.

[0067] The reception unit can accept consultation content via chat or email. Examples of chat or email include, but are not limited to, web chat, SMS, and email. The reception unit, for example, accepts consultation content input by a user via web chat. The reception unit can also accept consultation content input by a user via SMS. Furthermore, the reception unit can also accept consultation content input by a user via email. This allows the user to input consultation content via chat or email. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the content of the chat or email input by the user into a generation AI, which can analyze the content and select an appropriate reception method.

[0068] The generation unit can understand the user's question and generate an appropriate answer using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The generation unit can understand the user's question using morphological analysis, for example. The generation unit can also understand the user's question using grammatical analysis. Furthermore, the generation unit can also understand the user's question using semantic analysis. In this way, by using natural language processing technology, the user's question can be accurately understood and an appropriate answer can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's question into a generation AI, which can analyze the question and generate an appropriate answer.

[0069] The providing unit can provide the generated advice or service introduction to the user via chat or email. Examples of chat or email include, but are not limited to, web chat, SMS, and email. For example, the providing unit can provide the generated advice to the user via web chat. The providing unit can also provide the generated advice to the user via SMS. Furthermore, the providing unit can also provide the generated advice to the user via email. This allows the generated advice or service introduction to be provided to the user via chat or email. Some or all of the above-described processing in the providing unit can be performed using, or without using, a generation AI. For example, the providing unit can input the generated advice or service introduction to the generation AI, which can analyze the content and select an appropriate provision method.

[0070] The recording unit stores the user's past consultation history in a database and can refer to the data during future consultations. Examples of databases include, but are not limited to, relational databases and NoSQL databases. For example, the recording unit stores the user's past consultation history in a relational database. The recording unit can also store the user's past consultation history in a NoSQL database. Furthermore, the recording unit can store the user's past consultation history in a database and refer to the data during future consultations. This makes it possible to provide consistent advice by storing the user's past consultation history and referring to the data during future consultations. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the recording unit can input the user's past consultation history into a generation AI, which can analyze the data and select an appropriate recording method.

[0071] The reception unit can estimate the user's emotions and adjust the timing of receiving the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can adjust to receive the consultation content immediately. Furthermore, if the user is relaxed, the reception unit can also adjust to receive the consultation content more slowly and in a more detailed manner. Furthermore, if the user is in a hurry, the reception unit can also adjust to receive the consultation content quickly in a concise question format. By adjusting the timing of receiving the consultation content according to the user's emotions, the consultation content can be received at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then analyze the data and select an appropriate timing for receiving the consultation.

[0072] The reception unit can analyze the user's past consultation history and select a reception method. For example, the reception unit prioritizes and suggests consultation methods (chat, email, etc.) that the user has frequently used in the past. The reception unit can also automatically suggest related topics based on the content of the user's past consultations. Furthermore, the reception unit can also suggest the optimal reception time based on the user's past consultation history. In this way, the optimal reception method can be selected by analyzing the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past consultation history into a generation AI, which then analyzes the data and selects an appropriate reception method.

[0073] When receiving consultation content, the reception unit can filter the consultation content based on the user's current financial situation or areas of interest. The reception unit, for example, analyzes the user's current financial situation and prioritizes receiving related consultation content. The reception unit can also filter related consultation content based on the user's areas of interest (investment, savings, etc.). Furthermore, the reception unit can suggest appropriate consultation content according to the user's financial situation. In this way, by filtering the consultation content based on the user's current financial situation and areas of interest, it is possible to prioritize receiving highly relevant consultation content. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's financial situation and areas of interest into the generation AI, and the generation AI can analyze the data and perform appropriate filtering.

[0074] When receiving the consultation content, the reception unit can select a reception means according to the user's input method. For example, when the user uses voice input, the reception unit receives the consultation content using voice recognition technology. Furthermore, when the user uses text input, the reception unit can analyze the consultation content using natural language processing technology. Furthermore, when the user sends an image, the reception unit can understand the consultation content using image analysis technology. This improves user convenience by selecting the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input data into a generation AI, which can analyze the data and select an appropriate reception means.

[0075] The reception unit can estimate the user's emotions and determine the priority of consultation requests to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize urgent consultation requests. Furthermore, when the user is relaxed, the reception unit can prioritize detailed consultation requests. Furthermore, when the user is in a hurry, the reception unit can prioritize brief consultation requests. Thus, by determining the priority of consultation requests according to the user's emotions, urgent consultation requests can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then analyze the data and determine appropriate priorities.

[0076] When receiving an inquiry, the reception unit can prioritize inquiries that are highly relevant based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize inquiries about financial services related to that region. The reception unit can also prioritize inquiries about financial issues specific to the region based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can prioritize inquiries about financial services at the user's travel destination. In this way, by taking the user's geographical location information into consideration, inquiries about financial issues specific to the region can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then analyze the data and select an appropriate reception method.

[0077] When receiving a consultation request, the reception unit can analyze the user's social media activity and receive relevant consultation requests. For example, the reception unit can analyze the user's social media posts and prioritize receiving related financial consultation requests. The reception unit can also refer to the activities of the user's friends on social media to suggest related financial consultation requests. Furthermore, the reception unit can prioritize receiving related financial consultation requests based on the user's social media check-in information. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving related financial consultation requests. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI, which can analyze the data and select an appropriate reception method.

[0078] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving the consultation content. The reception unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. Furthermore, the reception unit can also optimize the reception procedure based on the user's past feedback. In this way, the optimal reception method can be proposed by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, and the generation AI can analyze the data and select an appropriate reception method.

[0079] The generation unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate concise and easy-to-understand advice. Furthermore, if the user is relaxed, the generation unit can generate detailed and courteous advice. Furthermore, if the user is in a hurry, the generation unit can generate quick advice that focuses on the main points. This allows the user to provide more appropriate advice by adjusting the way the advice is presented according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze the data and select an appropriate way to present the advice.

[0080] When generating advice, the generation unit can adjust the level of detail of the advice based on the priority of the consultation content. For example, the generation unit generates detailed advice for consultation content with a high level of importance. The generation unit can also generate concise advice for consultation content with a low level of importance. Furthermore, the generation unit can gradually adjust the level of detail of the advice according to the importance. In this way, by adjusting the level of detail of the advice based on the importance of the consultation content, detailed advice can be provided for important consultation content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the consultation content to the generation AI, and the generation AI can analyze the data and select an appropriate level of detail of the advice.

[0081] When generating advice, the generation unit can apply different generation algorithms depending on the type of consultation content. For example, the generation unit can apply a generation algorithm specialized for investment to a consultation content regarding investment. Furthermore, the generation unit can also apply a generation algorithm specialized for savings to a consultation content regarding savings. Furthermore, the generation unit can apply a generation algorithm specialized for credit cards to a consultation content regarding credit cards. In this way, by applying different generation algorithms depending on the category of the consultation content, more appropriate advice can be provided. Examples of generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the consultation content into the generation AI, which can analyze the data and select an appropriate generation algorithm.

[0082] When generating advice, the generation unit can improve the accuracy of generation based on the user's past advice results. The generation unit, for example, analyzes the results of advice the user has received in the past and improves the accuracy of generation. The generation unit can also generate optimal advice based on the user's past advice results. Furthermore, the generation unit can also optimize the generation algorithm by referring to the user's past advice results. In this way, the accuracy of generation can be improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past advice result data into the generation AI, and the generation AI can analyze the data and select an appropriate generation method.

[0083] The generation unit can estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate short, to-the-point advice. Furthermore, if the user is relaxed, the generation unit can generate longer advice with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate concise, quick advice. This allows for more appropriate advice to be provided by adjusting the length of the advice according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze the data and select an appropriate length of advice.

[0084] When generating advice, the generation unit can determine the priority of advice based on the submission date and time of the consultation content. For example, if the consultation content is urgent, the generation unit generates advice preferentially. Also, if the consultation content is normal, the generation unit can generate advice in the order of submission. Furthermore, the generation unit can gradually adjust the priority of advice depending on the time of submission of the consultation content. In this way, by determining the priority of advice based on the time of submission of the consultation content, advice can be provided quickly for consultation content with high urgency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission date and time data of the consultation content to the generation AI, and the generation AI can analyze the data and determine an appropriate priority.

[0085] When generating advice, the generation unit can adjust the order of advice based on the relevance of the consultation content. For example, if the consultation content is highly relevant, the generation unit can generate advice preferentially. Also, if the consultation content is low relevance, the generation unit can generate advice later. Furthermore, the generation unit can gradually adjust the order of advice according to the relevance of the consultation content. In this way, by adjusting the order of advice based on the relevance of the consultation content, advice can be provided preferentially for highly relevant consultation content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the consultation content to the generation AI, and the generation AI can analyze the data and determine an appropriate order of advice.

[0086] When generating advice, the generation unit can adjust the use of technical terms in the advice according to the user's knowledge level. For example, if the user is a beginner, the generation unit can generate easy-to-understand advice by avoiding technical terms. Furthermore, if the user is an intermediate expert, the generation unit can generate advice using appropriate technical terms. Furthermore, if the user is an advanced expert, the generation unit can generate detailed advice using a lot of technical terms. By adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Knowledge level evaluation criteria include, for example, survey results, past consultation details, etc., but are not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's knowledge level data into the generation AI, which can analyze the data and determine the appropriate use of technical terms in the advice.

[0087] The providing unit can estimate the user's emotions and adjust the advice providing method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a concise and easy-to-understand format. Furthermore, if the user is relaxed, the providing unit can also provide advice in a format including detailed explanations. Furthermore, if the user is in a hurry, the providing unit can also provide advice in a quick format that focuses on the main points. By adjusting the advice providing method according to the user's emotions, the advice can be provided in a more appropriate format. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then analyze the data and select an appropriate advice providing method.

[0088] When providing advice, the providing unit can select a delivery method by referring to the user's past feedback. The providing unit, for example, suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also customize the delivery interface by reflecting the user's past feedback. Furthermore, the providing unit can optimize the delivery procedure based on the user's past feedback. This makes it possible to select an optimal delivery method by referring to the user's past feedback. Methods for collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then analyze the data and select an appropriate delivery method.

[0089] When providing advice, the providing unit can adjust the content of the advice based on the user's current financial situation. For example, the providing unit can analyze the user's current financial situation and provide relevant advice. The providing unit can also suggest appropriate services depending on the user's financial situation. Furthermore, the providing unit can customize optimal advice based on the user's financial situation. This allows for more appropriate advice to be provided by customizing the content of the advice based on the user's current financial situation. Criteria for evaluating the financial situation include, but are not limited to, income, expenses, and asset status. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's financial situation data into the generation AI, which can then analyze the data and select appropriate content to be provided.

[0090] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. The providing unit can improve the advice providing method, for example, based on feedback previously provided by the user. The providing unit can also optimize the advice providing interface by reflecting user feedback. Furthermore, the providing unit can improve the advice providing procedure based on user feedback. This allows the advice providing method to be improved by reflecting user feedback. Methods for collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input user feedback data into a generation AI, which can then analyze the data and select an appropriate advice providing method.

[0091] The providing unit can estimate the user's emotions and determine the order in which advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing advice with a high level of urgency. Furthermore, if the user is relaxed, the providing unit can also prioritize providing detailed advice. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise advice. Thus, by determining the order in which advice is provided according to the user's emotions, it is possible to prioritize providing advice with a high level of urgency. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can analyze the data and determine an appropriate advice provision order.

[0092] When providing advice, the providing unit can select a provision method based on the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide advice on financial services related to that region. The providing unit can also provide advice on financial issues specific to the region based on the user's geographical location information. Furthermore, if the user is traveling, the providing unit can provide advice on financial services in the user's travel destination. This makes it possible to provide advice on financial issues specific to the region by taking the user's geographical location information into consideration. Methods for acquiring geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which can then analyze the data and select an appropriate provision method.

[0093] When providing advice, the providing unit can adjust the content of the advice by analyzing the user's social media activity. For example, the providing unit can analyze the user's social media posts and provide relevant financial advice. The providing unit can also provide relevant financial advice by referring to the activities of the user's friends on social media. Furthermore, the providing unit can provide relevant financial advice based on the user's social media check-in information. In this way, relevant financial advice can be provided by analyzing the user's social media activity. Methods for analyzing social media activity include, but are not limited to, post content, number of likes, number of followers, etc. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's social media data into a generation AI, which can analyze the data and select appropriate content to provide.

[0094] When providing advice, the providing unit can adjust the advice providing method by reflecting the user's past feedback. The providing unit can, for example, propose an optimal advice providing method based on feedback provided by the user in the past. The providing unit can also customize the advice providing interface by reflecting the user's past feedback. Furthermore, the providing unit can also optimize the advice providing procedure based on the user's past feedback. This allows the optimal advice providing method to be proposed by reflecting the user's past feedback. Methods for collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then analyze the data and select an appropriate advice providing method.

[0095] The recording unit can estimate the user's emotions and select record data based on the estimated user emotions. For example, when the user is feeling stressed, the recording unit prioritizes recording important data. The recording unit can also record detailed data when the user is relaxed. Furthermore, when the user is in a hurry, the recording unit can record concise data. This allows important data to be prioritized by selecting record data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the recording unit can input the user's emotion data into the generation AI, which then analyzes the data and selects appropriate record data.

[0096] The recording unit can adjust the recording algorithm by referring to the past consultation history when recording. The recording unit, for example, selects an optimal recording algorithm based on the user's past consultation history. The recording unit can also optimize the format of the recorded data by referring to the user's past consultation history. Furthermore, the recording unit can analyze the user's past consultation history and improve the recording algorithm. In this way, the recording algorithm can be optimized by referring to the past consultation history. Examples of recording algorithms include, but are not limited to, database indexing and data compression. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input the user's past consultation history data into the generation AI, which can analyze the data and select an appropriate recording algorithm.

[0097] The recording unit can modify the recorded data by reflecting user feedback during recording. For example, the recording unit updates the recorded data based on feedback provided by the user. The recording unit can also improve the format of the recorded data by reflecting user feedback. Furthermore, the recording unit can optimize the recording algorithm based on user feedback. This allows the recorded data to be updated by reflecting user feedback. Methods of collecting feedback include, but are not limited to, survey results and user reviews. Some or all of the above-described processing in the recording unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recording unit can input user feedback data into a generation AI, which can then analyze the data and select appropriate record data.

[0098] The recording unit can estimate the user's emotions and adjust the recording frequency based on the estimated user emotions. For example, the recording unit can record frequently when the user is stressed. Furthermore, the recording unit can also record at a moderate frequency when the user is relaxed. Furthermore, the recording unit can record only as frequently as necessary when the user is in a hurry. This allows recording to be performed at an appropriate frequency by adjusting the recording frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the recording unit can input the user's emotion data into the generation AI, which then analyzes the data and determines an appropriate recording frequency.

[0099] The recording unit can weight the recorded data based on the submission date and time of the consultation content when recording. For example, if the consultation content is urgent, the recording unit can record it with a higher weight. Furthermore, if the consultation content is normal, the recording unit can weight and record it in the order of submission. Furthermore, the recording unit can record the weighting in stages depending on the time of submission of the consultation content. In this way, by weighting the recorded data based on the time of submission of the consultation content, data with a high degree of urgency can be recorded preferentially. Methods for obtaining the submission date and time include, but are not limited to, timestamps, calendar information, etc. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recording unit can input the submission date and time data of the consultation content to a generation AI, which can analyze the data and determine an appropriate weighting.

[0100] During recording, the recording unit can integrate information from different data sources to increase the recorded data. For example, the recording unit can expand the recorded data by referring to the user's social media activity. The recording unit can also expand the recorded data by referring to the user's financial transaction history. Furthermore, the recording unit can expand the recorded data by referring to the user's past consultation history. This allows the recorded data to be expanded by integrating information from different data sources. Types of data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input information from different data sources into the generation AI, which can then analyze the data and select an appropriate recording method.

[0101] During recording, the recording unit can perform recording based on a schedule based on the user's calendar information. The recording unit, for example, performs recording by referring to a schedule registered in the user's calendar. The recording unit can also record data related to a specific event from the user's calendar information. Furthermore, the recording unit can perform recording based on the schedule based on the user's calendar information. This allows recording based on the schedule by referring to the user's calendar information. Methods for acquiring calendar information include, but are not limited to, Google Calendar and Outlook Calendar. Some or all of the above-described processing in the recording unit can be performed using, or without, a generation AI. For example, the recording unit can input the user's calendar information data into the generation AI, which can then analyze the data and select an appropriate recording method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recording unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the consultation content from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI and generates appropriate advice or service. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated advice or service to the user. The recording unit is realized, for example, by the database 24 of the data processing device 12 and records the history of the advice or service provided. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recording 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 generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI and generates appropriate advice or service. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated advice or service to the user. The recording unit is realized, for example, by the database 24 of the data processing device 12 and records the history of the advice or service provided. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recording 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 generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI and generates appropriate advice or services. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the generated advice or services to the user. The recording unit is realized, for example, by the database 24 of the data processing device 12 and records the history of the advice or services provided. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recording 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 generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI and generates appropriate advice or service. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated advice or service to the user. The recording unit is realized, for example, by the database 24 of the data processing device 12 and records the history of the advice or service provided.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can analyze the user's past consultation history and select a reception method. For example, it can prioritize and suggest consultation methods (chat, email, etc.) that the user has frequently used in the past. The reception unit can also automatically suggest related topics based on the content of the user's past consultations. Furthermore, the reception unit can also suggest the optimal reception time based on the user's past consultation history. In this way, the optimal reception method can be selected by analyzing the user's past consultation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past consultation history into a generation AI, which can analyze the data and select an appropriate reception method.

[0104] When generating advice, the generation unit can adjust the level of detail of the advice based on the priority of the consultation content. For example, detailed advice is generated for consultation content with a high level of importance. The generation unit can also generate concise advice for consultation content with a low level of importance. Furthermore, the generation unit can gradually adjust the level of detail of the advice according to the importance. In this way, by adjusting the level of detail of the advice based on the importance of the consultation content, detailed advice can be provided for important consultation content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the consultation content to the generation AI, which can analyze the data and select an appropriate level of detail of the advice.

[0105] When providing advice, the providing unit can adjust the content of the advice based on the user's current financial situation. For example, the providing unit can analyze the user's current financial situation and provide relevant advice. The providing unit can also suggest appropriate services depending on the user's financial situation. Furthermore, the providing unit can customize optimal advice based on the user's financial situation. This allows for more appropriate advice to be provided by customizing the content of the advice based on the user's current financial situation. Criteria for evaluating the financial situation include, but are not limited to, income, expenses, and asset status. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's financial situation data into the generation AI, which can then analyze the data and select appropriate content to be provided.

[0106] During recording, the recording unit can integrate information from different data sources to increase the recorded data. For example, the recording unit can expand the recorded data by referring to the user's social media activity. The recording unit can also expand the recorded data by referring to the user's financial transaction history. Furthermore, the recording unit can expand the recorded data by referring to the user's past consultation history. This allows the recorded data to be expanded by integrating information from different data sources. Types of data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can input information from different data sources into the generation AI, which can then analyze the data and select an appropriate recording method.

[0107] When generating advice, the generation unit can adjust the use of technical terms in the advice according to the user's knowledge level. For example, if the user is a beginner, the generation unit can generate easy-to-understand advice by avoiding technical terms. Furthermore, if the user is an intermediate expert, the generation unit can generate advice using appropriate technical terms. Furthermore, if the user is an advanced expert, the generation unit can generate detailed advice using a lot of technical terms. By adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Knowledge level evaluation criteria include, for example, survey results, past consultation details, etc., but are not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's knowledge level data into the generation AI, and the generation AI can analyze the data to determine the appropriate use of technical terms in the advice.

[0108] The reception unit can estimate the user's emotions and adjust the timing of accepting the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can adjust the timing to accept the consultation content immediately. Furthermore, if the user is relaxed, the reception unit can also adjust the timing to accept the consultation content more quickly, taking a little more time. Furthermore, if the user is in a hurry, the reception unit can also adjust the timing to accept the consultation content quickly in the form of a simple question. By adjusting the timing to accept the consultation content according to the user's emotions, the consultation content can be accepted at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, and the generation AI can analyze the data and select an appropriate timing to accept the consultation.

[0109] The generation unit can estimate the user's emotions and adjust the way advice is presented based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate concise and easy-to-understand advice. Furthermore, if the user is relaxed, the generation unit can generate detailed and courteous advice. Furthermore, if the user is in a hurry, the generation unit can generate quick advice that focuses on the main points. This allows for more appropriate advice to be provided by adjusting the way advice is presented according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze the data and select an appropriate way to present the advice.

[0110] The providing unit can estimate the user's emotions and adjust the advice provision method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a concise and easy-to-understand format. Furthermore, if the user is relaxed, the providing unit can provide advice in a format including detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide advice in a quick format that focuses on the main points. By adjusting the advice provision method according to the user's emotions, the advice can be provided in a more appropriate format. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then analyze the data and select an appropriate advice provision method.

[0111] The recording unit can estimate the user's emotions and select record data based on the estimated user emotions. For example, if the user is feeling stressed, important data is recorded preferentially. The recording unit can also record detailed data if the user is relaxed. Furthermore, the recording unit can record concise data if the user is in a hurry. This allows important data to be recorded preferentially by selecting record data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the recording unit can input the user's emotion data into the generation AI, which can then analyze the data and select appropriate record data.

[0112] The recording unit can estimate the user's emotions and adjust the recording frequency based on the estimated user emotions. For example, if the user is feeling stressed, the recording unit can record frequently. Furthermore, if the user is relaxed, the recording unit can record at a moderate frequency. Furthermore, if the user is in a hurry, the recording unit can record only as frequently as necessary. By adjusting the recording frequency according to the user's emotions, recording can be performed at an appropriate frequency. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit can be performed using, for example, the generation AI, or without the generation AI. For example, the recording unit can input the user's emotion data into the generation AI, which can then analyze the data and determine an appropriate recording frequency.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives the consultation content from the user. The consultation content from the user includes, but is not limited to, for example, financial consultation, technical consultation, and lifestyle consultation. The reception unit receives the consultation content via chat or email, for example. The reception unit can also estimate the user's emotions and adjust the timing of receiving the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can adjust the timing to receive the consultation content immediately. Step 2: The generation unit uses a generation AI to analyze the consultation content received by the reception unit and generate appropriate advice and services. The generation unit uses, for example, natural language processing technology to understand the user's question and generate an appropriate answer. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can also estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. For example, if the user is feeling stressed, concise and easy-to-understand advice can be generated. Step 3: The providing unit provides the user with the advice or service generated by the generating unit. For example, the providing unit provides the user with the generated advice or service introduction via chat or email. The providing unit can also estimate the user's emotions and adjust the way in which the advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a concise and easy-to-understand format. Step 4: The recording unit records the history of advice and services provided by the providing unit. For example, the recording unit stores the user's past consultation history in a database and refers to that data during future consultations. The recording unit can also estimate the user's emotions and select recorded data based on the estimated user emotions. For example, if the user is feeling stressed, important data can be recorded with priority.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the 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.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

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

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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; a generation unit that analyzes the consultation content received by the reception unit and generates advice and services; a providing unit that provides the advice and service generated by the generating unit to a user; a recording unit that records a history of advice and services provided by the providing unit; A system characterized by:

2. The reception unit Accept enquiries via chat or email 2. The system of claim 1.

3. The generation unit Uses natural language processing technology to understand user questions and generate appropriate answers 2. The system of claim 1.

4. The providing unit Providing generated advice or service referrals to users via chat or email 2. The system of claim 1.

5. The recording unit Save the user's past consultation history in a database and refer to that data for future consultations.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of accepting consultations based on the estimated user emotions.

2. The system of claim 1.

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

8. The reception unit Filtering inquiries based on the user's current financial situation or areas of interest 2. The system of claim 1.

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