System

Abstract

To provide a system capable of efficiently performing the temporary examination of a housing loan, customer support and the provision of education information.SOLUTION: The system includes a first collecting unit, a first analyzing unit, a first providing unit, a second collecting unit, a generating unit, a second providing unit, a third collecting unit, a second analyzing unit, a third providing unit, a receiving unit, and an associating unit. The first collection unit collects customer information. The first analysis unit analyzes the collected information. The first provision unit provides a provisional examination result on the basis of the analyzed information. The second collection unit collects needs of the customer. The generation unit generates advice based on the collected needs. The second provision unit provides the generated advice. The third collection unit collects educational information. The second analysis unit analyzes the collected information. The third providing unit provides information based on the information. The reception unit receives an inquiry from a customer. The responder responds based on the received inquiry.SELECTED DRAWING: Figure 1

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 technology does not efficiently handle preliminary mortgage screening, customer support, or the provision of educational information, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently provide preliminary mortgage screening, customer support, and educational information. [Means for solving the problem]

[0006] The system according to the embodiment includes a first collection unit, a first analysis unit, a first provision unit, a second collection unit, a generation unit, a second provision unit, a third collection unit, a second analysis unit, a third provision unit, a reception unit, and a response unit. The first collection unit collects customer information. The first analysis unit analyzes the information collected by the first collection unit. The first provision unit provides a provisional screening result based on the information analyzed by the first analysis unit. The second collection unit collects customer needs. The generation unit generates advice based on the needs collected by the second collection unit. The second provision unit provides the advice generated by the generation unit. The third collection unit collects educational information. The second analysis unit analyzes the information collected by the third collection unit. The third provision unit provides information based on the information analyzed by the second analysis unit. The reception unit receives customer inquiries. The response unit responds based on the inquiries received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide preliminary mortgage screening, customer support, and educational information. [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 mortgage support system according to an embodiment of the present invention utilizes a generative AI to collect and analyze customer information and provide preliminary screening results and advice. The mortgage support system allows customers to use the generative AI to resolve questions about loan application eligibility and procedures, and to receive guidance on the necessary documents and procedures. The generative AI is available 24 hours a day, enhancing customer convenience. The mortgage support system also provides customers with mortgage planning and advice. For example, customers can interact with the generative AI to select a repayment plan, obtain interest rate estimates, and obtain information on additional costs and taxes. Furthermore, the mortgage support system educates and provides customers with information about mortgages. For example, customers can learn basic mortgage knowledge, important terminology, and market trends through the generative AI. The generative AI provides customers with relevant news and articles to deepen their understanding. Finally, the mortgage support system accelerates customer service and improves operational efficiency. The generative AI responds immediately to customer inquiries and requests, eliminating the need for human staff intervention. This improves the quality of customer service and reduces costs. This allows the mortgage support system to enhance customer convenience and provide faster and more efficient service. For example, customers can have their questions about loan application eligibility and procedures resolved 24 hours a day. Customers can also receive customized advice through generative AI, deepening their knowledge about home loans. Furthermore, the use of generative AI can improve operational efficiency, reduce costs, and improve the quality of customer service.

[0029] A housing loan support system according to an embodiment includes a first collection unit, a first analysis unit, a first provision unit, a second collection unit, a generation unit, a second provision unit, a third collection unit, a second analysis unit, a third provision unit, a reception unit, and a response unit. The first collection unit collects customer information. The customer information includes, but is not limited to, for example, name, address, purchase history, and inquiry history. The first collection unit collects customer information through, for example, a questionnaire. The first collection unit can also collect customer behavior data using a sensor. The first collection unit can also collect customer information by analyzing log data. The first analysis unit analyzes the information collected by the first collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. The first analysis unit analyzes the customer information using, for example, statistical analysis. The first analysis unit can also analyze customer behavior patterns using a machine learning algorithm. The first analysis unit can also analyze the customer information using data mining technology. The first providing unit provides a provisional screening result based on the information analyzed by the first analyzing unit. The provisional screening result may include, but is not limited to, for example, pass / fail, scoring, risk assessment, etc. The first providing unit may provide, for example, a pass / fail result. The first providing unit may also provide a scoring result. The first providing unit may also provide a risk assessment result. The second collecting unit collects customer needs. The customer needs may include, but are not limited to, for example, product features, service quality, price range, etc. The second collecting unit may collect customer needs through, for example, a questionnaire. The second collecting unit may also collect customer behavior data using a sensor. The second collecting unit may also collect customer needs by analyzing log data. The generating unit generates advice based on the needs collected by the second collecting unit. The advice may include, but is not limited to, for example, how to select a product, how to use a product, and improvement suggestions. The generating unit may generate advice on how to select a product. The generating unit may also generate advice on how to use a product. Furthermore, the generator can also generate advice regarding improvement suggestions.The second providing unit provides the advice generated by the generating unit. The second providing unit provides, for example, advice on how to select a product. The second providing unit can also provide advice on how to use the product. The second providing unit can also provide advice on improvement suggestions. The third collecting unit collects educational information. The educational information includes, for example, but is not limited to, learning materials, educational programs, and training materials. The third collecting unit collects, for example, information on the Internet. The third collecting unit can also collect information from books and papers. The third collecting unit can also collect information through interviews with experts. The second analyzing unit analyzes the information collected by the third collecting unit. The analysis can be performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to these examples. The second analyzing unit analyzes the educational information using, for example, statistical analysis. The second analyzing unit can also analyze the educational information using a machine learning algorithm. The second analyzing unit can also analyze the educational information using data mining technology. The third providing unit provides information based on the information analyzed by the second analyzing unit. The third providing unit provides, for example, information on learning materials. The third providing unit can also provide information on educational programs. The third providing unit can also provide information on training materials. The reception unit accepts customer inquiries. Inquiries include, for example, but are not limited to, product usage, troubleshooting, and support requests. The reception unit accepts, for example, online inquiries. The reception unit can also accept telephone inquiries. The reception unit can also accept face-to-face inquiries. The response unit responds based on the inquiries accepted by the reception unit. Responses include, for example, but are not limited to, explanations on product usage, assistance with troubleshooting, and responses to support requests. The response unit explains, for example, how to use the product. The response unit can also assist with troubleshooting. The response unit can also respond to support requests.As a result, the housing loan support system according to the embodiment can efficiently collect, analyze, and provide customer information, generate advice, and respond to inquiries.

[0030] The collection unit can analyze the customer's past behavioral history and select the optimal information collection method. For example, the collection unit allows the generation AI to select the optimal method based on the information collection methods used by the customer in the past. The collection unit can also allow the generation AI to suggest the most efficient information collection method based on the customer's past behavioral history. The collection unit can also analyze the customer's past behavioral patterns and allow the generation AI to determine the optimal timing for information collection. This enables efficient information collection by selecting the optimal information collection method based on the customer's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's past behavioral data into the generation AI and have the generation AI select the optimal information collection method.

[0031] When collecting information, the collection unit can filter the information based on the customer's current living situation and areas of interest. For example, the collection unit takes into account the customer's current living situation and allows the generation AI to collect only relevant information. The collection unit can also allow the generation AI to filter information based on the customer's areas of interest. The collection unit can also allow the generation AI to collect optimal information by combining the customer's living situation and areas of interest. This makes it possible to collect highly relevant information by filtering information based on the customer's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's living situation data into the generation AI and have the generation AI perform information filtering.

[0032] When collecting information, the collection unit can select the optimal collection means depending on the customer's input method. For example, when the customer uses voice input, the collection unit can have the generation AI collect information using voice recognition technology. Furthermore, when the customer uses text input, the collection unit can have the generation AI collect information using text analysis technology. Furthermore, when the customer uses image input, the collection unit can have the generation AI collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's voice data into the generation AI and have the generation AI convert the voice data into text data.

[0033] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, the collection unit allows the generation AI to collect relevant information based on the customer's current location. The collection unit can also allow the generation AI to provide optimal information by taking into account the customer's geographical location information. The collection unit can also allow the generation AI to collect region-specific information based on the customer's location information. This allows highly relevant information to be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input customer location data into the generation AI and cause the generation AI to collect highly relevant information.

[0034] When collecting information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit can analyze the customer's social media posts, and the generation AI can collect related information. The collection unit can also have the generation AI collect information based on the activities of the customer's friends on social media. The collection unit can also have the generation AI collect related information based on the customer's social media check-in information. In this way, by analyzing the customer's social media activities, related information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the customer's social media data into the generation AI and have the generation AI collect related information.

[0035] The collection unit can customize the collection method by reflecting past customer feedback when collecting information. For example, the collection unit causes the generation AI to adjust the information collection method based on past customer feedback. The collection unit can also reflect customer feedback and cause the generation AI to select the optimal information collection method. The collection unit can also analyze past customer feedback and cause the generation AI to improve the accuracy of information collection. In this way, the collection method can be optimized by reflecting past customer feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input customer feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit allows the generation AI to perform a detailed analysis of information with high importance. The analysis unit can also allow the generation AI to perform a concise analysis of information with low importance. The analysis unit can also allow the generation AI to adjust the depth of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit allows the generation AI to apply a specific analysis algorithm to financial information. The analysis unit can also allow the generation AI to apply a different analysis algorithm to customer behavior history. The analysis unit can also allow the generation AI to select the optimal analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the optimal analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on the customer's past analysis results. The analysis unit can also allow the generation AI to select the optimal analysis method by referring to the customer's past analysis results. The analysis unit can also analyze the customer's past analysis results and allow the generation AI to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit allows the generation AI to prioritize analysis of the most recent information. The analysis unit can also allow the generation AI to postpone analysis of information that was submitted earlier. The analysis unit can also allow the generation AI to determine the order of analysis based on the time of submission of information. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information. The analysis unit can also allow the generation AI to postpone analysis of less relevant information. The analysis unit can also allow the generation AI to determine the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. For example, if the customer has technical expertise, the analysis unit can have the generation AI use a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the analysis unit can also have the generation AI provide analysis results in simpler terms. Furthermore, the analysis unit can also adjust the use of technical terminology in the analysis according to the customer's level of expertise. This allows for the provision of analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the customer's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input customer's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0042] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit allows the generation AI to provide detailed information for information with high importance. The providing unit can also allow the generation AI to provide concise information for information with low importance. The providing unit can also allow the generation AI to adjust the level of detail of the information provided based on the importance of the information. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the information provided.

[0043] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit allows the generation AI to apply a specific provision algorithm to financial information. The providing unit can also allow the generation AI to apply different provision algorithms to customer behavioral history. The providing unit can also allow the generation AI to select the optimal provision algorithm depending on the category of information. This improves the accuracy of information provision by applying the optimal provision algorithm depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information category data to the generation AI and cause the generation AI to apply the optimal provision algorithm.

[0044] The providing unit can improve the accuracy of provision by referring to the customer's past provision results when providing data. For example, the providing unit causes the generation AI to improve the accuracy of provision based on the customer's past provision results. The providing unit can also cause the generation AI to select the optimal provision method by referring to the customer's past provision results. The providing unit can also analyze the customer's past provision results and cause the generation AI to improve the accuracy of provision. In this way, the accuracy of provision is improved by referring to the customer's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the customer's past provision data into the generation AI and cause the generation AI to improve the accuracy of provision.

[0045] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit allows the generation AI to provide the most recent information preferentially. The providing unit can also allow the generation AI to provide information that was submitted earlier later. The providing unit can also allow the generation AI to determine the order of provision based on the time of submission of information. This enables efficient information provision by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information submission time data to the generation AI and have the generation AI determine the priority of provision.

[0046] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can have the generation AI provide highly relevant information preferentially. The providing unit can also have the generation AI provide less relevant information later. The providing unit can also have the generation AI determine the order of provision based on the relevance of the information. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of provision.

[0047] The providing unit can adjust the use of technical terminology provided during provision according to the customer's level of expertise. For example, if the customer has technical expertise, the providing unit can have the generation AI use a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the providing unit can also have the generation AI provide information in simpler terms. Furthermore, the providing unit can adjust the use of technical terminology provided by the generation AI according to the customer's level of expertise. This allows for the provision of more understandable information by adjusting the use of technical terminology provided according to the customer's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input customer's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0048] The generation unit can adjust the level of detail of the generated advice based on the importance of the advice when generating it. For example, the generation unit causes the generation AI to provide detailed advice for advice with a high level of importance. The generation unit can also cause the generation AI to provide concise advice for advice with a low level of importance. The generation unit can also cause the generation AI to adjust the level of detail of the generated advice based on the importance of the advice. This makes it possible to provide advice efficiently by adjusting the level of detail of the generated advice based on the importance of the advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input importance data of the advice to the generation AI and cause the generation AI to adjust the level of detail of the generated advice.

[0049] The generation unit can apply different generation algorithms depending on the category of advice when generating the advice. For example, the generation unit causes the generation AI to apply a specific generation algorithm to financial advice. The generation unit can also cause the generation AI to apply different generation algorithms to the customer's behavioral history. The generation unit can also cause the generation AI to select the optimal generation algorithm depending on the category of advice. This improves the accuracy of the advice by applying the optimal generation algorithm depending on the category of advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input advice category data into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0050] During generation, the generation unit can improve the accuracy of generation by referring to the customer's past advice results. For example, the generation unit causes the generation AI to improve the accuracy of generation based on the customer's past advice results. The generation unit can also cause the generation AI to select the optimal generation method by referring to the customer's past advice results. The generation unit can also analyze the customer's past advice results and cause the generation AI to improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the customer's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the customer's past advice data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0051] The generation unit can determine the generation priority based on the time of submission of advice at the time of generation. For example, the generation unit causes the generation AI to generate the most recent advice first. The generation unit can also cause the generation AI to generate advice that was submitted earlier later. The generation unit can also cause the generation AI to determine the order of generation based on the time of submission of advice. This makes it possible to provide advice efficiently by determining the generation priority based on the time of submission of advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the time of submission of advice to the generation AI and cause the generation AI to determine the generation priority.

[0052] The generation unit can adjust the order of generation based on the relevance of the advice when generating it. For example, the generation unit allows the generation AI to generate highly relevant advice with priority. The generation unit can also allow the generation AI to generate less relevant advice later. The generation unit can also allow the generation AI to determine the order of generation based on the relevance of the advice. This makes it possible to provide advice efficiently by adjusting the order of generation based on the relevance of the advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input relevance data of the advice to the generation AI and cause the generation AI to adjust the order of generation.

[0053] During generation, the generation unit can adjust the use of technical terminology in the generated advice according to the customer's level of expertise. For example, if the customer has technical expertise, the generation unit causes the generation AI to use a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the generation unit can also cause the generation AI to provide advice in simple language. Furthermore, the generation unit can also adjust the use of technical terminology in the generated advice according to the customer's level of expertise. This makes it possible to provide advice that is easier to understand by adjusting the use of technical terminology in the generated advice according to the customer's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input customer's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0054] The reception unit can select the optimal reception method by referring to the customer's past inquiry history when receiving a call. For example, the reception unit allows the generation AI to select the optimal reception method based on the customer's past inquiry history. The reception unit can also allow the generation AI to suggest the optimal reception method by referring to the customer's past inquiry history. The reception unit can also analyze the customer's past inquiry history and allow the generation AI to select the optimal reception method. In this way, the optimal reception method can be selected by referring to the customer's past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's inquiry history data into the generation AI and have the generation AI select the optimal reception method.

[0055] The reception unit can customize the reception method based on the customer's current situation at the time of reception. For example, the reception unit takes into account the customer's current situation and the generation AI provides the optimal reception method. The reception unit can also customize the reception method based on the customer's current situation. The reception unit can also analyze the customer's current situation and the generation AI provides the optimal reception method. This enables more appropriate reception by customizing the reception method based on the customer's current situation. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the customer's current situation into the generation AI and have the generation AI customize the reception method.

[0056] The reception unit can select the optimal reception method by taking into account the geographical location information of the customer at the time of reception. For example, the reception unit allows the generation AI to provide the optimal reception method based on the customer's current location. The reception unit can also select the optimal reception method by taking into account the customer's geographical location information. The reception unit can also allow the generation AI to provide a region-specific reception method based on the customer's location information. This makes it possible to select the optimal reception method by taking into account the customer's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the customer's location data into the generation AI and have the generation AI select the optimal reception method.

[0057] The reception unit can analyze the customer's social media activity and suggest a means of reception at the time of reception. For example, the reception unit analyzes the customer's social media posts, and the generation AI suggests the optimal means of reception. The reception unit can also have the generation AI suggest a means of reception based on the activity of the customer's friends on social media. The reception unit can also have the generation AI suggest the optimal means of reception based on the customer's check-in information on social media. In this way, the optimal means of reception can be suggested by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's social media data into the generation AI and have the generation AI suggest a means of reception.

[0058] When responding, the response unit can select the optimal response method by referring to the customer's past inquiry history. In the response unit, for example, the generation AI selects the optimal response method based on the customer's past inquiry history. The response unit can also have the generation AI suggest the optimal response method by referring to the customer's past inquiry history. The response unit can also analyze the customer's past inquiry history and have the generation AI select the optimal response method. In this way, the optimal response method can be selected by referring to the customer's past inquiry history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the customer's inquiry history data into the generation AI and have the generation AI select the optimal response method.

[0059] The response unit can customize the response measures based on the customer's current situation when responding. For example, the response unit takes into account the customer's current situation and the generation AI provides the optimal response measures. The response unit can also customize the response measures based on the customer's current situation. The response unit can also analyze the customer's current situation and the generation AI can provide the optimal response measures. This enables more appropriate response by customizing the response measures based on the customer's current situation. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input data on the customer's current situation into the generation AI and have the generation AI customize the response measures.

[0060] The response unit can improve the response method by reflecting customer feedback when responding. In the response unit, for example, the generation AI improves the response method based on customer feedback. The response unit can also have the generation AI select the optimal response method by referring to customer feedback. The response unit can also analyze customer feedback and have the generation AI improve the accuracy of the response. In this way, the response method can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input customer feedback data into the generation AI and have the generation AI improve the response method.

[0061] When responding, the response unit can select the optimal response method by taking into account the customer's geographical location information. In the response unit, for example, the generation AI provides the optimal response method based on the customer's current location. The response unit can also select the optimal response method by taking into account the customer's geographical location information. The response unit can also provide a region-specific response method based on the customer's location information. In this way, the optimal response method can be selected by taking into account the customer's geographical location information. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input the customer's location data into the generation AI and have the generation AI select the optimal response method.

[0062] When responding, the response unit can analyze the customer's social media activity and suggest a response method. For example, the response unit analyzes the content of the customer's social media posts, and the generation AI suggests the optimal response method. The response unit can also have the generation AI suggest a response method based on the activity of the customer's friends on social media. The response unit can also have the generation AI suggest the optimal response method based on the customer's social media check-in information. In this way, the optimal response method can be suggested by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the customer's social media data into the generation AI and have the generation AI suggest a response method.

[0063] The response unit can customize the response method by reflecting the customer's past feedback when responding. In the response unit, for example, the generation AI customizes the response method based on the customer's past feedback. The response unit can also reflect the customer's feedback and the generation AI can select the optimal response method. The response unit can also analyze the customer's past feedback and the generation AI can improve the accuracy of the response. In this way, the response method can be customized by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input customer feedback data into the generation AI and have the generation AI customize the response method.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The mortgage loan support system can further include a credit score monitoring unit that monitors a customer's credit score in real time. The credit score monitoring unit can track fluctuations in a customer's credit score in real time and issue an alert if an abnormal fluctuation is detected. For example, if a customer's credit score drops suddenly, the credit score monitoring unit can immediately notify the customer and provide advice to identify the cause. In addition, if the credit score improves, the credit score monitoring unit can notify the customer that the loan terms may be improved. Furthermore, the credit score monitoring unit can analyze the customer's credit score history and predict future fluctuations in the credit score. This allows the customer to always understand the status of their credit score and take appropriate measures.

[0066] The collection unit can analyze the customer's past behavioral history and select the optimal information collection method. For example, the generation AI selects the optimal method based on the information collection methods used by the customer in the past. The collection unit can also have the generation AI suggest the most efficient information collection method based on the customer's past behavioral history. The collection unit can also analyze the customer's past behavioral patterns and have the generation AI determine the optimal timing for information collection. This enables efficient information collection by selecting the optimal information collection method based on the customer's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past behavioral data into the generation AI and have the generation AI select the optimal information collection method.

[0067] When collecting information, the collection unit can filter the information based on the customer's current living situation and areas of interest. For example, the generation AI collects only relevant information taking into account the customer's current living situation. The collection unit can also cause the generation AI to filter the information based on the customer's areas of interest. The collection unit can also cause the generation AI to collect optimal information by combining the customer's living situation and areas of interest. This makes it possible to collect highly relevant information by filtering information based on the customer's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's living situation data into the generation AI and have the generation AI perform information filtering.

[0068] When collecting information, the collection unit can select the optimal collection method depending on the customer's input method. For example, if the customer uses voice input, the generation AI can collect information using voice recognition technology. Furthermore, if the customer uses text input, the collection unit can also have the generation AI collect information using text analysis technology. Furthermore, if the customer uses image input, the collection unit can also have the generation AI collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection method depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's voice data into the generation AI and have the generation AI convert the voice data into text data.

[0069] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, the generation AI can collect relevant information based on the customer's current location. The collection unit can also cause the generation AI to provide optimal information by taking into account the customer's geographical location information. The collection unit can also cause the generation AI to collect region-specific information based on the customer's location information. This allows highly relevant information to be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input customer location data into the generation AI and cause the generation AI to collect highly relevant information.

[0070] When collecting information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit can analyze the customer's social media posts and have the generation AI collect related information. The collection unit can also have the generation AI collect information based on the activities of the customer's friends on social media. The collection unit can also have the generation AI collect related information based on the customer's social media check-in information. In this way, by analyzing the customer's social media activities, related information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's social media data into the generation AI and have the generation AI collect related information.

[0071] When collecting information, the collection unit can customize the collection method by reflecting past customer feedback. For example, the generation AI adjusts the information collection method based on past customer feedback. The collection unit can also reflect customer feedback, allowing the generation AI to select the optimal information collection means. The collection unit can also analyze past customer feedback, allowing the generation AI to improve the accuracy of information collection. In this way, the collection method can be optimized by reflecting past customer feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into the generation AI and have the generation AI customize the collection method.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The first collection unit collects customer information. The customer information includes, for example, name, address, purchase history, inquiry history, etc. The first collection unit can collect customer information through questionnaires, as well as collect customer behavior data using sensors and analyze log data to collect customer information. Step 2: The first analysis unit analyzes the information collected by the first collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. This makes it possible to analyze customer information and customer behavior patterns. Step 3: The first providing unit provides a preliminary screening result based on the information analyzed by the first analysis unit. The preliminary screening result may include pass / fail, scoring, risk assessment, etc. The first providing unit provides these results to perform an initial evaluation of the customer. Step 4: The second collection unit collects customer needs. Customer needs include product features, service quality, price range, etc. The second collection unit collects customer needs through questionnaires, sensors, and log data analysis. Step 5: The generator generates advice based on the needs collected by the second collector. The advice includes how to select a product, how to use it, and suggestions for improvement. By generating this advice, the generator makes specific suggestions to the customer. Step 6: The second providing unit provides the advice generated by the generating unit. The second providing unit provides specific support to the customer by providing advice on how to select and use the product and suggestions for improvement. Step 7: The third collection department collects educational information. Educational information includes learning materials, educational programs, training materials, etc. The third collection department collects information through online information, books and papers, and interviews with experts. Step 8: The second analysis unit analyzes the information collected by the third collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. This enables the analysis of educational information. Step 9: The third providing unit provides information based on the information analyzed by the second analyzing unit. The third providing unit provides information about learning materials, educational programs, and training materials. Step 10: The reception department handles customer inquiries. These inquiries include product usage, troubleshooting, and support requests. The reception department handles inquiries online, by phone, and in person. Step 11: The response department responds to the inquiries received by the reception department, including explaining how to use the product, assisting with troubleshooting, and responding to support requests.

[0074] (Example 2) A mortgage support system according to an embodiment of the present invention utilizes a generative AI to collect and analyze customer information and provide preliminary screening results and advice. The mortgage support system allows customers to use the generative AI to resolve questions about loan application eligibility and procedures, and to receive guidance on the necessary documents and procedures. The generative AI is available 24 hours a day, enhancing customer convenience. The mortgage support system also provides customers with mortgage planning and advice. For example, customers can interact with the generative AI to select a repayment plan, obtain interest rate estimates, and obtain information on additional costs and taxes. Furthermore, the mortgage support system educates and provides customers with information about mortgages. For example, customers can learn basic mortgage knowledge, important terminology, and market trends through the generative AI. The generative AI provides customers with relevant news and articles to deepen their understanding. Finally, the mortgage support system accelerates customer service and improves operational efficiency. The generative AI responds immediately to customer inquiries and requests, eliminating the need for human staff intervention. This improves the quality of customer service and reduces costs. This allows the mortgage support system to enhance customer convenience and provide faster and more efficient service. For example, customers can have their questions about loan application eligibility and procedures resolved 24 hours a day. Customers can also receive customized advice through generative AI, deepening their knowledge about home loans. Furthermore, the use of generative AI can improve operational efficiency, reduce costs, and improve the quality of customer service.

[0075] A housing loan support system according to an embodiment includes a first collection unit, a first analysis unit, a first provision unit, a second collection unit, a generation unit, a second provision unit, a third collection unit, a second analysis unit, a third provision unit, a reception unit, and a response unit. The first collection unit collects customer information. The customer information includes, but is not limited to, for example, name, address, purchase history, and inquiry history. The first collection unit collects customer information through, for example, a questionnaire. The first collection unit can also collect customer behavior data using a sensor. The first collection unit can also collect customer information by analyzing log data. The first analysis unit analyzes the information collected by the first collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. The first analysis unit analyzes the customer information using, for example, statistical analysis. The first analysis unit can also analyze customer behavior patterns using a machine learning algorithm. The first analysis unit can also analyze the customer information using data mining technology. The first providing unit provides a provisional screening result based on the information analyzed by the first analyzing unit. The provisional screening result may include, but is not limited to, for example, pass / fail, scoring, risk assessment, etc. The first providing unit may provide, for example, a pass / fail result. The first providing unit may also provide a scoring result. The first providing unit may also provide a risk assessment result. The second collecting unit collects customer needs. The customer needs may include, but are not limited to, for example, product features, service quality, price range, etc. The second collecting unit may collect customer needs through, for example, a questionnaire. The second collecting unit may also collect customer behavior data using a sensor. The second collecting unit may also collect customer needs by analyzing log data. The generating unit generates advice based on the needs collected by the second collecting unit. The advice may include, but is not limited to, for example, how to select a product, how to use a product, and improvement suggestions. The generating unit may generate advice on how to select a product. The generating unit may also generate advice on how to use a product. Furthermore, the generator can also generate advice regarding improvement suggestions.The second providing unit provides the advice generated by the generating unit. The second providing unit provides, for example, advice on how to select a product. The second providing unit can also provide advice on how to use the product. The second providing unit can also provide advice on improvement suggestions. The third collecting unit collects educational information. The educational information includes, for example, but is not limited to, learning materials, educational programs, and training materials. The third collecting unit collects, for example, information on the Internet. The third collecting unit can also collect information from books and papers. The third collecting unit can also collect information through interviews with experts. The second analyzing unit analyzes the information collected by the third collecting unit. The analysis can be performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to these examples. The second analyzing unit analyzes the educational information using, for example, statistical analysis. The second analyzing unit can also analyze the educational information using a machine learning algorithm. The second analyzing unit can also analyze the educational information using data mining technology. The third providing unit provides information based on the information analyzed by the second analyzing unit. The third providing unit provides, for example, information on learning materials. The third providing unit can also provide information on educational programs. The third providing unit can also provide information on training materials. The reception unit accepts customer inquiries. Inquiries include, for example, but are not limited to, product usage, troubleshooting, and support requests. The reception unit accepts, for example, online inquiries. The reception unit can also accept telephone inquiries. The reception unit can also accept face-to-face inquiries. The response unit responds based on the inquiries accepted by the reception unit. Responses include, for example, but are not limited to, explanations on product usage, assistance with troubleshooting, and responses to support requests. The response unit explains, for example, how to use the product. The response unit can also assist with troubleshooting. The response unit can also respond to support requests.As a result, the housing loan support system according to the embodiment can efficiently collect, analyze, and provide customer information, generate advice, and respond to inquiries.

[0076] The collection unit can estimate the customer's emotions and adjust the timing of information collection based on the estimated customer emotions. For example, if the customer is feeling stressed, the collection unit can delay information collection until the generation AI is relaxed. Furthermore, if the customer is relaxed, the collection unit can also cause the generation AI to immediately start information collection. Furthermore, if the customer is in a hurry, the collection unit can also cause the generation AI to quickly collect information. This allows for more appropriate information collection by adjusting the timing of information collection according to the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, with 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 collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions.

[0077] The collection unit can analyze the customer's past behavioral history and select the optimal information collection method. For example, the collection unit allows the generation AI to select the optimal method based on the information collection methods used by the customer in the past. The collection unit can also allow the generation AI to suggest the most efficient information collection method based on the customer's past behavioral history. The collection unit can also analyze the customer's past behavioral patterns and allow the generation AI to determine the optimal timing for information collection. This enables efficient information collection by selecting the optimal information collection method based on the customer's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's past behavioral data into the generation AI and have the generation AI select the optimal information collection method.

[0078] When collecting information, the collection unit can filter the information based on the customer's current living situation and areas of interest. For example, the collection unit takes into account the customer's current living situation and allows the generation AI to collect only relevant information. The collection unit can also allow the generation AI to filter information based on the customer's areas of interest. The collection unit can also allow the generation AI to collect optimal information by combining the customer's living situation and areas of interest. This makes it possible to collect highly relevant information by filtering information based on the customer's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's living situation data into the generation AI and have the generation AI perform information filtering.

[0079] When collecting information, the collection unit can select the optimal collection means depending on the customer's input method. For example, when the customer uses voice input, the collection unit can have the generation AI collect information using voice recognition technology. Furthermore, when the customer uses text input, the collection unit can have the generation AI collect information using text analysis technology. Furthermore, when the customer uses image input, the collection unit can have the generation AI collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's voice data into the generation AI and have the generation AI convert the voice data into text data.

[0080] The collection unit can estimate the customer's emotions and determine the priority of information to be collected based on the estimated customer emotions. For example, if the customer is feeling stressed, the collection unit causes the generation AI to prioritize collecting information of high importance. Furthermore, if the customer is relaxed, the collection unit can also cause the generation AI to collect detailed information. Furthermore, if the customer is in a hurry, the collection unit can prioritize information that the generation AI can collect quickly. Thus, by determining the priority of information according to the customer's emotions, important information can be collected preferentially. 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 collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the customer's emotions.

[0081] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, the collection unit allows the generation AI to collect relevant information based on the customer's current location. The collection unit can also allow the generation AI to provide optimal information by taking into account the customer's geographical location information. The collection unit can also allow the generation AI to collect region-specific information based on the customer's location information. This allows highly relevant information to be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input customer location data into the generation AI and cause the generation AI to collect highly relevant information.

[0082] When collecting information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit can analyze the customer's social media posts, and the generation AI can collect related information. The collection unit can also have the generation AI collect information based on the activities of the customer's friends on social media. The collection unit can also have the generation AI collect related information based on the customer's social media check-in information. In this way, by analyzing the customer's social media activities, related information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the customer's social media data into the generation AI and have the generation AI collect related information.

[0083] The collection unit can customize the collection method by reflecting past customer feedback when collecting information. For example, the collection unit causes the generation AI to adjust the information collection method based on past customer feedback. The collection unit can also reflect customer feedback and cause the generation AI to select the optimal information collection method. The collection unit can also analyze past customer feedback and cause the generation AI to improve the accuracy of information collection. In this way, the collection method can be optimized by reflecting past customer feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input customer feedback data into the generation AI and cause the generation AI to customize the collection method.

[0084] The analysis unit can estimate the customer's emotions and adjust the way the analysis is presented based on the estimated customer's emotions. For example, if the customer is relaxed, the generation AI can provide detailed analysis results. If the customer is stressed, the analysis unit can also provide concise analysis results. If the customer is in a hurry, the generation AI can also provide analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the customer'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI adjust the way the analysis results are presented.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit allows the generation AI to perform a detailed analysis of information with high importance. The analysis unit can also allow the generation AI to perform a concise analysis of information with low importance. The analysis unit can also allow the generation AI to adjust the depth of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit allows the generation AI to apply a specific analysis algorithm to financial information. The analysis unit can also allow the generation AI to apply a different analysis algorithm to customer behavior history. The analysis unit can also allow the generation AI to select the optimal analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and have the generation AI apply the optimal analysis algorithm.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on the customer's past analysis results. The analysis unit can also allow the generation AI to select the optimal analysis method by referring to the customer's past analysis results. The analysis unit can also analyze the customer's past analysis results and allow the generation AI to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0088] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, if the customer is relaxed, the generation AI can provide a detailed analysis. If the customer is in a hurry, the analysis unit can also provide a concise analysis. If the customer is stressed, the generation AI can also provide a concise analysis. This allows for adjusting the length of the analysis according to the customer's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI adjust the length of the analysis result.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit allows the generation AI to prioritize analysis of the most recent information. The analysis unit can also allow the generation AI to postpone analysis of information that was submitted earlier. The analysis unit can also allow the generation AI to determine the order of analysis based on the time of submission of information. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information. The analysis unit can also allow the generation AI to postpone analysis of less relevant information. The analysis unit can also allow the generation AI to determine the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0091] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. For example, if the customer has technical expertise, the analysis unit can have the generation AI use a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the analysis unit can also have the generation AI provide analysis results in simpler terms. Furthermore, the analysis unit can also adjust the use of technical terminology in the analysis according to the customer's level of expertise. This allows for the provision of analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the customer's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input customer's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0092] The providing unit can estimate the customer's emotions and adjust the way the information is presented based on the estimated customer's emotions. For example, if the customer is relaxed, the providing unit can have the generation AI provide detailed information. Furthermore, if the customer is stressed, the providing unit can have the generation AI provide concise information. Furthermore, if the customer is in a hurry, the providing unit can have the generation AI provide information that focuses on the main points. This allows for more appropriate information provision by adjusting the way information is presented according to the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input customer facial expression data into the generation AI and have the generation AI adjust the way the information is presented.

[0093] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit allows the generation AI to provide detailed information for information with high importance. The providing unit can also allow the generation AI to provide concise information for information with low importance. The providing unit can also allow the generation AI to adjust the level of detail of the information provided based on the importance of the information. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the information provided.

[0094] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit allows the generation AI to apply a specific provision algorithm to financial information. The providing unit can also allow the generation AI to apply different provision algorithms to customer behavioral history. The providing unit can also allow the generation AI to select the optimal provision algorithm depending on the category of information. This improves the accuracy of information provision by applying the optimal provision algorithm depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information category data to the generation AI and cause the generation AI to apply the optimal provision algorithm.

[0095] The providing unit can improve the accuracy of provision by referring to the customer's past provision results when providing data. For example, the providing unit causes the generation AI to improve the accuracy of provision based on the customer's past provision results. The providing unit can also cause the generation AI to select the optimal provision method by referring to the customer's past provision results. The providing unit can also analyze the customer's past provision results and cause the generation AI to improve the accuracy of provision. In this way, the accuracy of provision is improved by referring to the customer's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the customer's past provision data into the generation AI and cause the generation AI to improve the accuracy of provision.

[0096] The providing unit can estimate the customer's emotions and adjust the length of the information to be provided based on the estimated customer emotions. For example, if the customer is relaxed, the providing unit can cause the generation AI to provide detailed information. If the customer is in a hurry, the providing unit can also cause the generation AI to provide concise information. If the customer is stressed, the providing unit can also cause the generation AI to provide information that focuses on the main points. This allows the length of information to be adjusted according to the customer's emotions, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input customer facial expression data into the generation AI and have the generation AI adjust the length of the information.

[0097] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit allows the generation AI to provide the most recent information preferentially. The providing unit can also allow the generation AI to provide information that was submitted earlier later. The providing unit can also allow the generation AI to determine the order of provision based on the time of submission of information. This enables efficient information provision by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information submission time data to the generation AI and have the generation AI determine the priority of provision.

[0098] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can have the generation AI provide highly relevant information preferentially. The providing unit can also have the generation AI provide less relevant information later. The providing unit can also have the generation AI determine the order of provision based on the relevance of the information. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of provision.

[0099] The providing unit can adjust the use of technical terminology provided during provision according to the customer's level of expertise. For example, if the customer has technical expertise, the providing unit can have the generation AI use a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the providing unit can also have the generation AI provide information in simpler terms. Furthermore, the providing unit can adjust the use of technical terminology provided by the generation AI according to the customer's level of expertise. This allows for the provision of more understandable information by adjusting the use of technical terminology provided according to the customer's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input customer's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0100] The generation unit can estimate the customer's emotions and adjust the way the advice is expressed based on the estimated customer's emotions. For example, if the customer is relaxed, the generation AI can provide detailed advice. If the customer is stressed, the generation AI can provide concise advice. If the customer is in a hurry, the generation AI can provide advice that focuses on the key points. This allows for more appropriate advice to be provided by adjusting the way the advice is expressed based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input customer facial expression data into the generation AI and have the generation AI adjust the way the advice is expressed.

[0101] The generation unit can adjust the level of detail of the generated advice based on the importance of the advice when generating it. For example, the generation unit causes the generation AI to provide detailed advice for advice with a high level of importance. The generation unit can also cause the generation AI to provide concise advice for advice with a low level of importance. The generation unit can also cause the generation AI to adjust the level of detail of the generated advice based on the importance of the advice. This makes it possible to provide advice efficiently by adjusting the level of detail of the generated advice based on the importance of the advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input importance data of the advice to the generation AI and cause the generation AI to adjust the level of detail of the generated advice.

[0102] The generation unit can apply different generation algorithms depending on the category of advice when generating the advice. For example, the generation unit causes the generation AI to apply a specific generation algorithm to financial advice. The generation unit can also cause the generation AI to apply different generation algorithms to the customer's behavioral history. The generation unit can also cause the generation AI to select the optimal generation algorithm depending on the category of advice. This improves the accuracy of the advice by applying the optimal generation algorithm depending on the category of advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input advice category data into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0103] During generation, the generation unit can improve the accuracy of generation by referring to the customer's past advice results. For example, the generation unit causes the generation AI to improve the accuracy of generation based on the customer's past advice results. The generation unit can also cause the generation AI to select the optimal generation method by referring to the customer's past advice results. The generation unit can also analyze the customer's past advice results and cause the generation AI to improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the customer's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the customer's past advice data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0104] The generation unit can estimate the customer's emotions and adjust the length of the advice to be generated based on the estimated customer emotions. For example, if the customer is relaxed, the generation AI can provide detailed advice. If the customer is in a hurry, the generation unit can also provide concise advice. If the customer is stressed, the generation AI can also provide advice that focuses on the key points. This allows for adjusting the length of advice according to the customer's emotions, making it possible to provide more appropriate advice. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input customer facial expression data into the generation AI and have the generation AI adjust the length of the advice.

[0105] The generation unit can determine the generation priority based on the time of submission of advice at the time of generation. For example, the generation unit causes the generation AI to generate the most recent advice first. The generation unit can also cause the generation AI to generate advice that was submitted earlier later. The generation unit can also cause the generation AI to determine the order of generation based on the time of submission of advice. This makes it possible to provide advice efficiently by determining the generation priority based on the time of submission of advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the time of submission of advice to the generation AI and cause the generation AI to determine the generation priority.

[0106] The generation unit can adjust the order of generation based on the relevance of the advice when generating it. For example, the generation unit allows the generation AI to generate highly relevant advice with priority. The generation unit can also allow the generation AI to generate less relevant advice later. The generation unit can also allow the generation AI to determine the order of generation based on the relevance of the advice. This makes it possible to provide advice efficiently by adjusting the order of generation based on the relevance of the advice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input relevance data of the advice to the generation AI and cause the generation AI to adjust the order of generation.

[0107] During generation, the generation unit can adjust the use of technical terminology in the generated advice according to the customer's level of expertise. For example, if the customer has technical expertise, the generation unit causes the generation AI to use a lot of technical terminology. Furthermore, if the customer does not have technical expertise, the generation unit can also cause the generation AI to provide advice in simple language. Furthermore, the generation unit can also adjust the use of technical terminology in the generated advice according to the customer's level of expertise. This makes it possible to provide advice that is easier to understand by adjusting the use of technical terminology in the generated advice according to the customer's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input customer's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0108] The reception unit can estimate the customer's emotions and adjust the reception method based on the estimated customer emotions. For example, if the customer is stressed, the generation AI can provide a simple interface. If the customer is relaxed, the generation AI can provide detailed input options. If the customer is in a hurry, the generation AI can prioritize voice input. This allows for more appropriate reception by adjusting the reception method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input customer facial expression data into the generation AI and have the generation AI adjust the reception method.

[0109] The reception unit can select the optimal reception method by referring to the customer's past inquiry history when receiving a call. For example, the reception unit allows the generation AI to select the optimal reception method based on the customer's past inquiry history. The reception unit can also allow the generation AI to suggest the optimal reception method by referring to the customer's past inquiry history. The reception unit can also analyze the customer's past inquiry history and allow the generation AI to select the optimal reception method. In this way, the optimal reception method can be selected by referring to the customer's past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's inquiry history data into the generation AI and have the generation AI select the optimal reception method.

[0110] The reception unit can customize the reception method based on the customer's current situation at the time of reception. For example, the reception unit takes into account the customer's current situation and the generation AI provides the optimal reception method. The reception unit can also customize the reception method based on the customer's current situation. The reception unit can also analyze the customer's current situation and the generation AI provides the optimal reception method. This enables more appropriate reception by customizing the reception method based on the customer's current situation. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the customer's current situation into the generation AI and have the generation AI customize the reception method.

[0111] The reception unit can estimate the customer's emotions and determine reception priorities based on the estimated customer emotions. For example, if the customer is stressed, the reception unit can have the generation AI prioritize reception. Furthermore, if the customer is relaxed, the reception unit can have the generation AI perform normal reception. Furthermore, if the customer is in a hurry, the reception unit can have the generation AI perform quick reception. This enables more appropriate reception by determining reception priorities based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input customer facial expression data into the generation AI and have the generation AI determine reception priorities.

[0112] The reception unit can select the optimal reception method by taking into account the geographical location information of the customer at the time of reception. For example, the reception unit allows the generation AI to provide the optimal reception method based on the customer's current location. The reception unit can also select the optimal reception method by taking into account the customer's geographical location information. The reception unit can also allow the generation AI to provide a region-specific reception method based on the customer's location information. This makes it possible to select the optimal reception method by taking into account the customer's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the customer's location data into the generation AI and have the generation AI select the optimal reception method.

[0113] The reception unit can analyze the customer's social media activity and suggest a means of reception at the time of reception. For example, the reception unit analyzes the customer's social media posts, and the generation AI suggests the optimal means of reception. The reception unit can also have the generation AI suggest a means of reception based on the activity of the customer's friends on social media. The reception unit can also have the generation AI suggest the optimal means of reception based on the customer's check-in information on social media. In this way, the optimal means of reception can be suggested by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's social media data into the generation AI and have the generation AI suggest a means of reception.

[0114] The response unit can estimate the customer's emotions and adjust the response method based on the estimated customer emotions. For example, if the customer is stressed, the response unit can have the generation AI respond in gentle words. If the customer is relaxed, the response unit can also have the generation AI provide detailed explanations. If the customer is in a hurry, the response unit can also have the generation AI respond quickly. This allows for more appropriate response by adjusting the response method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the response unit can be performed using AI, for example, or without AI. For example, the response unit can input customer facial expression data into the generation AI and have the generation AI adjust the response method.

[0115] When responding, the response unit can select the optimal response method by referring to the customer's past inquiry history. In the response unit, for example, the generation AI selects the optimal response method based on the customer's past inquiry history. The response unit can also have the generation AI suggest the optimal response method by referring to the customer's past inquiry history. The response unit can also analyze the customer's past inquiry history and have the generation AI select the optimal response method. In this way, the optimal response method can be selected by referring to the customer's past inquiry history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the customer's inquiry history data into the generation AI and have the generation AI select the optimal response method.

[0116] The response unit can customize the response measures based on the customer's current situation when responding. For example, the response unit takes into account the customer's current situation and the generation AI provides the optimal response measures. The response unit can also customize the response measures based on the customer's current situation. The response unit can also analyze the customer's current situation and the generation AI can provide the optimal response measures. This enables more appropriate response by customizing the response measures based on the customer's current situation. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input data on the customer's current situation into the generation AI and have the generation AI customize the response measures.

[0117] The response unit can improve the response method by reflecting customer feedback when responding. In the response unit, for example, the generation AI improves the response method based on customer feedback. The response unit can also have the generation AI select the optimal response method by referring to customer feedback. The response unit can also analyze customer feedback and have the generation AI improve the accuracy of the response. In this way, the response method can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input customer feedback data into the generation AI and have the generation AI improve the response method.

[0118] The response unit can estimate the customer's emotions and determine the priority of responses based on the estimated customer emotions. For example, if the customer is stressed, the response unit can have the generation AI prioritize the response. Furthermore, if the customer is relaxed, the response unit can have the generation AI provide a normal response. Furthermore, if the customer is in a hurry, the response unit can have the generation AI provide a quick response. This enables more appropriate responses by determining the priority of responses based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, with 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 response unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the response unit can input customer facial expression data into the generation AI and have the generation AI determine the priority of responses.

[0119] When responding, the response unit can select the optimal response method by taking into account the customer's geographical location information. In the response unit, for example, the generation AI provides the optimal response method based on the customer's current location. The response unit can also select the optimal response method by taking into account the customer's geographical location information. The response unit can also provide a region-specific response method based on the customer's location information. In this way, the optimal response method can be selected by taking into account the customer's geographical location information. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input the customer's location data into the generation AI and have the generation AI select the optimal response method.

[0120] When responding, the response unit can analyze the customer's social media activity and suggest a response method. For example, the response unit analyzes the content of the customer's social media posts, and the generation AI suggests the optimal response method. The response unit can also have the generation AI suggest a response method based on the activity of the customer's friends on social media. The response unit can also have the generation AI suggest the optimal response method based on the customer's social media check-in information. In this way, the optimal response method can be suggested by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the customer's social media data into the generation AI and have the generation AI suggest a response method.

[0121] The response unit can customize the response method by reflecting the customer's past feedback when responding. In the response unit, for example, the generation AI customizes the response method based on the customer's past feedback. The response unit can also reflect the customer's feedback and the generation AI can select the optimal response method. The response unit can also analyze the customer's past feedback and the generation AI can improve the accuracy of the response. In this way, the response method can be customized by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input customer feedback data into the generation AI and have the generation AI customize the response method. === Hard Collateral 1-1 === Each of the multiple elements, including the first collection unit, first analysis unit, first provision unit, second collection unit, generation unit, second provision unit, third collection unit, second analysis unit, third provision unit, reception unit, response unit, and collection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the first collection unit can collect customer information using the camera 42 and microphone 38B of the smart device 14. The first analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected customer information. The first provision unit provides a provisional screening result via the specific processing unit 290 of the data processing device 12. The second collection unit can collect customer needs using the camera 42 and microphone 38B of the smart device 14. The generation unit generates advice via the specific processing unit 290 of the data processing device 12. The second provision unit provides advice via the control unit 46A of the smart device 14. The third collection unit collects educational information via the specific processing unit 290 of the data processing device 12. The second analysis unit analyzes the educational information using the specific processing unit 290 of the data processing device 12. The third provision unit provides the educational information using the control unit 46A of the smart device 14. The reception unit receives customer inquiries using the control unit 46A of the smart device 14. The response unit responds to the inquiries using the specific processing unit 290 of the data processing device 12. The collection unit estimates the customer's emotions using the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of information collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned first collection unit, first analysis unit, first provision unit, second collection unit, generation unit, second provision unit, third collection unit, second analysis unit, third provision unit, reception unit, response unit, and collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the first collection unit can collect customer information using the camera 42 and microphone 238 of the smart glasses 214. The first analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected customer information. The first provision unit provides a provisional screening result via the specific processing unit 290 of the data processing device 12. The second collection unit can collect customer needs using the camera 42 and microphone 238 of the smart glasses 214. The generation unit generates advice via the specific processing unit 290 of the data processing device 12. The second provision unit provides advice via the control unit 46A of the smart glasses 214. The third collection unit collects educational information via the specific processing unit 290 of the data processing device 12. The second analysis unit analyzes the educational information using the specific processing unit 290 of the data processing device 12. The third provision unit provides the educational information using the control unit 46A of the smart glasses 214. The reception unit receives customer inquiries using the control unit 46A of the smart glasses 214. The response unit responds to the inquiries using the specific processing unit 290 of the data processing device 12. The collection unit estimates the customer's emotions using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the timing of information collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned first collection unit, first analysis unit, first provision unit, second collection unit, generation unit, second provision unit, third collection unit, second analysis unit, third provision unit, reception unit, response unit, and collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the first collection unit can collect customer information using the camera 42 and microphone 238 of the headset type terminal 314. The first analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected customer information. The first provision unit provides a provisional screening result by the specific processing unit 290 of the data processing device 12. The second collection unit can collect customer needs using the camera 42 and microphone 238 of the headset type terminal 314. The generation unit generates advice by the specific processing unit 290 of the data processing device 12. The second provision unit provides advice by the control unit 46A of the headset type terminal 314. The third collection unit collects educational information using the specific processing unit 290 of the data processing device 12. The second analysis unit analyzes the educational information using the specific processing unit 290 of the data processing device 12. The third provision unit provides the educational information using the control unit 46A of the headset type terminal 314. The reception unit receives customer inquiries using the control unit 46A of the headset type terminal 314. The response unit responds to the inquiries using the specific processing unit 290 of the data processing device 12. The collection unit estimates the customer's emotions using the camera 42 and microphone 238 of the headset type terminal 314, and adjusts the timing of information collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned first collection unit, first analysis unit, first provision unit, second collection unit, generation unit, second provision unit, third collection unit, second analysis unit, third provision unit, reception unit, response unit, and collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the first collection unit can collect customer information using the camera 42 and microphone 238 of the robot 414. The first analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected customer information. The first provision unit provides a provisional screening result via the specific processing unit 290 of the data processing device 12. The second collection unit can collect customer needs using the camera 42 and microphone 238 of the robot 414. The generation unit generates advice via the specific processing unit 290 of the data processing device 12. The second provision unit provides advice via the control unit 46A of the robot 414. The third collection unit collects educational information via the specific processing unit 290 of the data processing device 12. The second analysis unit analyzes the educational information using the specific processing unit 290 of the data processing device 12. The third provision unit provides the educational information using the control unit 46A of the robot 414. The reception unit receives customer inquiries using the control unit 46A of the robot 414. The response unit responds to the inquiries using the specific processing unit 290 of the data processing device 12. The collection unit estimates the customer's emotions using the camera 42 and microphone 238 of the robot 414, and adjusts the timing of information collection using the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The mortgage loan support system can further include a credit score monitoring unit that monitors a customer's credit score in real time. The credit score monitoring unit can track fluctuations in a customer's credit score in real time and issue an alert if an abnormal fluctuation is detected. For example, if a customer's credit score drops suddenly, the credit score monitoring unit can immediately notify the customer and provide advice to identify the cause. In addition, if the credit score improves, the credit score monitoring unit can notify the customer that the loan terms may be improved. Furthermore, the credit score monitoring unit can analyze the customer's credit score history and predict future fluctuations in the credit score. This allows the customer to always understand the status of their credit score and take appropriate measures.

[0124] The collection unit can estimate the customer's emotions and customize the content of information collection based on the estimated customer emotions. For example, if the customer is feeling anxious, the collection unit can prioritize collecting information to help the customer relax. If the customer is excited, the collection unit can provide detailed information. Furthermore, if the customer is tired, the collection unit can collect concise, to-the-point information. This makes it possible to provide more appropriate information by customizing the content of information collection according to the customer's emotions.

[0125] The collection unit can analyze the customer's past behavioral history and select the optimal information collection method. For example, the generation AI selects the optimal method based on the information collection methods used by the customer in the past. The collection unit can also have the generation AI suggest the most efficient information collection method based on the customer's past behavioral history. The collection unit can also analyze the customer's past behavioral patterns and have the generation AI determine the optimal timing for information collection. This enables efficient information collection by selecting the optimal information collection method based on the customer's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past behavioral data into the generation AI and have the generation AI select the optimal information collection method.

[0126] When collecting information, the collection unit can filter the information based on the customer's current living situation and areas of interest. For example, the generation AI collects only relevant information taking into account the customer's current living situation. The collection unit can also cause the generation AI to filter the information based on the customer's areas of interest. The collection unit can also cause the generation AI to collect optimal information by combining the customer's living situation and areas of interest. This makes it possible to collect highly relevant information by filtering information based on the customer's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's living situation data into the generation AI and have the generation AI perform information filtering.

[0127] When collecting information, the collection unit can select the optimal collection method depending on the customer's input method. For example, if the customer uses voice input, the generation AI can collect information using voice recognition technology. Furthermore, if the customer uses text input, the collection unit can also have the generation AI collect information using text analysis technology. Furthermore, if the customer uses image input, the collection unit can also have the generation AI collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection method depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's voice data into the generation AI and have the generation AI convert the voice data into text data.

[0128] The collection unit can estimate the customer's emotions and prioritize the information to be collected based on the estimated customer emotions. For example, if the customer is stressed, the generation AI prioritizes collecting information of high importance. Furthermore, if the customer is relaxed, the collection unit can also allow the generation AI to collect detailed information. Furthermore, if the customer is in a hurry, the collection unit can prioritize information that the generation AI can collect quickly. Thus, by prioritizing information according to the customer's emotions, important information can be collected preferentially. 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 collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input customer facial expression data into the generation AI and have the generation AI estimate the customer's emotions.

[0129] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, the generation AI can collect relevant information based on the customer's current location. The collection unit can also cause the generation AI to provide optimal information by taking into account the customer's geographical location information. The collection unit can also cause the generation AI to collect region-specific information based on the customer's location information. This allows highly relevant information to be collected preferentially by taking into account the customer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input customer location data into the generation AI and cause the generation AI to collect highly relevant information.

[0130] When collecting information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit can analyze the customer's social media posts and have the generation AI collect related information. The collection unit can also have the generation AI collect information based on the activities of the customer's friends on social media. The collection unit can also have the generation AI collect related information based on the customer's social media check-in information. In this way, by analyzing the customer's social media activities, related information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's social media data into the generation AI and have the generation AI collect related information.

[0131] When collecting information, the collection unit can customize the collection method by reflecting past customer feedback. For example, the generation AI adjusts the information collection method based on past customer feedback. The collection unit can also reflect customer feedback, allowing the generation AI to select the optimal information collection means. The collection unit can also analyze past customer feedback, allowing the generation AI to improve the accuracy of information collection. In this way, the collection method can be optimized by reflecting past customer feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input customer feedback data into the generation AI and have the generation AI customize the collection method.

[0132] The analysis unit can estimate the customer's emotions and adjust the way the analysis is presented based on the estimated customer emotions. For example, if the customer is relaxed, the generation AI can provide detailed analysis results. Furthermore, if the customer is stressed, the analysis unit can provide concise analysis results. Furthermore, if the customer is in a hurry, the generation AI can provide analysis results that focus on the main points. By adjusting the way the analysis is presented based on the customer's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI adjust the way the analysis results are presented.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The first collection unit collects customer information. The customer information includes, for example, name, address, purchase history, inquiry history, etc. The first collection unit can collect customer information through questionnaires, as well as collect customer behavior data using sensors and analyze log data to collect customer information. Step 2: The first analysis unit analyzes the information collected by the first collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. This makes it possible to analyze customer information and customer behavior patterns. Step 3: The first providing unit provides a preliminary screening result based on the information analyzed by the first analysis unit. The preliminary screening result may include pass / fail, scoring, risk assessment, etc. The first providing unit provides these results to perform an initial evaluation of the customer. Step 4: The second collection unit collects customer needs. Customer needs include product features, service quality, price range, etc. The second collection unit collects customer needs through questionnaires, sensors, and log data analysis. Step 5: The generator generates advice based on the needs collected by the second collector. The advice includes how to select a product, how to use it, and suggestions for improvement. By generating this advice, the generator makes specific suggestions to the customer. Step 6: The second providing unit provides the advice generated by the generating unit. The second providing unit provides specific support to the customer by providing advice on how to select and use the product and suggestions for improvement. Step 7: The third collection department collects educational information. Educational information includes learning materials, educational programs, training materials, etc. The third collection department collects information through online information, books and papers, and interviews with experts. Step 8: The second analysis unit analyzes the information collected by the third collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. This enables the analysis of educational information. Step 9: The third providing unit provides information based on the information analyzed by the second analyzing unit. The third providing unit provides information about learning materials, educational programs, and training materials. Step 10: The reception department handles customer inquiries. These inquiries include product usage, troubleshooting, and support requests. The reception department handles inquiries online, by phone, and in person. Step 11: The response department responds to the inquiries received by the reception department, including explaining how to use the product, assisting with troubleshooting, and responding to support requests.

[0135] 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.

[0136] 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.

[0137] 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.

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

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0142] The 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.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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 AI 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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 AI 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 system comprising: a first collection unit that collects customer information; a first analysis unit that analyzes the information collected by the first collection unit; a first provision unit that provides a preliminary screening result based on the information analyzed by the first analysis unit; a second collection unit that collects customer needs; a generation unit that generates advice based on the needs collected by the second collection unit; a second provision unit that provides the advice generated by the generation unit; a third collection unit that collects educational information; a second analysis unit that analyzes the information collected by the third collection unit; a third provision unit that provides information based on the information analyzed by the second analysis unit; a reception unit that accepts customer inquiries; and a response unit that responds based on the inquiries accepted by the reception unit.

2. The collecting unit Estimate customer sentiment and adjust the timing of information gathering based on the estimated sentiment 2. The system of claim 1.

3. 2. The system according to claim 1, wherein the collection unit analyzes the customer's past behavior history and selects the most appropriate information collection method.

4. The system according to claim 1 , wherein the collection unit performs filtering based on the customer's current living situation and areas of interest when collecting information.

5. 2. The system according to claim 1, wherein the collection unit selects the most appropriate collection means depending on the input method of the customer when collecting information.

6. The collecting unit Estimate customer sentiment and prioritize the information to be collected based on the estimated sentiment 2. The system of claim 1.

7. The system according to claim 1 , wherein the collection unit, when collecting information, prioritizes collection of highly relevant information based on geographical location information of the customer.

8. The collecting unit When collecting information, analyze your social media activity and collect relevant information 2. The system of claim 1.

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