System

Abstract

To improve efficiency of a response for confirmation or a query of contract content from a customer using a system according to an embodiment.SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; an access unit; and a provision unit. The reception unit receives a query from a customer. The analysis unit analyzes the query received by the reception unit, and acquires contract information. The access unit accesses to an inquiry system of each contract service to acquire the contract information acquired by the analysis unit. The provision unit provides the information acquired by the access unit to the customer.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of confirming contract details and responding to inquiries from customers was cumbersome, resulting in reduced sales efficiency.

[0005] The system according to the embodiment aims to improve the efficiency of confirming contract details and responding to inquiries from customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an access unit, and a provision unit. The reception unit receives questions from customers. The analysis unit analyzes the questions received by the reception unit and acquires contract information. The access unit accesses inquiry systems for each contract service for the contract information acquired by the analysis unit. The provision unit provides the information acquired by the access unit to customers. Effect of the Invention

[0007] The system according to the embodiment can efficiently confirm contract details and respond to inquiries from customers. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The corporate direct sales support system according to the embodiment of the present invention is a system for streamlining "confirmation and inquiries about existing contract contents" and "response to additions and changes to existing contracts" from customers. This system is a mechanism for accepting questions from customers, analyzing the questions, acquiring contract information, accessing the inquiry system of each contract service to acquire the acquired contract information, and providing the acquired information to the customer. For example, a question such as "Please tell me the P# of SmartVPN of ●● Co., Ltd." is accepted from a customer. This question is analyzed using AI. Next, contract information is acquired based on the analyzed question. AI analyzes the content of the question and identifies appropriate contract information. For example, it accesses the inquiry system of the contract service to acquire the corresponding contract information. The acquired contract information is acquired by accessing the inquiry system of each contract service. For example, the corresponding inquiry system is accessed to acquire the contract information of SmartVPN. Finally, the acquired information is provided to the customer. AI analyzes the acquired information and converts it into an appropriate format for providing to the customer. For example, the contract information is provided in text format. This mechanism improves sales efficiency and enables quick and accurate customer service. For example, since a response to a customer's question can be obtained immediately, customer satisfaction is improved. Also, sales staff can efficiently handle customers in their daily work. Furthermore, by providing an authentication unit for security and authentication, the safety of acquired information is ensured. For example, only information authenticated by the authentication unit is provided to the customer, reducing the risk of information leakage. Also, by estimating the customer's emotions and adjusting the method of accepting questions and priorities based on the estimated emotions, more appropriate customer service is possible. For example, if a customer is dissatisfied, customer satisfaction can be improved by prioritizing the handling of that question. Thus, the present invention is a system for streamlining customer service in corporate direct sales and improving sales efficiency. As a result, the corporate direct sales support system can efficiently accept and analyze questions from customers, acquire contract information, and provide it.

[0029] The corporate direct sales support system according to the embodiment includes a reception unit, an analysis unit, an access unit, and a provision unit. The reception unit receives questions from customers. Questions from customers include, for example, text format, voice format, image format, and the like, but are not limited to these examples. The reception unit, for example, analyzes questions in text format using natural language processing technology. The reception unit can also convert voice format questions into text format using voice recognition technology and analyze them. Furthermore, the reception unit can also analyze questions in image format using image recognition technology. The analysis unit analyzes the questions received by the reception unit and acquires contract information. The analysis unit, for example, analyzes the content of the question using natural language processing technology and identifies appropriate contract information. The analysis unit can also analyze past question history and contract information using data mining technology and identify appropriate contract information. Furthermore, the analysis unit can analyze the content of the question using a machine learning algorithm and identify appropriate contract information. The access unit accesses the inquiry system of each contract service to acquire information from the contract information acquired by the analysis unit. The access unit, for example, accesses the inquiry system of the contract service using an API. The access unit can also directly access the database to acquire the contract information. Furthermore, the access unit can also acquire the contract information through a web interface. The provision unit provides the information acquired by the access unit to the customer. The provision unit provides the information through a web interface, for example. Furthermore, the provision unit can also provide the information by email. Furthermore, the provision unit can also provide the information through a chatbot. This allows the corporate direct sales support system according to the embodiment to efficiently accept and analyze questions from customers, acquire and provide contract information. For example, the reception unit accepts questions from customers, and the analysis unit analyzes the content of the questions and identifies appropriate contract information. The access unit accesses a contract service inquiry system to acquire contract information, and the provision unit provides the acquired information to the customer. This allows quick and accurate responses to questions from customers.Furthermore, some or all of the above-mentioned processing in each element of the reception unit, analysis unit, access unit, and provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs a question from a customer into the AI, which analyzes the content of the question. The analysis unit identifies contract information based on the results of the analysis by the AI. The access unit accesses the inquiry system for the contract service based on the contract information identified by the AI. The provision unit provides the customer with information based on the information acquired by the AI. In this way, the use of AI makes it possible to provide more efficient and accurate customer service.

[0030] The reception unit receives questions from customers. Questions from customers include, but are not limited to, for example, text format, voice format, image format, and the like. The reception unit, for example, analyzes questions in text format using natural language processing technology. Specifically, the natural language processing technology is used to perform grammatical analysis and semantic analysis of the text to accurately grasp the intent of the question. For example, if a customer asks, "Please tell me how to change the contents of the contract," the natural language processing technology is used to extract keywords such as "contract contents" and "change method," and the intent of the question is analyzed. The reception unit can also convert questions in voice format into text format using voice recognition technology and analyze the questions. Voice recognition technology analyzes voice data, recognizes phonemes and words, and converts them into text. For example, if a customer asks by voice, "Please tell me about contract renewal," the voice data is converted into text using voice recognition technology, and the intent of the question is analyzed using natural language processing technology. The reception unit can also analyze questions in image format using image recognition technology. Image recognition technology analyzes image data and recognizes text and objects in the image. For example, if a customer sends an image of a contract and asks, "Please confirm the contents of this contract," image recognition technology is used to extract the text in the contract, and natural language processing technology is used to analyze the intent of the question. This allows the reception department to efficiently receive and analyze various types of questions from customers. Furthermore, the reception department can also use AI to determine the priority of questions and prioritize questions with high urgency. For example, AI analyzes the content of the question, immediately responds to questions with high urgency, and responds later to questions with low urgency. This allows the reception department to quickly and efficiently respond to questions from customers.

[0031] The analysis unit analyzes the question received by the reception unit and acquires the contract information. The analysis unit, for example, uses natural language processing technology to analyze the content of the question and identifies appropriate contract information. Specifically, the analysis unit uses natural language processing technology to analyze the intent of the question and search for related contract information from a database. For example, if a customer asks "Please tell me how to cancel the contract," natural language processing technology is used to extract keywords such as "contract" and "cancellation method" and identifies related contract information. The analysis unit can also use data mining technology to analyze past question history and contract information and identify appropriate contract information. Data mining technology is a technology for extracting useful information from a large amount of data, and by analyzing past question history and contract information, it identifies the optimal answer to the customer's question. For example, if a similar question has been asked in the past, the answer to that question is used as a reference to identify appropriate contract information. Furthermore, the analysis unit can also use a machine learning algorithm to analyze the content of the question and identify appropriate contract information. Machine learning algorithms are a technology for learning past data and making predictions and classifications for new data, and analyze the content of the customer's question to identify the optimal contract information. For example, if a customer asks "Please tell me about contract renewal," the machine learning algorithm is used to analyze the content of the question and identify the relevant contract information. This allows the analysis unit to quickly and accurately analyze the questions received by the reception unit and obtain the appropriate contract information. Furthermore, the analysis unit can also use AI to analyze trends in the content of questions and predict future question trends. For example, if AI analyzes past question data and discovers a tendency for many questions to be concentrated at a certain period, it can prepare an appropriate response for that period. This allows the analysis unit to not only quickly and accurately respond to questions from customers, but also predict future question trends and respond efficiently.

[0032] The access unit accesses the inquiry system of each contract service to acquire the contract information acquired by the analysis unit. The access unit accesses the inquiry system of the contract service using, for example, an API. An API (Application Programming Interface) is an interface for exchanging data and functions between different software systems, and the access unit accesses the inquiry system of the contract service using the API to acquire the necessary contract information. For example, when a customer asks, "Please tell me the contract renewal status," the access unit accesses the inquiry system of the contract service using the API to acquire the contract renewal status of the customer. The access unit can also directly access a database to acquire the contract information. A database is a system for storing contract information, and the access unit directly accesses the database to acquire the necessary contract information. For example, when a customer asks, "Please tell me the details of the contract," the access unit accesses the database to acquire the contract details of the customer. Furthermore, the access unit can also acquire the contract information through a web interface. A web interface is an interface for exchanging information through a web browser, and the access unit accesses the inquiry system of the contract service using the web interface to acquire the necessary contract information. For example, when a customer asks, "Please tell me my contract history," the access department accesses the contract service inquiry system through a web interface and acquires the customer's contract history information. This allows the access department to quickly and accurately acquire the contract information acquired by the analysis department and provide it to the provision department. Furthermore, the access department can also use AI to optimize access efficiency. For example, AI analyzes access frequency and access time and identifies the optimal access timing, thereby improving access efficiency. This allows the access department to quickly and efficiently acquire contract information and improve the performance of the entire system.

[0033] The providing unit provides the information acquired by the access unit to the customer. The providing unit provides the information through, for example, a web interface. The web interface is an interface that allows the customer to access information through a web browser, and the providing unit uses the web interface to provide the contract information to the customer. For example, when a customer requests "I want to check the details of the contract," the providing unit displays the contract details information through the web interface. The providing unit can also provide information by email. Email is a means for quickly transmitting information to a customer, and the providing unit uses email to provide the contract information to the customer. For example, when a customer requests "Please tell me the contract renewal status," the providing unit transmits the contract renewal status by email. Furthermore, the providing unit can also provide information through a chatbot. A chatbot is a system for exchanging information with a customer in an interactive format, and the providing unit uses the chatbot to provide the contract information to the customer. For example, when a customer asks the chatbot, "Please tell me the history of the contract," the providing unit provides the contract history information through the chatbot. This allows the providing unit to provide information to the customer through a variety of means, thereby improving customer convenience. Furthermore, the providing unit can also use AI to improve the accuracy and efficiency of information provision. For example, AI can analyze a customer's past question history and behavioral patterns to identify the optimal way to provide information, thereby providing more appropriate information to the customer. This allows the information department to provide information to customers quickly and accurately, improving customer satisfaction.

[0034] The access unit further includes an authentication unit for security or authentication of the acquired information, and the providing unit can provide the information authenticated by the authentication unit to the customer. The authentication unit, for example, uses encryption technology to ensure the security of the acquired information. For example, the authentication unit encrypts the acquired information to prevent leakage of the information. The authentication unit can also perform access control to allow only authenticated users to access the information. For example, the authentication unit performs access control based on the authentication information of the user to allow only authenticated users to access the information. The authentication unit can also ensure the security of the information using an authentication protocol. For example, the authentication unit ensures the security of the information using an authentication protocol such as password authentication, two-factor authentication, or biometric authentication. This ensures the safety of the acquired information. The providing unit provides the information authenticated by the authentication unit to the customer. The providing unit, for example, provides the information authenticated by the authentication unit through a web interface. The providing unit can also provide the information authenticated by the authentication unit by email. The providing unit can also provide the information authenticated by the authentication unit through a chatbot. This allows the providing unit to provide only the information authenticated by the authentication unit to the customer. This ensures the security of the acquired information. Some or all of the above-mentioned processing in the authentication unit may be performed, for example, using AI, or may be performed without using AI. For example, the authentication unit inputs the acquired information into AI, which performs processing to ensure the security of the information. The provision unit provides the information to the customer based on the information authenticated by AI. This makes it possible to provide information more efficiently and safely by using AI.

[0035] The reception unit can analyze the customer's past question history and select an appropriate reception method. The reception unit, for example, analyzes the customer's past question history using data mining technology. For example, the reception unit analyzes the past question content, frequency of questions, type of questions, and the like, and selects an appropriate reception method. The reception unit can also analyze the customer's past question history using statistical analysis technology. For example, the reception unit suggests an optimal reception method for a specific time period from the past question history. Furthermore, the reception unit can analyze the customer's past question history using a machine learning algorithm. For example, the reception unit preferentially provides a reception channel (telephone, email, chat, etc.) that the customer prefers to use from the past question history. This allows the reception unit to provide an optimal reception method based on the customer's past question history. For example, the reception unit preferentially suggests a question format that the customer has frequently used in the past. Also, the reception unit preferentially provides a reception channel that the customer has preferred in the past. Furthermore, the reception unit suggests an optimal reception method for a specific time period from the customer's past question history. This allows the reception unit to provide an optimal reception method based on the customer's past question history. 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 inputs the customer's past question history into AI, which selects the optimal reception method. By using AI, it becomes possible to select a more efficient and accurate reception method.

[0036] The reception unit can filter based on the customer's current contract status and field of interest when receiving a question. The reception unit, for example, acquires the customer's current contract status from a database and filters when receiving a question. For example, the reception unit accepts only related questions based on the expiration date of the customer's contract, the type of contract, the conditions of the contract, and the like. The reception unit can also analyze the customer's field of interest from past behavior history and filter when receiving a question. For example, the reception unit preferentially accepts questions with high relevance based on topics of interest of the customer and past behavior history. Furthermore, the reception unit can filter specific questions based on the contents of the customer's contract and assign them to an appropriate person in charge. For example, the reception unit filters specific questions based on the contents of the customer's contract and assigns them to an appropriate person in charge. This allows the reception unit to preferentially accept questions related to the customer's current contract status and field of interest. For example, the reception unit accepts only related questions based on the customer's current contract status. Also, the reception unit preferentially accepts questions with high relevance based on the customer's field of interest. Furthermore, the reception unit filters specific questions based on the contents of the customer's contract and assigns them to an appropriate person in charge. This makes it possible to preferentially accept questions related to the customer's contract status and areas of interest. 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 inputs the customer's contract status and areas of interest into AI, which then performs filtering. This makes it possible to perform filtering more efficiently and accurately by using AI.

[0037] The reception unit can preferentially receive questions with high relevance in consideration of the geographical location information of the customer when receiving a question. The reception unit, for example, acquires the geographical location information of the customer from GPS data, and preferentially receives questions with high relevance when receiving a question. For example, when the customer is in a specific area, the reception unit preferentially receives questions related to the area. The reception unit can also preferentially process the most relevant question based on the customer's current location. Furthermore, the reception unit can also preferentially receive questions regarding problems specific to the area based on the geographical location information of the customer. For example, the reception unit preferentially receives questions regarding problems specific to the area based on the geographical location information of the customer. This allows the reception unit to preferentially receive questions with high relevance based on the geographical location information of the customer. For example, when the customer is in a specific area, the reception unit preferentially receives questions related to the area. The reception unit also preferentially processes the most relevant question based on the customer's current location. The reception unit also preferentially receives questions regarding problems specific to the area based on the geographical location information of the customer. This allows questions with high relevance to be preferentially accepted based on the geographical location information of the customer. Some or all of the above-mentioned processing in the acceptance unit may be performed, for example, using AI, or may be performed without using AI. For example, the acceptance unit inputs the geographical location information of the customer into AI, and the AI ​​preferentially accepts questions with high relevance. This allows questions to be accepted more efficiently and accurately by using AI.

[0038] The reception unit can analyze the social media activity of the customer when receiving a question, and receive a related question. The reception unit, for example, analyzes the social media activity of the customer using a data mining technique. For example, the reception unit analyzes the content of the customer's posts, the number of likes, the number of followers, and the like, and receives a related question. The reception unit can also preferentially receive questions related to recent interests from the social media activity of the customer. For example, the reception unit receives related questions based on issues the customer mentioned on social media. The reception unit can also analyze the social media activity of the customer and preferentially process the most relevant question. For example, the reception unit analyzes the social media activity of the customer and preferentially process the most relevant question. This allows the reception unit to receive a related question based on the social media activity of the customer. For example, the reception unit preferentially receives questions related to recent interests from the social media activity of the customer. The reception unit also receives related questions based on issues the customer mentioned on social media. The reception unit can also analyze the social media activity of the customer and preferentially process the most relevant question. This allows the reception unit to receive a related question based on the social media activity of the customer. 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 inputs the customer's social media activity into AI, which then receives related questions. By using AI, this allows for more efficient and accurate question reception.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during the analysis. The analysis unit, for example, evaluates the importance of the question, and performs a detailed analysis on a question with a high importance. For example, the analysis unit evaluates the importance based on the urgency of the question, the impact of the question, the frequency of the question, and the like. The analysis unit can also perform a concise analysis on a question with a low importance. For example, the analysis unit adjusts the depth of the analysis according to the importance of the question. As a result, the analysis unit can adjust the level of detail of the analysis according to the importance of the question. For example, a detailed analysis is performed on a question with a high importance. Also, a concise analysis is performed on a question with a low importance. Furthermore, the depth of the analysis is adjusted according to the importance of the question. As a result, an appropriate analysis can be performed according to the importance of the question. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the importance of the question to an AI, and the AI ​​adjusts the level of detail of the analysis. As a result, more efficient and accurate analysis can be performed by using an AI.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. The analysis unit, for example, classifies the category of the question and applies a specialized analysis algorithm to technical questions. For example, the analysis unit applies a natural language processing algorithm to technical questions. The analysis unit can also apply a contract analysis algorithm to questions about the contract content. For example, the analysis unit applies a contract analysis algorithm to questions about the contract content. Furthermore, the analysis unit can also apply a general-purpose analysis algorithm to general questions. For example, the analysis unit applies a general-purpose analysis algorithm to general questions. This enables the analysis unit to perform an appropriate analysis according to the category of the question. For example, the analysis unit applies a specialized analysis algorithm to technical questions. Furthermore, the analysis unit applies a contract analysis algorithm to questions about the contract content. Furthermore, the analysis unit applies a general-purpose analysis algorithm to general questions. This enables an appropriate analysis according to the category of the question. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the question category into the AI, which then applies the appropriate analysis algorithm, making the use of AI more efficient and accurate.

[0041] The analysis unit can determine the priority of the analysis based on the time of submission of the question during the analysis. The analysis unit, for example, evaluates the time of submission of the question and prioritizes the analysis of the most recently submitted question. For example, the analysis unit evaluates the time of submission based on the submission date and time, the timing of submission, the frequency of submission, and the like. The analysis unit can also postpone the analysis of questions submitted earlier. For example, the analysis unit adjusts the order of analysis based on the time of submission. This allows the analysis unit to determine the priority of the analysis based on the time of submission of the question. For example, the most recently submitted question is analyzed first. Also, questions submitted earlier are analyzed later. Furthermore, the order of analysis is adjusted based on the time of submission. This allows the appropriate priority of analysis to be determined based on the time of submission of the question. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the time of submission of the question to AI, and AI determines the priority of analysis. This allows more efficient and accurate analysis by using AI.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the questions during analysis. The analysis unit, for example, evaluates the relevance of the questions and prioritizes the analysis of questions with high relevance. For example, the analysis unit evaluates the relevance based on the similarity of the question content, the customer's interest, past question history, etc. Also, the analysis unit can postpone the analysis of questions with low relevance. For example, the analysis unit adjusts the order of analysis based on the relevance. As a result, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, questions with high relevance are analyzed with priority. Also, questions with low relevance are analyzed with postponement. Furthermore, the order of analysis is adjusted based on the relevance. As a result, the appropriate order of analysis can be adjusted based on the relevance of the questions. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the relevance of the questions to AI, and the AI ​​adjusts the order of analysis. As a result, more efficient and accurate analysis is possible by using AI.

[0043] The access unit can adjust the level of detail of the access based on the importance of the contract information at the time of access. The access unit, for example, evaluates the importance of the contract information, and performs detailed access to contract information with high importance. For example, the access unit evaluates the importance based on the amount of the contract, the period of the contract, the impact of the contract, and the like. The access unit can also perform brief access to contract information with low importance. For example, the access unit adjusts the depth of the access according to the importance of the contract information. As a result, the access unit can adjust the level of detail of the access according to the importance of the contract information. For example, detailed access is performed for contract information with high importance. Also, brief access is performed for contract information with low importance. Furthermore, the depth of the access is adjusted according to the importance of the contract information. As a result, appropriate access according to the importance of the contract information is possible. A part or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit inputs the importance of the contract information to AI, and AI adjusts the level of detail of the access. As a result, more efficient and accurate access is possible by using AI.

[0044] The access unit can apply different access algorithms depending on the category of the contract information at the time of access. The access unit, for example, classifies the category of the contract information and applies a specialized access algorithm to technical contract information. For example, the access unit applies a search algorithm to technical contract information. The access unit can also apply a contract access algorithm to information on the contract content. For example, the access unit applies a contract access algorithm to information on the contract content. Furthermore, the access unit can also apply a general-purpose access algorithm to general contract information. For example, the access unit applies a general-purpose access algorithm to general contract information. This enables the access unit to perform appropriate access according to the category of the contract information. For example, the access unit applies a specialized access algorithm to technical contract information. Furthermore, the access unit applies a contract access algorithm to information on the contract content. Furthermore, the access unit applies a general-purpose access algorithm to general contract information. This enables appropriate access according to the category of the contract information. A part or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit inputs the category of the contract information into AI, and the AI ​​applies an appropriate access algorithm. This will enable more efficient and accurate access by using AI.

[0045] The access unit can determine the priority of access based on the submission time of the contract information at the time of access. The access unit, for example, evaluates the submission time of the contract information and preferentially accesses the most recently submitted contract information. For example, the access unit evaluates the submission time based on the submission date and time, the timing of submission, the frequency of submission, and the like. The access unit can also access contract information submitted earlier at a later date. For example, the access unit adjusts the order of access based on the submission time. This allows the access unit to determine the priority of access based on the submission time of the contract information. For example, recently submitted contract information is preferentially accessed. Also, contract information submitted earlier is accessed later. Furthermore, the order of access is adjusted based on the submission time. This allows appropriate access priority to be determined based on the submission time of the contract information. Part or all of the above-mentioned processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit inputs the submission time of the contract information into AI, and AI determines the priority of access. This allows more efficient and accurate access by using AI.

[0046] The access unit can adjust the order of access based on the relevance of the contract information at the time of access. The access unit, for example, evaluates the relevance of the contract information and preferentially accesses contract information with high relevance. For example, the access unit evaluates the relevance based on the similarity of the contract information, the customer's interest, past contract history, etc. In addition, the access unit can access contract information with low relevance later. For example, the access unit adjusts the order of access based on the relevance. As a result, the access unit can adjust the order of access based on the relevance of the contract information. For example, highly relevant contract information is preferentially accessed. In addition, contract information with low relevance is accessed later. Furthermore, the order of access is adjusted based on the relevance. As a result, an appropriate order of access can be adjusted based on the relevance of the contract information. A part or all of the above-mentioned processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit inputs the relevance of the contract information to AI, and AI adjusts the order of access. As a result, more efficient and accurate access is possible by using AI.

[0047] The provision unit can adjust the level of detail of the provision based on the importance of the contract information at the time of provision. The provision unit, for example, evaluates the importance of the contract information, and provides detailed information for contract information with high importance. For example, the provision unit evaluates the importance based on the amount of the contract, the period of the contract, the impact of the contract, and the like. The provision unit can also provide concise information for contract information with low importance. For example, the provision unit adjusts the depth of the provision according to the importance of the contract information. As a result, the provision unit can adjust the level of detail of the provision according to the importance of the contract information. For example, detailed information is provided for contract information with high importance. Also, concise information is provided for contract information with low importance. Furthermore, the depth of the provision is adjusted according to the importance of the contract information. As a result, appropriate provision according to the importance of the contract information is possible. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit inputs the importance of the contract information to AI, and AI adjusts the level of detail of the provision. As a result, more efficient and accurate provision is possible by using AI.

[0048] The providing unit can apply different provision algorithms according to the category of the contract information at the time of provision. The providing unit, for example, classifies the category of the contract information and applies a specialized provision algorithm to technical contract information. For example, the providing unit applies a search algorithm to technical contract information. The providing unit can also apply a contract provision algorithm to information on the contract content. For example, the providing unit applies a contract provision algorithm to information on the contract content. Furthermore, the providing unit can also apply a general-purpose provision algorithm to general contract information. For example, the providing unit applies a general-purpose provision algorithm to general contract information. This enables the providing unit to provide appropriate information according to the category of the contract information. For example, the providing unit applies a specialized provision algorithm to technical contract information. Furthermore, the providing unit applies a contract provision algorithm to information on the contract content. Furthermore, the providing unit applies a general-purpose provision algorithm to general contract information. This enables appropriate information to be provided according to the category of the contract information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision department inputs the category of contract information into the AI, which then applies an appropriate provision algorithm. This makes it possible to provide information more efficiently and accurately using AI.

[0049] The providing unit can determine the priority of provision based on the submission time of the contract information at the time of provision. The providing unit, for example, evaluates the submission time of the contract information and provides the most recently submitted contract information preferentially. For example, the providing unit evaluates the submission time based on the submission date and time, the timing of submission, the frequency of submission, and the like. In addition, the providing unit can provide contract information with an older submission time later. For example, the providing unit adjusts the order of provision based on the submission time. Thereby, the providing unit can determine the priority of provision based on the submission time of the contract information. For example, the most recently submitted contract information is provided preferentially. In addition, the contract information with an older submission time is provided later. Furthermore, the order of provision is adjusted based on the submission time. Thereby, it is possible to determine the appropriate priority of provision based on the submission time of the contract information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the submission time of the contract information into AI, and AI determines the priority of provision. Thereby, more efficient and accurate provision is possible by using AI.

[0050] The providing unit can adjust the order of provision based on the relevance of the contract information at the time of provision. The providing unit, for example, evaluates the relevance of the contract information and provides highly relevant contract information preferentially. For example, the providing unit evaluates the relevance based on the similarity of the contract information, the customer's interest, past contract history, etc. Also, the providing unit can provide less relevant contract information later. For example, the providing unit adjusts the order of provision based on the relevance. As a result, the providing unit can adjust the order of provision based on the relevance of the contract information. For example, highly relevant contract information is preferentially provided. Also, less relevant contract information is provided later. Furthermore, the providing unit adjusts the order of provision based on the relevance. As a result, it is possible to adjust the appropriate order of provision based on the relevance of the contract information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the relevance of the contract information to AI, and AI adjusts the order of provision. As a result, more efficient and accurate provision is possible by using AI.

[0051] The authentication unit can adjust the level of detail of authentication based on the importance of the contract information at the time of authentication. The authentication unit, for example, evaluates the importance of the contract information, and performs detailed authentication for contract information with high importance. For example, the authentication unit evaluates the importance based on the amount of the contract, the period of the contract, the impact of the contract, and the like. The authentication unit can also perform simple authentication for contract information with low importance. For example, the authentication unit adjusts the depth of authentication according to the importance of the contract information. As a result, the authentication unit can adjust the level of detail of authentication according to the importance of the contract information. For example, detailed authentication is performed for contract information with high importance. Also, simple authentication is performed for contract information with low importance. Furthermore, the authentication depth is adjusted according to the importance of the contract information. As a result, appropriate authentication according to the importance of the contract information is possible. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit inputs the importance of the contract information to AI, and AI adjusts the level of detail of authentication. As a result, more efficient and accurate authentication is possible by using AI.

[0052] The authentication unit can determine the priority of authentication based on the submission time of the contract information at the time of authentication. The authentication unit, for example, evaluates the submission time of the contract information and preferentially authenticates the most recently submitted contract information. For example, the authentication unit evaluates the submission time based on the submission date and time, the timing of submission, the frequency of submission, and the like. In addition, the authentication unit can also postpone the authentication of contract information submitted earlier. For example, the authentication unit adjusts the order of authentication based on the submission time. As a result, the authentication unit can determine the priority of authentication based on the submission time of the contract information. For example, the most recently submitted contract information is preferentially authenticated. In addition, the contract information submitted earlier is postponed for authentication. Furthermore, the authentication order is adjusted based on the submission time. As a result, it is possible to determine an appropriate priority of authentication based on the submission time of the contract information. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit inputs the submission time of the contract information to AI, and AI determines the priority of authentication. As a result, more efficient and accurate authentication is possible by using AI.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the customer's past purchase history and preferentially accept related questions. For example, the reception unit preferentially processes related questions based on products and services purchased by the customer in the past. The reception unit can also preferentially accept questions about specific products and services from the customer's purchase history. Furthermore, the reception unit can analyze the customer's purchase history and preferentially process the most relevant questions. This allows the reception unit to preferentially accept questions related to the customer's purchase history. For example, the reception unit preferentially processes related questions based on products and services purchased by the customer in the past. The reception unit also preferentially accepts questions about specific products and services from the customer's purchase history. Furthermore, the reception unit analyzes the customer's purchase history and preferentially processes the most relevant questions. This allows the reception unit to preferentially accept questions related to the customer's purchase history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the customer's purchase history into AI, and the AI ​​preferentially accepts questions related to AI. This will enable the use of AI to handle questions more efficiently and accurately.

[0055] The analysis unit can analyze the customer's past feedback and improve the accuracy of the analysis. For example, the analysis unit adjusts the analysis algorithm based on the feedback provided by the customer in the past. The analysis unit can also extract specific problems from the customer's feedback and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the customer's feedback and identify the most effective analysis method. This allows the analysis unit to improve the accuracy of the analysis based on the customer's feedback. For example, the analysis unit adjusts the analysis algorithm based on the feedback provided by the customer in the past. The analysis unit can also extract specific problems from the customer's feedback and improve the accuracy of the analysis. Furthermore, the analysis unit analyzes the customer's feedback and identifies the most effective analysis method. This allows the analysis unit to improve the accuracy of the analysis based on the customer's feedback. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the customer's feedback into AI, and the AI ​​improves the accuracy of the analysis. This allows more efficient and accurate analysis by using AI.

[0056] The providing unit can analyze the customer's past inquiry history and provide related information preferentially. For example, the providing unit can provide related information preferentially based on the content of the customer's past inquiry. The providing unit can also provide information on a specific topic preferentially from the customer's inquiry history. Furthermore, the providing unit can analyze the customer's inquiry history and provide the most relevant information preferentially. As a result, the providing unit can provide related information preferentially based on the customer's inquiry history. For example, the providing unit can provide related information preferentially based on the content of the customer's past inquiry. The providing unit can also provide information on a specific topic preferentially from the customer's inquiry history. Furthermore, the providing unit can analyze the customer's inquiry history and provide the most relevant information preferentially. As a result, related information can be provided preferentially based on the customer's inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the customer's inquiry history into AI, and the AI ​​provides related information preferentially. As a result, more efficient and accurate information can be provided by using AI.

[0057] The providing unit can analyze the customer's past purchase history and provide related information preferentially. For example, the providing unit can provide related information preferentially based on products and services purchased by the customer in the past. The providing unit can also provide information on a specific product or service preferentially from the customer's purchase history. Furthermore, the providing unit can analyze the customer's purchase history and provide the most relevant information preferentially. As a result, the providing unit can provide related information preferentially based on the customer's purchase history. For example, the providing unit can provide related information preferentially based on products and services purchased by the customer in the past. The providing unit can also provide information on a specific product or service preferentially from the customer's purchase history. Furthermore, the providing unit can analyze the customer's purchase history and provide the most relevant information preferentially. As a result, related information can be provided preferentially based on the customer's purchase history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the customer's purchase history into AI, and the AI ​​provides related information preferentially. As a result, more efficient and accurate information provision is possible by using AI.

[0058] The providing unit can provide relevant information preferentially in consideration of the geographical location information of the customer. For example, when the customer is in a specific area, the providing unit preferentially provides information related to the area. The providing unit can also provide the most relevant information preferentially based on the customer's current location. Furthermore, the providing unit can also provide information related to problems specific to the area preferentially based on the customer's geographical location information. This allows the providing unit to provide relevant information preferentially based on the customer's geographical location information. For example, when the customer is in a specific area, the providing unit preferentially provides information related to the area. Furthermore, the providing unit preferentially provides information related to problems specific to the area based on the customer's geographical location information. This allows the providing unit to provide relevant information preferentially based on the customer's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the customer's geographical location information into AI, and the AI ​​provides relevant information preferentially. This allows more efficient and accurate information provision by using AI.

[0059] The providing unit can analyze the social media activity of the customer and provide related information preferentially. For example, the providing unit analyzes the content of the customer's posts, the number of likes, the number of followers, and the like, and provides the related information. The providing unit can also provide information related to recent interests from the social media activity of the customer preferentially. Furthermore, the providing unit can analyze the social media activity of the customer and provide the most relevant information preferentially. As a result, the providing unit can provide related information based on the social media activity of the customer. For example, the providing unit analyzes the content of the customer's posts, the number of likes, the number of followers, and the like, and provides the related information. The providing unit can also provide information related to recent interests from the social media activity of the customer preferentially. Furthermore, the providing unit analyzes the social media activity of the customer and provides the most relevant information preferentially. As a result, the providing unit can provide related information based on the social media activity of the customer. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the social media activity of the customer into AI, and AI provides related information. As a result, more efficient and accurate information provision is possible by using AI.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit receives questions from customers. Questions from customers may be in text format, voice format, image format, etc. The reception unit can analyze questions in text format using natural language processing technology, and convert voice format questions into text format using voice recognition technology and analyze them. It can also analyze questions in image format using image recognition technology. Step 2: The analysis unit analyzes the question received by the reception unit and obtains the contract information. The analysis unit analyzes the content of the question using natural language processing technology, data mining technology, and machine learning algorithms, and identifies the appropriate contract information. Step 3: The access unit accesses the inquiry system of each contract service to acquire the contract information acquired by the analysis unit. The access unit can access the inquiry system of the contract service using an API, as well as directly access a database or acquire the contract information through a web interface. Step 4: The provision unit provides the information acquired by the access unit to the customer. The provision unit can provide the information through a web interface, email, or chatbot.

[0062] (Example 2) The corporate direct sales support system according to the embodiment of the present invention is a system for streamlining "confirmation and inquiries about existing contract contents" and "response to additions and changes to existing contracts" from customers. This system is a mechanism for accepting questions from customers, analyzing the questions, acquiring contract information, accessing the inquiry system of each contract service to acquire the acquired contract information, and providing the acquired information to the customer. For example, a question such as "Please tell me the P# of SmartVPN of ●● Co., Ltd." is accepted from a customer. This question is analyzed using AI. Next, contract information is acquired based on the analyzed question. AI analyzes the content of the question and identifies appropriate contract information. For example, it accesses the inquiry system of the contract service to acquire the corresponding contract information. The acquired contract information is acquired by accessing the inquiry system of each contract service. For example, the corresponding inquiry system is accessed to acquire the contract information of SmartVPN. Finally, the acquired information is provided to the customer. AI analyzes the acquired information and converts it into an appropriate format for providing to the customer. For example, the contract information is provided in text format. This mechanism improves sales efficiency and enables quick and accurate customer service. For example, since a response to a customer's question can be obtained immediately, customer satisfaction is improved. Also, sales staff can efficiently handle customers in their daily work. Furthermore, by providing an authentication unit for security and authentication, the safety of acquired information is ensured. For example, only information authenticated by the authentication unit is provided to the customer, reducing the risk of information leakage. Also, by estimating the customer's emotions and adjusting the method of accepting questions and priorities based on the estimated emotions, more appropriate customer service is possible. For example, if a customer is dissatisfied, customer satisfaction can be improved by prioritizing the handling of that question. Thus, the present invention is a system for streamlining customer service in corporate direct sales and improving sales efficiency. As a result, the corporate direct sales support system can efficiently accept and analyze questions from customers, acquire contract information, and provide it.

[0063] The corporate direct sales support system according to the embodiment includes a reception unit, an analysis unit, an access unit, and a provision unit. The reception unit receives questions from customers. Questions from customers include, for example, text format, voice format, image format, and the like, but are not limited to these examples. The reception unit, for example, analyzes questions in text format using natural language processing technology. The reception unit can also convert voice format questions into text format using voice recognition technology and analyze them. Furthermore, the reception unit can also analyze questions in image format using image recognition technology. The analysis unit analyzes the questions received by the reception unit and acquires contract information. The analysis unit, for example, analyzes the content of the question using natural language processing technology and identifies appropriate contract information. The analysis unit can also analyze past question history and contract information using data mining technology and identify appropriate contract information. Furthermore, the analysis unit can analyze the content of the question using a machine learning algorithm and identify appropriate contract information. The access unit accesses the inquiry system of each contract service to acquire information from the contract information acquired by the analysis unit. The access unit, for example, accesses the inquiry system of the contract service using an API. The access unit can also directly access the database to acquire the contract information. Furthermore, the access unit can also acquire the contract information through a web interface. The provision unit provides the information acquired by the access unit to the customer. The provision unit provides the information through a web interface, for example. Furthermore, the provision unit can also provide the information by email. Furthermore, the provision unit can also provide the information through a chatbot. This allows the corporate direct sales support system according to the embodiment to efficiently accept and analyze questions from customers, acquire and provide contract information. For example, the reception unit accepts questions from customers, and the analysis unit analyzes the content of the questions and identifies appropriate contract information. The access unit accesses a contract service inquiry system to acquire contract information, and the provision unit provides the acquired information to the customer. This allows quick and accurate responses to questions from customers.Furthermore, some or all of the above-mentioned processing in each element of the reception unit, analysis unit, access unit, and provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs a question from a customer into the AI, which analyzes the content of the question. The analysis unit identifies contract information based on the results of the analysis by the AI. The access unit accesses the inquiry system for the contract service based on the contract information identified by the AI. The provision unit provides the customer with information based on the information acquired by the AI. In this way, the use of AI makes it possible to provide more efficient and accurate customer service.

[0064] The reception unit receives questions from customers. Questions from customers include, but are not limited to, for example, text format, voice format, image format, and the like. The reception unit, for example, analyzes questions in text format using natural language processing technology. Specifically, the natural language processing technology is used to perform grammatical analysis and semantic analysis of the text to accurately grasp the intent of the question. For example, if a customer asks, "Please tell me how to change the contents of the contract," the natural language processing technology is used to extract keywords such as "contract contents" and "change method," and the intent of the question is analyzed. The reception unit can also convert questions in voice format into text format using voice recognition technology and analyze the questions. Voice recognition technology analyzes voice data, recognizes phonemes and words, and converts them into text. For example, if a customer asks by voice, "Please tell me about contract renewal," the voice data is converted into text using voice recognition technology, and the intent of the question is analyzed using natural language processing technology. The reception unit can also analyze questions in image format using image recognition technology. Image recognition technology analyzes image data and recognizes text and objects in the image. For example, if a customer sends an image of a contract and asks, "Please confirm the contents of this contract," image recognition technology is used to extract the text in the contract, and natural language processing technology is used to analyze the intent of the question. This allows the reception department to efficiently receive and analyze various types of questions from customers. Furthermore, the reception department can also use AI to determine the priority of questions and prioritize questions with high urgency. For example, AI analyzes the content of the question, immediately responds to questions with high urgency, and responds later to questions with low urgency. This allows the reception department to quickly and efficiently respond to questions from customers.

[0065] The analysis unit analyzes the question received by the reception unit and acquires the contract information. The analysis unit, for example, uses natural language processing technology to analyze the content of the question and identifies appropriate contract information. Specifically, the analysis unit uses natural language processing technology to analyze the intent of the question and search for related contract information from a database. For example, if a customer asks "Please tell me how to cancel the contract," natural language processing technology is used to extract keywords such as "contract" and "cancellation method" and identifies related contract information. The analysis unit can also use data mining technology to analyze past question history and contract information and identify appropriate contract information. Data mining technology is a technology for extracting useful information from a large amount of data, and by analyzing past question history and contract information, it identifies the optimal answer to the customer's question. For example, if a similar question has been asked in the past, the answer to that question is used as a reference to identify appropriate contract information. Furthermore, the analysis unit can also use a machine learning algorithm to analyze the content of the question and identify appropriate contract information. Machine learning algorithms are a technology for learning past data and making predictions and classifications for new data, and analyze the content of the customer's question to identify the optimal contract information. For example, if a customer asks "Please tell me about contract renewal," the machine learning algorithm is used to analyze the content of the question and identify the relevant contract information. This allows the analysis unit to quickly and accurately analyze the questions received by the reception unit and obtain the appropriate contract information. Furthermore, the analysis unit can also use AI to analyze trends in the content of questions and predict future question trends. For example, if AI analyzes past question data and discovers a tendency for many questions to be concentrated at a certain period, it can prepare an appropriate response for that period. This allows the analysis unit to not only quickly and accurately respond to questions from customers, but also predict future question trends and respond efficiently.

[0066] The access unit accesses the inquiry system of each contract service to acquire the contract information acquired by the analysis unit. The access unit accesses the inquiry system of the contract service using, for example, an API. An API (Application Programming Interface) is an interface for exchanging data and functions between different software systems, and the access unit accesses the inquiry system of the contract service using the API to acquire the necessary contract information. For example, when a customer asks, "Please tell me the contract renewal status," the access unit accesses the inquiry system of the contract service using the API to acquire the contract renewal status of the customer. The access unit can also directly access a database to acquire the contract information. A database is a system for storing contract information, and the access unit directly accesses the database to acquire the necessary contract information. For example, when a customer asks, "Please tell me the details of the contract," the access unit accesses the database to acquire the contract details of the customer. Furthermore, the access unit can also acquire the contract information through a web interface. A web interface is an interface for exchanging information through a web browser, and the access unit accesses the inquiry system of the contract service using the web interface to acquire the necessary contract information. For example, when a customer asks, "Please tell me my contract history," the access department accesses the contract service inquiry system through a web interface and acquires the customer's contract history information. This allows the access department to quickly and accurately acquire the contract information acquired by the analysis department and provide it to the provision department. Furthermore, the access department can also use AI to optimize access efficiency. For example, AI analyzes access frequency and access time and identifies the optimal access timing, thereby improving access efficiency. This allows the access department to quickly and efficiently acquire contract information and improve the performance of the entire system.

[0067] The providing unit provides the information acquired by the access unit to the customer. The providing unit provides the information through, for example, a web interface. The web interface is an interface that allows the customer to access information through a web browser, and the providing unit uses the web interface to provide the contract information to the customer. For example, when a customer requests "I want to check the details of the contract," the providing unit displays the contract details information through the web interface. The providing unit can also provide information by email. Email is a means for quickly transmitting information to a customer, and the providing unit uses email to provide the contract information to the customer. For example, when a customer requests "Please tell me the contract renewal status," the providing unit transmits the contract renewal status by email. Furthermore, the providing unit can also provide information through a chatbot. A chatbot is a system for exchanging information with a customer in an interactive format, and the providing unit uses the chatbot to provide the contract information to the customer. For example, when a customer asks the chatbot, "Please tell me the history of the contract," the providing unit provides the contract history information through the chatbot. This allows the providing unit to provide information to the customer through a variety of means, thereby improving customer convenience. Furthermore, the providing unit can also use AI to improve the accuracy and efficiency of information provision. For example, AI can analyze a customer's past question history and behavioral patterns to identify the optimal way to provide information, thereby providing more appropriate information to the customer. This allows the information department to provide information to customers quickly and accurately, improving customer satisfaction.

[0068] The access unit further includes an authentication unit for security or authentication of the acquired information, and the providing unit can provide the information authenticated by the authentication unit to the customer. The authentication unit, for example, uses encryption technology to ensure the security of the acquired information. For example, the authentication unit encrypts the acquired information to prevent leakage of the information. The authentication unit can also perform access control to allow only authenticated users to access the information. For example, the authentication unit performs access control based on the authentication information of the user to allow only authenticated users to access the information. The authentication unit can also ensure the security of the information using an authentication protocol. For example, the authentication unit ensures the security of the information using an authentication protocol such as password authentication, two-factor authentication, or biometric authentication. This ensures the safety of the acquired information. The providing unit provides the information authenticated by the authentication unit to the customer. The providing unit, for example, provides the information authenticated by the authentication unit through a web interface. The providing unit can also provide the information authenticated by the authentication unit by email. The providing unit can also provide the information authenticated by the authentication unit through a chatbot. This allows the providing unit to provide only the information authenticated by the authentication unit to the customer. This ensures the security of the acquired information. Some or all of the above-mentioned processing in the authentication unit may be performed, for example, using AI, or may be performed without using AI. For example, the authentication unit inputs the acquired information into AI, which performs processing to ensure the security of the information. The provision unit provides the information to the customer based on the information authenticated by AI. This makes it possible to provide information more efficiently and safely by using AI.

[0069] The reception unit can estimate the customer's emotion and adjust the method of receiving questions based on the estimated customer's emotion. For example, the reception unit captures the customer's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the customer's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also analyze the customer's text data and estimate the emotion. For example, the reception unit estimates the customer's emotion using a text analysis technique. This allows the reception unit to estimate the customer's emotion and adjust the method of receiving questions based on the estimated emotion. For example, if the customer is dissatisfied, the reception unit quickly accepts questions. Also, if the customer is relaxed, the reception unit uses a normal reception method. Furthermore, if the customer is in a hurry, the reception unit preferentially accepts questions. This makes it possible to accept questions appropriately according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. In this way, the use of AI enables more efficient and accurate emotion estimation.

[0070] The reception unit can analyze the customer's past question history and select an appropriate reception method. The reception unit, for example, analyzes the customer's past question history using data mining technology. For example, the reception unit analyzes the past question content, frequency of questions, type of questions, and the like, and selects an appropriate reception method. The reception unit can also analyze the customer's past question history using statistical analysis technology. For example, the reception unit suggests an optimal reception method for a specific time period from the past question history. Furthermore, the reception unit can analyze the customer's past question history using a machine learning algorithm. For example, the reception unit preferentially provides a reception channel (telephone, email, chat, etc.) that the customer prefers to use from the past question history. This allows the reception unit to provide an optimal reception method based on the customer's past question history. For example, the reception unit preferentially suggests a question format that the customer has frequently used in the past. Also, the reception unit preferentially provides a reception channel that the customer has preferred in the past. Furthermore, the reception unit suggests an optimal reception method for a specific time period from the customer's past question history. This allows the reception unit to provide an optimal reception method based on the customer's past question history. 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 inputs the customer's past question history into AI, which selects the optimal reception method. By using AI, it becomes possible to select a more efficient and accurate reception method.

[0071] The reception unit can filter based on the customer's current contract status and field of interest when receiving a question. The reception unit, for example, acquires the customer's current contract status from a database and filters when receiving a question. For example, the reception unit accepts only related questions based on the expiration date of the customer's contract, the type of contract, the conditions of the contract, and the like. The reception unit can also analyze the customer's field of interest from past behavior history and filter when receiving a question. For example, the reception unit preferentially accepts questions with high relevance based on topics of interest of the customer and past behavior history. Furthermore, the reception unit can filter specific questions based on the contents of the customer's contract and assign them to an appropriate person in charge. For example, the reception unit filters specific questions based on the contents of the customer's contract and assigns them to an appropriate person in charge. This allows the reception unit to preferentially accept questions related to the customer's current contract status and field of interest. For example, the reception unit accepts only related questions based on the customer's current contract status. Also, the reception unit preferentially accepts questions with high relevance based on the customer's field of interest. Furthermore, the reception unit filters specific questions based on the contents of the customer's contract and assigns them to an appropriate person in charge. This makes it possible to preferentially accept questions related to the customer's contract status and areas of interest. 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 inputs the customer's contract status and areas of interest into AI, which then performs filtering. This makes it possible to perform filtering more efficiently and accurately by using AI.

[0072] The reception unit can estimate the customer's emotion and determine the priority of questions to be received based on the estimated emotion. For example, the reception unit captures the customer's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the customer's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also analyze the text data of the customer and estimate the emotion. For example, the reception unit estimates the customer's emotion using a text analysis technique. This allows the reception unit to estimate the customer's emotion and determine the priority of questions to be received based on the estimated emotion. For example, if the customer is dissatisfied, the reception unit processes the question with the highest priority. Also, if the customer is relaxed, the reception unit processes the question with normal priority. Furthermore, if the customer is in a hurry, the reception unit processes the question with priority over other questions. This allows more appropriate response by determining the priority of questions according to the customer's emotion. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0073] The reception unit can preferentially receive questions with high relevance in consideration of the geographical location information of the customer when receiving a question. The reception unit, for example, acquires the geographical location information of the customer from GPS data, and preferentially receives questions with high relevance when receiving a question. For example, when the customer is in a specific area, the reception unit preferentially receives questions related to the area. The reception unit can also preferentially process the most relevant question based on the customer's current location. Furthermore, the reception unit can also preferentially receive questions regarding problems specific to the area based on the geographical location information of the customer. For example, the reception unit preferentially receives questions regarding problems specific to the area based on the geographical location information of the customer. This allows the reception unit to preferentially receive questions with high relevance based on the geographical location information of the customer. For example, when the customer is in a specific area, the reception unit preferentially receives questions related to the area. The reception unit also preferentially processes the most relevant question based on the customer's current location. The reception unit also preferentially receives questions regarding problems specific to the area based on the geographical location information of the customer. This allows questions with high relevance to be preferentially accepted based on the geographical location information of the customer. Some or all of the above-mentioned processing in the acceptance unit may be performed, for example, using AI, or may be performed without using AI. For example, the acceptance unit inputs the geographical location information of the customer into AI, and the AI ​​preferentially accepts questions with high relevance. This allows questions to be accepted more efficiently and accurately by using AI.

[0074] The reception unit can analyze the social media activity of the customer when receiving a question, and receive a related question. The reception unit, for example, analyzes the social media activity of the customer using a data mining technique. For example, the reception unit analyzes the content of the customer's posts, the number of likes, the number of followers, and the like, and receives a related question. The reception unit can also preferentially receive questions related to recent interests from the social media activity of the customer. For example, the reception unit receives related questions based on issues the customer mentioned on social media. The reception unit can also analyze the social media activity of the customer and preferentially process the most relevant question. For example, the reception unit analyzes the social media activity of the customer and preferentially process the most relevant question. This allows the reception unit to receive a related question based on the social media activity of the customer. For example, the reception unit preferentially receives questions related to recent interests from the social media activity of the customer. The reception unit also receives related questions based on issues the customer mentioned on social media. The reception unit can also analyze the social media activity of the customer and preferentially process the most relevant question. This allows the reception unit to receive a related question based on the social media activity of the customer. 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 inputs the customer's social media activity into AI, which then receives related questions. By using AI, this allows for more efficient and accurate question reception.

[0075] The analysis unit can estimate the customer's emotions and adjust the expression method of the analysis based on the estimated emotions. For example, the analysis unit captures the customer's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression. The analysis unit can also record the customer's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also analyze the customer's text data and estimate the emotions. For example, the analysis unit estimates the customer's emotions using text analysis technology. This allows the analysis unit to estimate the customer's emotions and adjust the expression method of the analysis based on the estimated emotions. For example, if the customer is dissatisfied, the analysis unit uses a concise and clear expression method. Also, if the customer is relaxed, the analysis unit uses an expression method including a detailed explanation. Furthermore, if the customer is in a hurry, the analysis unit uses a short expression method that captures the main points. This enables appropriate analysis expression according to the customer's emotions. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during the analysis. The analysis unit, for example, evaluates the importance of the question, and performs a detailed analysis on a question with a high importance. For example, the analysis unit evaluates the importance based on the urgency of the question, the impact of the question, the frequency of the question, and the like. The analysis unit can also perform a concise analysis on a question with a low importance. For example, the analysis unit adjusts the depth of the analysis according to the importance of the question. As a result, the analysis unit can adjust the level of detail of the analysis according to the importance of the question. For example, a detailed analysis is performed on a question with a high importance. Also, a concise analysis is performed on a question with a low importance. Furthermore, the depth of the analysis is adjusted according to the importance of the question. As a result, an appropriate analysis can be performed according to the importance of the question. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the importance of the question to an AI, and the AI ​​adjusts the level of detail of the analysis. As a result, more efficient and accurate analysis can be performed by using an AI.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. The analysis unit, for example, classifies the category of the question and applies a specialized analysis algorithm to technical questions. For example, the analysis unit applies a natural language processing algorithm to technical questions. The analysis unit can also apply a contract analysis algorithm to questions about the contract content. For example, the analysis unit applies a contract analysis algorithm to questions about the contract content. Furthermore, the analysis unit can also apply a general-purpose analysis algorithm to general questions. For example, the analysis unit applies a general-purpose analysis algorithm to general questions. This enables the analysis unit to perform an appropriate analysis according to the category of the question. For example, the analysis unit applies a specialized analysis algorithm to technical questions. Furthermore, the analysis unit applies a contract analysis algorithm to questions about the contract content. Furthermore, the analysis unit applies a general-purpose analysis algorithm to general questions. This enables an appropriate analysis according to the category of the question. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the question category into the AI, which then applies the appropriate analysis algorithm, making the use of AI more efficient and accurate.

[0078] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit captures the customer's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the customer's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also analyze the customer's text data and estimate the emotions. For example, the analysis unit estimates the customer's emotions using text analysis technology. This allows the analysis unit to estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is dissatisfied, the analysis unit performs a short and to the point analysis. Also, if the customer is relaxed, the analysis unit performs a detailed analysis. Furthermore, if the customer is in a hurry, the analysis unit performs the analysis quickly. This enables the length of the analysis to be appropriate according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0079] The analysis unit can determine the priority of the analysis based on the time of submission of the question during the analysis. The analysis unit, for example, evaluates the time of submission of the question and prioritizes the analysis of the most recently submitted question. For example, the analysis unit evaluates the time of submission based on the submission date and time, the timing of submission, the frequency of submission, and the like. The analysis unit can also postpone the analysis of questions submitted earlier. For example, the analysis unit adjusts the order of analysis based on the time of submission. This allows the analysis unit to determine the priority of the analysis based on the time of submission of the question. For example, the most recently submitted question is analyzed first. Also, questions submitted earlier are analyzed later. Furthermore, the order of analysis is adjusted based on the time of submission. This allows the appropriate priority of analysis to be determined based on the time of submission of the question. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the time of submission of the question to AI, and AI determines the priority of analysis. This allows more efficient and accurate analysis by using AI.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the questions during analysis. The analysis unit, for example, evaluates the relevance of the questions and prioritizes the analysis of questions with high relevance. For example, the analysis unit evaluates the relevance based on the similarity of the question content, the customer's interest, past question history, etc. Also, the analysis unit can postpone the analysis of questions with low relevance. For example, the analysis unit adjusts the order of analysis based on the relevance. As a result, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, questions with high relevance are analyzed with priority. Also, questions with low relevance are analyzed with postponement. Furthermore, the order of analysis is adjusted based on the relevance. As a result, the appropriate order of analysis can be adjusted based on the relevance of the questions. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the relevance of the questions to AI, and the AI ​​adjusts the order of analysis. As a result, more efficient and accurate analysis is possible by using AI.

[0081] The access unit can estimate the customer's emotions and adjust the access method based on the estimated emotions. For example, the access unit captures the customer's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the access unit calculates an emotion score based on changes in facial expression. The access unit can also record the customer's voice and estimate the emotions using voice analysis technology. For example, the access unit analyzes the tone and speed of the voice and calculates an emotion score. The access unit can also analyze the customer's text data and estimate the emotions. For example, the access unit estimates the customer's emotions using text analysis technology. This allows the access unit to estimate the customer's emotions and adjust the access method based on the estimated emotions. For example, if the customer is dissatisfied, the access unit accesses quickly. Also, if the customer is relaxed, the access unit uses a normal access method. Furthermore, if the customer is in a hurry, the access unit accesses preferentially. This enables an appropriate access method according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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-mentioned processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0082] The access unit can adjust the level of detail of the access based on the importance of the contract information at the time of access. The access unit, for example, evaluates the importance of the contract information, and performs detailed access to contract information with high importance. For example, the access unit evaluates the importance based on the amount of the contract, the period of the contract, the impact of the contract, and the like. The access unit can also perform brief access to contract information with low importance. For example, the access unit adjusts the depth of the access according to the importance of the contract information. As a result, the access unit can adjust the level of detail of the access according to the importance of the contract information. For example, detailed access is performed for contract information with high importance. Also, brief access is performed for contract information with low importance. Furthermore, the depth of the access is adjusted according to the importance of the contract information. As a result, appropriate access according to the importance of the contract information is possible. A part or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit inputs the importance of the contract information to AI, and AI adjusts the level of detail of the access. As a result, more efficient and accurate access is possible by using AI.

[0083] The access unit can apply different access algorithms depending on the category of the contract information at the time of access. The access unit, for example, classifies the category of the contract information and applies a specialized access algorithm to technical contract information. For example, the access unit applies a search algorithm to technical contract information. The access unit can also apply a contract access algorithm to information on the contract content. For example, the access unit applies a contract access algorithm to information on the contract content. Furthermore, the access unit can also apply a general-purpose access algorithm to general contract information. For example, the access unit applies a general-purpose access algorithm to general contract information. This enables the access unit to perform appropriate access according to the category of the contract information. For example, the access unit applies a specialized access algorithm to technical contract information. Furthermore, the access unit applies a contract access algorithm to information on the contract content. Furthermore, the access unit applies a general-purpose access algorithm to general contract information. This enables appropriate access according to the category of the contract information. A part or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit inputs the category of the contract information into AI, and the AI ​​applies an appropriate access algorithm. This will enable more efficient and accurate access by using AI.

[0084] The access unit can estimate the customer's emotions and determine the priority of access based on the estimated emotions. For example, the access unit captures the customer's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the access unit calculates an emotion score based on the change in facial expression. The access unit can also record the customer's voice and estimate the emotion using a voice analysis technique. For example, the access unit analyzes the tone and speed of the voice and calculates an emotion score. The access unit can also analyze the customer's text data and estimate the emotion. For example, the access unit estimates the customer's emotion using a text analysis technique. This allows the access unit to estimate the customer's emotions and determine the priority of access based on the estimated emotion. For example, if the customer is dissatisfied, the access unit accesses the contract information with the highest priority. Also, if the customer is relaxed, the access unit accesses with normal priority. Furthermore, if the customer is in a hurry, the access unit accesses the contract information with priority over other information. This enables appropriate access priority according to the customer's emotions. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0085] The access unit can determine the priority of access based on the submission time of the contract information at the time of access. The access unit, for example, evaluates the submission time of the contract information and preferentially accesses the most recently submitted contract information. For example, the access unit evaluates the submission time based on the submission date and time, the timing of submission, the frequency of submission, and the like. The access unit can also access contract information submitted earlier at a later date. For example, the access unit adjusts the order of access based on the submission time. This allows the access unit to determine the priority of access based on the submission time of the contract information. For example, recently submitted contract information is preferentially accessed. Also, contract information submitted earlier is accessed later. Furthermore, the order of access is adjusted based on the submission time. This allows appropriate access priority to be determined based on the submission time of the contract information. Part or all of the above-mentioned processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit inputs the submission time of the contract information into AI, and AI determines the priority of access. This allows more efficient and accurate access by using AI.

[0086] The access unit can adjust the order of access based on the relevance of the contract information at the time of access. The access unit, for example, evaluates the relevance of the contract information and preferentially accesses contract information with high relevance. For example, the access unit evaluates the relevance based on the similarity of the contract information, the customer's interest, past contract history, etc. In addition, the access unit can access contract information with low relevance later. For example, the access unit adjusts the order of access based on the relevance. As a result, the access unit can adjust the order of access based on the relevance of the contract information. For example, highly relevant contract information is preferentially accessed. In addition, contract information with low relevance is accessed later. Furthermore, the order of access is adjusted based on the relevance. As a result, an appropriate order of access can be adjusted based on the relevance of the contract information. A part or all of the above-mentioned processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit inputs the relevance of the contract information to AI, and AI adjusts the order of access. As a result, more efficient and accurate access is possible by using AI.

[0087] The providing unit can estimate the customer's emotion and adjust the presentation expression method based on the estimated emotion. For example, the providing unit captures the customer's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in facial expression. The providing unit can also record the customer's voice and estimate the emotion using a voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also analyze the customer's text data and estimate the emotion. For example, the providing unit estimates the customer's emotion using a text analysis technology. This allows the providing unit to estimate the customer's emotion and adjust the presentation expression method based on the estimated emotion. For example, if the customer is dissatisfied, the providing unit uses a concise and clear presentation method. Also, if the customer is relaxed, the providing unit uses a presentation method including a detailed explanation. Furthermore, if the customer is in a hurry, the providing unit uses a short presentation method that focuses on the main points. This enables appropriate presentation expression according to the customer's emotion. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This enables more efficient and accurate emotion estimation by using AI.

[0088] The provision unit can adjust the level of detail of the provision based on the importance of the contract information at the time of provision. The provision unit, for example, evaluates the importance of the contract information, and provides detailed information for contract information with high importance. For example, the provision unit evaluates the importance based on the amount of the contract, the period of the contract, the impact of the contract, and the like. The provision unit can also provide concise information for contract information with low importance. For example, the provision unit adjusts the depth of the provision according to the importance of the contract information. As a result, the provision unit can adjust the level of detail of the provision according to the importance of the contract information. For example, detailed information is provided for contract information with high importance. Also, concise information is provided for contract information with low importance. Furthermore, the depth of the provision is adjusted according to the importance of the contract information. As a result, appropriate provision according to the importance of the contract information is possible. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit inputs the importance of the contract information to AI, and AI adjusts the level of detail of the provision. As a result, more efficient and accurate provision is possible by using AI.

[0089] The providing unit can apply different provision algorithms according to the category of the contract information at the time of provision. The providing unit, for example, classifies the category of the contract information and applies a specialized provision algorithm to technical contract information. For example, the providing unit applies a search algorithm to technical contract information. The providing unit can also apply a contract provision algorithm to information on the contract content. For example, the providing unit applies a contract provision algorithm to information on the contract content. Furthermore, the providing unit can also apply a general-purpose provision algorithm to general contract information. For example, the providing unit applies a general-purpose provision algorithm to general contract information. This enables the providing unit to provide appropriate information according to the category of the contract information. For example, the providing unit applies a specialized provision algorithm to technical contract information. Furthermore, the providing unit applies a contract provision algorithm to information on the contract content. Furthermore, the providing unit applies a general-purpose provision algorithm to general contract information. This enables appropriate information to be provided according to the category of the contract information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision department inputs the category of contract information into the AI, which then applies an appropriate provision algorithm. This makes it possible to provide information more efficiently and accurately using AI.

[0090] The provision unit can estimate the customer's emotions and adjust the length of the provision based on the estimated emotions. For example, the provision unit captures the customer's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the provision unit calculates an emotion score based on the change in facial expression. The provision unit can also record the customer's voice and estimate the emotion using voice analysis technology. For example, the provision unit analyzes the tone and speed of the voice and calculates an emotion score. The provision unit can also analyze the customer's text data and estimate the emotion. For example, the provision unit estimates the customer's emotion using text analysis technology. This allows the provision unit to estimate the customer's emotions and adjust the length of the provision based on the estimated emotion. For example, if the customer is dissatisfied, the provision unit provides a short and to the point provision. Also, if the customer is relaxed, the provision unit provides detailed provision. Furthermore, if the customer is in a hurry, the provision unit provides the provision quickly. This enables the length of the provision to be appropriate according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0091] The providing unit can determine the priority of provision based on the submission time of the contract information at the time of provision. The providing unit, for example, evaluates the submission time of the contract information and provides the most recently submitted contract information preferentially. For example, the providing unit evaluates the submission time based on the submission date and time, the timing of submission, the frequency of submission, and the like. In addition, the providing unit can provide contract information with an older submission time later. For example, the providing unit adjusts the order of provision based on the submission time. Thereby, the providing unit can determine the priority of provision based on the submission time of the contract information. For example, the most recently submitted contract information is provided preferentially. In addition, the contract information with an older submission time is provided later. Furthermore, the order of provision is adjusted based on the submission time. Thereby, it is possible to determine the appropriate priority of provision based on the submission time of the contract information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the submission time of the contract information into AI, and AI determines the priority of provision. Thereby, more efficient and accurate provision is possible by using AI.

[0092] The providing unit can adjust the order of provision based on the relevance of the contract information at the time of provision. The providing unit, for example, evaluates the relevance of the contract information and provides highly relevant contract information preferentially. For example, the providing unit evaluates the relevance based on the similarity of the contract information, the customer's interest, past contract history, etc. Also, the providing unit can provide less relevant contract information later. For example, the providing unit adjusts the order of provision based on the relevance. As a result, the providing unit can adjust the order of provision based on the relevance of the contract information. For example, highly relevant contract information is preferentially provided. Also, less relevant contract information is provided later. Furthermore, the providing unit adjusts the order of provision based on the relevance. As a result, it is possible to adjust the appropriate order of provision based on the relevance of the contract information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the relevance of the contract information to AI, and AI adjusts the order of provision. As a result, more efficient and accurate provision is possible by using AI.

[0093] The authentication unit can estimate the customer's emotion and adjust the authentication method based on the estimated emotion. For example, the authentication unit captures the customer's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the authentication unit calculates an emotion score based on the change in facial expression. The authentication unit can also record the customer's voice and estimate the emotion using a voice analysis technology. For example, the authentication unit analyzes the tone and speed of the voice and calculates an emotion score. The authentication unit can also analyze the customer's text data and estimate the emotion. For example, the authentication unit estimates the customer's emotion using a text analysis technology. This allows the authentication unit to estimate the customer's emotion and adjust the authentication method based on the estimated emotion. For example, if the customer is dissatisfied, the authentication unit provides a quick and concise authentication method. Also, if the customer is relaxed, the authentication unit provides a detailed authentication method. Furthermore, if the customer is in a hurry, the authentication unit performs authentication quickly. This enables an appropriate authentication method according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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-mentioned processing in the authentication unit may be performed, for example, using AI, or may be performed without using AI. For example, the authentication unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0094] The authentication unit can adjust the level of detail of authentication based on the importance of the contract information at the time of authentication. The authentication unit, for example, evaluates the importance of the contract information, and performs detailed authentication for contract information with high importance. For example, the authentication unit evaluates the importance based on the amount of the contract, the period of the contract, the impact of the contract, and the like. The authentication unit can also perform simple authentication for contract information with low importance. For example, the authentication unit adjusts the depth of authentication according to the importance of the contract information. As a result, the authentication unit can adjust the level of detail of authentication according to the importance of the contract information. For example, detailed authentication is performed for contract information with high importance. Also, simple authentication is performed for contract information with low importance. Furthermore, the authentication depth is adjusted according to the importance of the contract information. As a result, appropriate authentication according to the importance of the contract information is possible. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit inputs the importance of the contract information to AI, and AI adjusts the level of detail of authentication. As a result, more efficient and accurate authentication is possible by using AI.

[0095] The authentication unit can estimate the emotion of the customer and determine the priority of authentication based on the estimated emotion. For example, the authentication unit captures the facial expression of the customer with a camera and estimates the emotion using an emotion estimation algorithm. For example, the authentication unit calculates an emotion score based on the change in facial expression. The authentication unit can also record the voice of the customer and estimate the emotion using a voice analysis technique. For example, the authentication unit analyzes the tone and speed of the voice and calculates an emotion score. The authentication unit can also analyze the text data of the customer and estimate the emotion. For example, the authentication unit estimates the emotion of the customer using a text analysis technique. This allows the authentication unit to estimate the emotion of the customer and determine the priority of authentication based on the estimated emotion. For example, if the customer is dissatisfied, the authentication unit authenticates the contract information with the highest priority. Also, if the customer is relaxed, the authentication unit authenticates with normal priority. Furthermore, if the customer is in a hurry, the authentication unit authenticates the contract information with priority over other information. This enables appropriate authentication priority according to the emotion of the customer. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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-mentioned processing in the authentication unit may be performed, for example, using AI, or may be performed without using AI. For example, the authentication unit can input image data of a customer captured by a camera to the generation AI and cause the generation AI to estimate the customer's emotions. This allows for more efficient and accurate emotion estimation by using AI.

[0096] The authentication unit can determine the priority of authentication based on the submission time of the contract information at the time of authentication. The authentication unit, for example, evaluates the submission time of the contract information and preferentially authenticates the most recently submitted contract information. For example, the authentication unit evaluates the submission time based on the submission date and time, the timing of submission, the frequency of submission, and the like. In addition, the authentication unit can also postpone the authentication of contract information submitted earlier. For example, the authentication unit adjusts the order of authentication based on the submission time. As a result, the authentication unit can determine the priority of authentication based on the submission time of the contract information. For example, the most recently submitted contract information is preferentially authenticated. In addition, the contract information submitted earlier is postponed for authentication. Furthermore, the authentication order is adjusted based on the submission time. As a result, it is possible to determine an appropriate priority of authentication based on the submission time of the contract information. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit inputs the submission time of the contract information to AI, and AI determines the priority of authentication. As a result, more efficient and accurate authentication is possible by using AI.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the customer's past purchase history and preferentially accept related questions. For example, the reception unit preferentially processes related questions based on products and services purchased by the customer in the past. The reception unit can also preferentially accept questions about specific products and services from the customer's purchase history. Furthermore, the reception unit can analyze the customer's purchase history and preferentially process the most relevant questions. This allows the reception unit to preferentially accept questions related to the customer's purchase history. For example, the reception unit preferentially processes related questions based on products and services purchased by the customer in the past. The reception unit also preferentially accepts questions about specific products and services from the customer's purchase history. Furthermore, the reception unit analyzes the customer's purchase history and preferentially processes the most relevant questions. This allows the reception unit to preferentially accept questions related to the customer's purchase history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the customer's purchase history into AI, and the AI ​​preferentially accepts questions related to AI. This will enable the use of AI to handle questions more efficiently and accurately.

[0099] The analysis unit can analyze the customer's past feedback and improve the accuracy of the analysis. For example, the analysis unit adjusts the analysis algorithm based on the feedback provided by the customer in the past. The analysis unit can also extract specific problems from the customer's feedback and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the customer's feedback and identify the most effective analysis method. This allows the analysis unit to improve the accuracy of the analysis based on the customer's feedback. For example, the analysis unit adjusts the analysis algorithm based on the feedback provided by the customer in the past. The analysis unit can also extract specific problems from the customer's feedback and improve the accuracy of the analysis. Furthermore, the analysis unit analyzes the customer's feedback and identifies the most effective analysis method. This allows the analysis unit to improve the accuracy of the analysis based on the customer's feedback. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the customer's feedback into AI, and the AI ​​improves the accuracy of the analysis. This allows more efficient and accurate analysis by using AI.

[0100] The providing unit can estimate the customer's emotions and adjust the format of the information to be provided based on the estimated emotions. For example, if the customer is dissatisfied, the providing unit provides information in a concise and clear format. If the customer is relaxed, the providing unit provides information in a format including detailed explanations. If the customer is in a hurry, the providing unit provides information in a short format that focuses on the main points. This makes it possible to provide appropriate information according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of the customer taken by a camera to the generation AI and cause the generation AI to execute an estimation of the customer's emotions. This makes it possible to perform more efficient and accurate emotion estimation by using AI.

[0101] The providing unit can analyze the customer's past inquiry history and provide related information preferentially. For example, the providing unit can provide related information preferentially based on the content of the customer's past inquiry. The providing unit can also provide information on a specific topic preferentially from the customer's inquiry history. Furthermore, the providing unit can analyze the customer's inquiry history and provide the most relevant information preferentially. As a result, the providing unit can provide related information preferentially based on the customer's inquiry history. For example, the providing unit can provide related information preferentially based on the content of the customer's past inquiry. The providing unit can also provide information on a specific topic preferentially from the customer's inquiry history. Furthermore, the providing unit can analyze the customer's inquiry history and provide the most relevant information preferentially. As a result, related information can be provided preferentially based on the customer's inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the customer's inquiry history into AI, and the AI ​​provides related information preferentially. As a result, more efficient and accurate information can be provided by using AI.

[0102] The providing unit can estimate the customer's emotions and adjust the amount of information to be provided based on the estimated emotions. For example, if the customer is dissatisfied, the providing unit provides concise, to-the-point information. If the customer is relaxed, the providing unit provides detailed information. If the customer is in a hurry, the providing unit quickly provides only the necessary information. This makes it possible to provide appropriate information according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of the customer taken by a camera to the generation AI and cause the generation AI to execute an estimation of the customer's emotions. This makes it possible to perform more efficient and accurate emotion estimation by using AI.

[0103] The providing unit can analyze the customer's past purchase history and provide related information preferentially. For example, the providing unit can provide related information preferentially based on products and services purchased by the customer in the past. The providing unit can also provide information on a specific product or service preferentially from the customer's purchase history. Furthermore, the providing unit can analyze the customer's purchase history and provide the most relevant information preferentially. As a result, the providing unit can provide related information preferentially based on the customer's purchase history. For example, the providing unit can provide related information preferentially based on products and services purchased by the customer in the past. The providing unit can also provide information on a specific product or service preferentially from the customer's purchase history. Furthermore, the providing unit can analyze the customer's purchase history and provide the most relevant information preferentially. As a result, related information can be provided preferentially based on the customer's purchase history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the customer's purchase history into AI, and the AI ​​provides related information preferentially. As a result, more efficient and accurate information provision is possible by using AI.

[0104] The providing unit can estimate the customer's emotions and determine the priority of information to be provided based on the estimated emotions. For example, if the customer is dissatisfied, the providing unit provides that information with the highest priority. Also, if the customer is relaxed, the providing unit provides information with normal priority. Furthermore, if the customer is in a hurry, the providing unit provides that information with priority over other information. This makes it possible to provide appropriate information according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input image data of the customer taken by a camera to the generation AI and cause the generation AI to execute an estimation of the customer's emotions. This makes it possible to perform more efficient and accurate emotion estimation by using AI.

[0105] The providing unit can provide relevant information preferentially in consideration of the geographical location information of the customer. For example, when the customer is in a specific area, the providing unit preferentially provides information related to the area. The providing unit can also provide the most relevant information preferentially based on the customer's current location. Furthermore, the providing unit can also provide information related to problems specific to the area preferentially based on the customer's geographical location information. This allows the providing unit to provide relevant information preferentially based on the customer's geographical location information. For example, when the customer is in a specific area, the providing unit preferentially provides information related to the area. Furthermore, the providing unit preferentially provides information related to problems specific to the area based on the customer's geographical location information. This allows the providing unit to provide relevant information preferentially based on the customer's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the customer's geographical location information into AI, and the AI ​​provides relevant information preferentially. This allows more efficient and accurate information provision by using AI.

[0106] The providing unit can estimate the customer's emotions and adjust the level of detail of the information to be provided based on the estimated emotions. For example, if the customer is dissatisfied, the providing unit provides concise, to-the-point information. Also, if the customer is relaxed, the providing unit provides detailed information. Furthermore, if the customer is in a hurry, the providing unit quickly provides only the necessary information. This makes it possible to provide appropriate information according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of the customer taken by a camera to the generation AI and cause the generation AI to execute an estimation of the customer's emotions. This makes it possible to perform more efficient and accurate emotion estimation by using AI.

[0107] The providing unit can analyze the social media activity of the customer and provide related information preferentially. For example, the providing unit analyzes the content of the customer's posts, the number of likes, the number of followers, and the like, and provides the related information. The providing unit can also provide information related to recent interests from the social media activity of the customer preferentially. Furthermore, the providing unit can analyze the social media activity of the customer and provide the most relevant information preferentially. As a result, the providing unit can provide related information based on the social media activity of the customer. For example, the providing unit analyzes the content of the customer's posts, the number of likes, the number of followers, and the like, and provides the related information. The providing unit can also provide information related to recent interests from the social media activity of the customer preferentially. Furthermore, the providing unit analyzes the social media activity of the customer and provides the most relevant information preferentially. As a result, the providing unit can provide related information based on the social media activity of the customer. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the social media activity of the customer into AI, and AI provides related information. As a result, more efficient and accurate information provision is possible by using AI.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception unit receives questions from customers. Questions from customers may be in text format, voice format, image format, etc. The reception unit can analyze questions in text format using natural language processing technology, and convert voice format questions into text format using voice recognition technology and analyze them. It can also analyze questions in image format using image recognition technology. Step 2: The analysis unit analyzes the question received by the reception unit and obtains the contract information. The analysis unit analyzes the content of the question using natural language processing technology, data mining technology, and machine learning algorithms, and identifies the appropriate contract information. Step 3: The access unit accesses the inquiry system of each contract service to acquire the contract information acquired by the analysis unit. The access unit can access the inquiry system of the contract service using an API, as well as directly access a database or acquire the contract information through a web interface. Step 4: The provision unit provides the information acquired by the access unit to the customer. The provision unit can provide the information through a web interface, email, or chatbot.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the reception unit is realized by the reception device 38 of the smart device 14. For example, a text-format question is received using a touch panel 38A. A voice-format question is received using a microphone 38B. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The access unit accesses the inquiry system for the contract service via, for example, the communication I / F 26 of the data processing device 12. The provision unit is realized by, for example, the output device 40 of the smart device 14, and provides information using a display 40A or a speaker 40B. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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, the RAM 30, and the 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the reception unit is realized by the microphone 238 of the smart glasses 214. For example, a question in the form of voice is received using the microphone 238. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The access unit accesses the inquiry system of the contract service via, for example, the communication I / F 26 of the data processing device 12. The provision unit is realized by, for example, the speaker 240 of the smart glasses 214, and provides information using voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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, the RAM 30, and the 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the reception unit is realized by the microphone 238 of the headset terminal 314. For example, a question in the form of voice is received using the microphone 238. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The access unit accesses the inquiry system for the contract service via, for example, the communication I / F 26 of the data processing device 12. The provision unit provides information using, for example, the speaker 240 or the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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, the RAM 30, and the 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0162] For example, the reception unit is realized by the microphone 238 of the robot 414. For example, a question in the form of voice is received using the microphone 238. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The access unit accesses the inquiry system for the contract service via, for example, the communication I / F 26 of the data processing device 12. The provision unit provides information using, for example, the speaker 240 of the robot 414 or the control target 443. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) a reception section for receiving questions from customers; an analysis unit that analyzes the question received by the reception unit and acquires contract information; an access unit that accesses a reference system for each contract service for the contract information acquired by the analysis unit; A providing unit that provides the information acquired by the access unit to a customer. A system characterized by: (Appendix 2) An authentication unit for security or authentication of information acquired by the access unit, The providing unit provides the information authenticated by the authenticating unit to a customer. 2. The system of claim 1, (Appendix 3) The reception unit is Infer customer sentiment and adjust how questions are received based on the inferred sentiment 2. The system of claim 1, (Appendix 4) The reception unit is Analyze the customer's past inquiry history and select the appropriate reception method 2. The system of claim 1, (Appendix 5) The reception unit is Filter questions based on the customer's current status and interests 2. The system of claim 1, (Appendix 6) The reception unit is Estimate customer sentiment and prioritize incoming questions based on the estimated sentiment 2. The system of claim 1, (Appendix 7) The reception unit is When accepting questions, the system takes into account the customer's geographic location to prioritize the most relevant questions 2. The system of claim 1, (Appendix 8) The reception unit is When receiving a question, analyze the customer's social media activity and receive related questions 2. The system of claim 1, (Appendix 9) The analysis unit is Infer customer sentiment and adjust analytics presentation based on inferred sentiment 2. The system of claim 1, (Appendix 10) The analysis unit is During analysis, adjust the level of detail in the analysis based on the importance of the question 2. The system of claim 1, (Appendix 11) The analysis unit is During parsing, apply different parsing algorithms depending on the question category 2. The system of claim 1, (Appendix 12) The analysis unit is Infer customer sentiment and adjust the length of the analysis based on the inferred sentiment 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, prioritize analysis based on when questions were submitted 2. The system of claim 1, (Appendix 14) The analysis unit is During parsing, adjust the order of parsing based on question relevance 2. The system of claim 1, (Appendix 15) The access unit is Infer customer sentiment and adjust access methods based on inferred sentiment 2. The system of claim 1, (Appendix 16) The access unit is At the time of access, adjust the granularity of access based on the importance of the contract information 2. The system of claim 1, (Appendix 17) The access unit is Apply different access algorithms depending on the category of contract information when accessed 2. The system of claim 1, (Appendix 18) The access unit is Estimate customer sentiment and prioritize access based on the estimated sentiment 2. The system of claim 1, (Appendix 19) The access unit is At the time of access, access will be prioritized based on when contract information was submitted. 2. The system of claim 1, (Appendix 20) The access unit is At the time of access, adjust the access order based on the relevance of the contract information 2. The system of claim 1, (Appendix 21) The providing unit is Infer customer sentiment and adjust how you present your offer based on that sentiment 2. The system of claim 1, (Appendix 22) The providing unit is When providing, adjust the level of detail provided based on the importance of the contract information 2. The system of claim 1, (Appendix 23) The providing unit is When providing, different provisioning algorithms are applied depending on the category of contract information. 2. The system of claim 1, (Appendix 24) The providing unit is Estimate customer sentiment and adjust the length of the offer based on the estimated sentiment 2. The system of claim 1, (Appendix 25) The providing unit is At the time of delivery, delivery will be prioritized based on the timing of contract information submission. 2. The system of claim 1, (Appendix 26) The providing unit is At delivery time, adjust delivery order based on contract information relevance 2. The system of claim 1, (Appendix 27) The authentication unit Infer customer sentiment and adjust authentication methods based on the inferred sentiment 3. The system of claim 2, (Appendix 28) The authentication unit Adjust authentication detail based on the importance of contract information during authentication 3. The system of claim 2, (Appendix 29) The authentication unit Infer customer sentiment and prioritize authentication based on the inferred sentiment 3. The system of claim 2, (Appendix 30) The authentication unit During certification, certification priorities are determined based on when contract information was submitted. 3. The system of claim 2, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for receiving questions from customers; an analysis unit that analyzes the question received by the reception unit and acquires contract information; an access unit that accesses a reference system for each contract service for the contract information acquired by the analysis unit; A providing unit that provides the information acquired by the access unit to a customer. A system characterized by:

2. An authentication unit for security or authentication of information acquired by the access unit, The providing unit provides the information authenticated by the authenticating unit to a customer.

2. The system of claim 1 .

3. The reception unit is Infer customer sentiment and adjust how questions are received based on the inferred sentiment 2. The system of claim 1 .

4. The reception unit is Analyze the customer's past inquiry history and select the appropriate reception method 2. The system of claim 1 .

5. The reception unit is Filter questions based on the customer's current status and interests 2. The system of claim 1 .

6. The reception unit is Estimate customer sentiment and prioritize incoming questions based on the estimated sentiment 2. The system of claim 1 .

7. The reception unit is When accepting questions, the system takes into account the customer's geographic location to prioritize the most relevant questions 2. The system of claim 1 .

8. The reception unit is When receiving a question, analyze the customer's social media activity and receive related questions 2. The system of claim 1 .

9. The analysis unit is Infer customer sentiment and adjust analytics presentation based on inferred sentiment 2. The system of claim 1 .

10. The analysis unit is During analysis, adjust the level of detail in the analysis based on the importance of the question 2. The system of claim 1 .

Images