system

Abstract

The system according to this embodiment aims to streamline customer service and reduce waiting times. [Solution] The system according to the embodiment comprises a reception unit, a summarization unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit receives identity verification and inquiry details. The summarization unit summarizes the information received by the reception unit. The analysis unit analyzes customer attribute information based on the information summarized by the summarization unit. The generation unit generates a response based on the information analyzed by the analysis unit. The provision unit provides the response generated by the generation unit. The storage unit logs and stores the response provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, customer support at a call center is complicated, and there is a risk of long waiting times.

[0005] The system according to the embodiment aims to improve customer support efficiency and shorten waiting times.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a summarization unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit receives identity verification and inquiry details. The summarization unit summarizes the information received by the reception unit. The analysis unit analyzes customer attribute information based on the information summarized by the summarization unit. The generation unit generates a response based on the information analyzed by the analysis unit. The provision unit provides the response generated by the generation unit. The storage unit logs and stores the response provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline customer service and reduce waiting times. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The call center system according to an embodiment of the present invention is a system that eliminates IVR and realizes simple inquiry handling. With this call center system, the incoming customer only needs to provide "identity verification + inquiry content". The receiving AI summarizes the content, reads the age group, tone of voice and emotion, and the generating AI analyzes it while taking into account the customer's mood, etc. This enables immediate responses tailored to each customer's condition, reduces waiting times, and realizes speedy customer service. In addition, customer inquiries are logged and stored according to pre-classified categories, so the AI ​​can handle everything from answering the call to inquiry, confirmation, answering, and log saving in a seamless manner. This results in significant economies of scale and contributes to avoiding customer harassment problems. For example, the incoming customer provides "identity verification + inquiry content". At this time, they provide information such as "My name is Yamada Taro, and the inquiry is to confirm the invoice". Next, the receiving AI summarizes this information and reads the age group, tone of voice and emotion. For example, if the voice is that of a young man, it will be classified as "young male", and if the tone is calm, it will be classified as "calm". Next, the generating AI analyzes this information and generates responses tailored to each customer's condition. For example, a young male customer with a calm demeanor will receive a quick and concise response. In this way, waiting times are reduced, and speedy customer service is achieved. Furthermore, customer inquiries are logged and stored according to pre-classified categories. For example, an inquiry about "checking an invoice" is classified under the "billing-related" category and stored as a log. This makes it easy to look up and check information later. This system allows the AI ​​to handle everything from answering calls to inquiries, confirmations, responses, and log storage in a seamless manner, resulting in significant economies of scale. It also contributes to avoiding customer harassment issues and reducing employee turnover. For example, by having the AI ​​handle inquiries, emotional interactions can be avoided, reducing operator stress. Thus, this invention eliminates IVR in call centers and enables simple inquiry handling, achieving both cost optimization and improved customer satisfaction.This allows the call center system to respond quickly and accurately to customer inquiries, thereby improving customer satisfaction.

[0029] The call center system according to this embodiment comprises a reception unit, a summarization unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit receives identity verification and inquiry content. The reception unit can perform identity verification using methods such as ID verification, password authentication, and biometric authentication. The reception unit can also receive inquiry content in the form of text, voice, images, etc. The summarization unit uses AI to summarize the information received by the reception unit. The summarization unit can perform summarization based on, for example, the length of the text or the importance of the information to be summarized. The analysis unit uses AI to analyze customer attribute information based on the information summarized by the summarization unit. The analysis unit can perform analysis using methods such as data mining, statistical analysis, and machine learning. The generation unit uses generation AI to generate answers based on the information analyzed by the analysis unit. The generation unit can generate answers using methods such as template-based generation and natural language generation. The provision unit provides the answers generated by the generation unit. The provision unit can provide answers using methods such as real-time provision and batch provision. The storage unit logs the responses provided by the provision unit. The storage unit can save the logs by methods such as database storage or file storage. This allows the call center system according to the embodiment to handle everything from identity verification to summarizing, analyzing, generating, providing, and logging inquiries. For example, the reception unit verifies the ID and receives the inquiry in text format. The summarization unit summarizes the received text and extracts important information. The analysis unit analyzes the customer's age group, tone of voice, and emotions based on the summarized information. The generation unit generates a response that matches the customer's condition based on the analyzed information. The provision unit provides the generated response in real time, and the storage unit saves the provided response in a database. This enables the call center system to respond quickly and accurately to customer inquiries and improve customer satisfaction.

[0030] The reception desk handles identity verification and receives inquiries. The reception desk can verify identity using methods such as ID verification, password authentication, and biometric authentication. Specifically, ID verification involves the user entering a pre-registered ID and password. Password authentication involves the user entering a password they have set, which the system then matches against a database. Biometric authentication uses technologies such as fingerprint recognition, facial recognition, and voice recognition to verify the user's biometric information. This allows the reception desk to perform identity verification quickly while ensuring high security. Furthermore, the reception desk can accept inquiries in various formats, including text, audio, and images. For example, in text format, it accepts content entered by the user in a chat box; in audio format, it converts what the user says over the phone into text using speech recognition technology; and in image format, it analyzes images sent by the user to extract the inquiry content. This allows the reception desk to handle diverse inquiry formats and improve user convenience. Additionally, the reception desk can temporarily store received inquiries and smoothly pass them on to the next processing step. This improves the overall efficiency of the system and reduces user waiting times.

[0031] The summarization unit uses AI to summarize information received by the reception unit. The summarization unit can perform summaries based, for example, on the length of the text and the importance of the information being summarized. Specifically, it uses natural language processing technology to extract important keywords and phrases from long texts and generate a summary. Because the AI ​​has been trained on a large amount of text data beforehand and possesses the ability to understand context and meaning, it can accurately extract important information. For example, if a user submits a long complaint, the summarization unit can extract the main issues and requirements from the complaint and summarize them into a short summary. Similarly, in the case of audio data, it can convert it to text using speech recognition technology and then perform a summary. This allows the summarization unit to efficiently process diverse data formats received from the reception unit and pass them on to the next analysis step. Furthermore, the summarization unit can collect user feedback to continuously improve its AI model in order to enhance the accuracy of the summaries. This enables the summarization unit to consistently provide highly accurate summaries and improve the overall efficiency and accuracy of the system.

[0032] The analysis unit uses AI to analyze customer attribute information based on the information summarized by the summarization unit. The analysis unit can perform analysis using methods such as data mining, statistical analysis, and machine learning. Specifically, it extracts attribute information such as customer age group, gender, region, and past inquiry history based on the summarized text data. The AI ​​statistically analyzes this data to predict customer behavior patterns and preferences. For example, by analyzing past inquiry history, it can identify what problems a particular customer frequently faces and predict future inquiry trends. It can also perform customer sentiment analysis. The AI ​​reads emotions from text data and determines the customer's current emotional state. This allows the analysis unit to understand not only customer attribute information but also their emotional state, providing information for more appropriate responses. Furthermore, the analysis unit can segment customers based on these analysis results. Customers are grouped based on specific attributes and behavior patterns, and optimal countermeasures are proposed for each group. This allows the analysis unit to build a foundation for responding to diverse customer needs and providing personalized services.

[0033] The generation unit uses generation AI to generate responses based on information analyzed by the analysis unit. The generation unit can generate responses using methods such as template-based generation and natural language generation. Specifically, the generation AI generates the optimal response based on customer attribute information and emotional state provided by the analysis unit. In template-based generation, pre-prepared response templates are used and customized according to the customer's specific situation. In natural language generation, the AI ​​understands the context and generates natural-sounding sentences. For example, if a customer inquires about a product defect, the generation AI analyzes the details of the defect and generates a response that proposes an appropriate solution. The generation AI can also consider the customer's emotional state and generate responses using appropriate tone and expression. This allows the generation unit to provide customers with quick and accurate responses, improving customer satisfaction. Furthermore, the generation unit can evaluate the quality of the generated responses and make corrections as needed. For example, if a generated response is inappropriate, the AI ​​analyzes the cause and reflects it in the next generation. This allows the generation unit to consistently provide high-quality responses.

[0034] The service provider delivers the answers generated by the generation provider. The service provider can deliver answers using methods such as real-time delivery or batch delivery. Specifically, real-time delivery enables rapid response by immediately sending generated answers to customers. For example, answers can be provided immediately after a customer makes an inquiry via a chatbot or voice assistant. Batch delivery generates answers to multiple inquiries at once and delivers them all at a set time. This allows the service provider to efficiently handle a large volume of inquiries. Furthermore, the service provider can flexibly select the method of delivering answers according to customer needs. For example, answers can be delivered using the method most convenient for the customer, such as email, SMS, or push notification. In addition, the service provider can monitor the receipt status of the delivered answers and customer reactions, and follow up as needed. This allows the service provider to respond to customers quickly and appropriately, improving customer satisfaction.

[0035] The storage unit logs the responses provided by the service provider. The storage unit can save logs using methods such as database storage or file storage. Specifically, it records detailed information such as the content of the response provided, the date and time of provision, and the customer's response, and stores it in a database. This allows the storage unit to centrally manage past inquiry and response histories. For example, if a customer makes another inquiry, the storage unit can refer to the past response history stored in the storage unit to provide a quick and accurate response. The storage unit can also analyze the stored data to improve the system and enhance services. For example, by analyzing past inquiry data, it can identify common inquiries and problems, leading to improvements in FAQs and response processes. Furthermore, the storage unit can implement measures such as encryption and access restrictions to ensure data security. This allows the storage unit to achieve efficient data management while protecting customer privacy.

[0036] The analysis unit can analyze the customer's age group, tone of voice, and emotions. For example, the analysis unit can analyze the tone and speed of the customer's voice to estimate their age group. It can also analyze the customer's way of speaking and word choice to classify their tone of voice. Furthermore, the analysis unit can analyze the emotions in the customer's voice to estimate emotions such as positive, negative, or neutral. This allows for more appropriate responses by analyzing customer attribute information in detail. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input customer voice data into AI and have the AI ​​perform the analysis of age group, tone of voice, and emotions.

[0037] The generation unit can generate responses that are appropriate to the customer's condition. For example, the generation unit can generate appropriate responses based on the customer's age group, tone of voice, and emotions. For example, it can provide concise and easy-to-understand responses to younger customers and polite and detailed responses to older customers. The generation unit can also generate positive or negative responses depending on the customer's emotions. This allows for the provision of appropriate responses according to the customer's state. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input customer attribute information into a generation AI and have the generation AI perform the generation of responses.

[0038] The storage unit can log and save inquiry content according to pre-classified categories. For example, the storage unit can classify inquiry content into categories such as "billing-related," "technical support," and "general inquiries," and save logs according to each category. This allows for efficient management of inquiry content and facilitates later retrieval and verification. Some or all of the above-described processes in the storage unit may be performed using AI, or they may not. For example, the storage unit can input inquiry content into AI and have the AI ​​perform category classification and log saving.

[0039] The service provider can provide the generated answers immediately. For example, it can provide answers in real time. For example, it can provide the generated answers immediately after a customer makes an inquiry. The service provider can also provide answers in batch processing. For example, it can provide answers in batches at regular time intervals. This enables rapid customer response. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated answers into the AI ​​and have the AI ​​provide the answers.

[0040] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception department can prioritize suggesting identity verification methods that the user has frequently used in the past. Furthermore, the reception department can automatically generate relevant questions based on the user's past inquiries. In addition, the reception department can suggest the most suitable reception method for a specific time period based on the user's past inquiry history. This allows for the provision of the most suitable reception method based on past history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past inquiry history into an AI and have the AI ​​select the most suitable reception method.

[0041] The reception desk can filter information based on the user's current situation and areas of interest during the reception process. For example, when a user enters their current situation, the reception desk can automatically generate relevant questions. Furthermore, the reception desk can prioritize displaying relevant information based on the user's areas of interest. In addition, the reception desk can suggest the most appropriate reception method according to the user's current situation. This enables appropriate responses tailored to the user's situation and interests. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current situation and areas of interest into an AI and have the AI ​​perform the filtering.

[0042] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize processing inquiries related to that region. The reception desk can also prioritize displaying relevant information based on the user's geographical location. Furthermore, the reception desk can suggest the most appropriate reception method according to the user's current location. This enables appropriate responses based on geographical information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​determine the priority of highly relevant inquiries.

[0043] The reception desk can analyze a user's social media activity and receive relevant inquiries upon receiving a request. For example, the reception desk can automatically generate relevant inquiry content from the user's social media activity. Furthermore, the reception desk can prioritize displaying relevant information based on the user's social media activity. In addition, the reception desk can analyze the user's social media activity and propose the optimal reception method. This enables appropriate responses based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user social media activity data into AI and have the AI ​​generate relevant inquiries and select the reception method.

[0044] The summarization unit can adjust the level of detail in the summary based on the importance of the inquiry content during summary generation. For example, the summarization unit can provide a detailed summary for important inquiries. It can also provide a concise summary for ordinary inquiries. Furthermore, it can provide a summary that gets straight to the point for urgent inquiries. This ensures that an appropriate summary is provided according to the importance of the inquiry content. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input inquiry importance data into the AI ​​and have the AI ​​adjust the level of detail in the summary.

[0045] The summarization unit can apply different summarization algorithms depending on the category of the inquiry when generating a summary. For example, the summarization unit can apply a detailed summarization algorithm for billing-related inquiries. It can also apply a concise summarization algorithm for technical support-related inquiries. Furthermore, it can apply a standard summarization algorithm for general inquiries. This ensures that appropriate summaries are provided according to the category. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input the category data of the inquiry into the AI ​​and have the AI ​​apply the summarization algorithm.

[0046] The summarization unit can determine the priority of summaries based on when the inquiry content was submitted during the summarization process. For example, the summarization unit can prioritize summarizing recently submitted inquiries. It can also postpone summarizing inquiries submitted in the past. Furthermore, the summarization unit can automatically determine the priority of summaries based on the submission date. This allows for the provision of appropriate summaries based on the submission date. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input inquiry submission date data into the AI ​​and have the AI ​​perform the determination of summary priority.

[0047] The summarization unit can adjust the order of summaries based on the relevance of the query content during summary generation. For example, the summarization unit can prioritize summarizing highly relevant query content. It can also postpone summarizing less relevant query content. Furthermore, the summarization unit can automatically adjust the order of summaries based on the relevance of the query content. This allows for the provision of appropriate summaries based on relevance. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input query relevance data into AI and have the AI ​​perform the adjustment of the summary order.

[0048] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between query content during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on related query content. Furthermore, the analysis unit can analyze the interrelationships between query content and apply the most appropriate analysis method. In addition, the analysis unit can automatically improve the accuracy of its analysis based on the interrelationships between query content. This enables the provision of appropriate analysis based on interrelationships. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input interrelationship data of query content into AI and have the AI ​​perform the analysis accuracy improvement.

[0049] The analysis unit can perform analysis while considering the attribute information of the person submitting the inquiry. For example, the analysis unit can apply the most suitable analysis method based on the age group of the submitter. Furthermore, the analysis unit can apply the most suitable analysis method based on the gender of the submitter. In addition, the analysis unit can apply the most suitable analysis method based on the occupation of the submitter. This allows for the provision of appropriate analysis based on the submitter's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the submitter's attribute information data into AI and have the AI ​​perform the analysis.

[0050] The analysis unit can perform analysis while considering the geographical distribution of the inquiry content. For example, the analysis unit can improve the accuracy of the analysis based on geographically related inquiry content. Furthermore, the analysis unit can analyze the geographical distribution of the inquiry content and apply the optimal analysis method. In addition, the analysis unit can automatically improve the accuracy of the analysis based on the geographical distribution of the inquiry content. This enables the provision of appropriate analysis based on geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data of the inquiry content into the AI ​​and have the AI ​​perform the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the query during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature. Furthermore, the analysis unit can apply the most suitable analysis method by referring to relevant literature related to the query. In addition, the analysis unit can automatically improve the accuracy of its analysis based on relevant literature related to the query. This enables the provision of appropriate analysis based on relevant literature. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevant literature data for the query into the AI ​​and have the AI ​​perform the analysis.

[0052] The generation unit can adjust the level of detail in the response based on the importance of the inquiry during generation. For example, the generation unit can generate a detailed response for important inquiries. It can also generate a concise response for typical inquiries. Furthermore, it can generate a concise response for urgent inquiries. This ensures that appropriate responses are provided according to the importance of the inquiry. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input inquiry importance data into the generation AI and have the generation AI adjust the level of detail in the response.

[0053] The generation unit can apply different generation algorithms depending on the category of the inquiry during generation. For example, the generation unit can apply a detailed generation algorithm for billing-related inquiries. It can also apply a concise generation algorithm for technical support-related inquiries. Furthermore, it can apply a standard generation algorithm for general inquiries. This ensures that appropriate answers are provided according to the category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the category data of the inquiry into the generation AI and have the generation AI execute the application of the generation algorithm.

[0054] The generation unit can determine the priority of responses based on the submission date of the inquiry during the generation process. For example, the generation unit can prioritize responses to recently submitted inquiries. It can also postpone responses to inquiries submitted in the past. Furthermore, the generation unit can automatically determine the priority of responses based on the submission date. This allows for the provision of appropriate responses based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input inquiry submission date data into a generation AI and have the generation AI perform the determination of response priority.

[0055] The generation unit can adjust the order of responses based on the relevance of the inquiry content during generation. For example, the generation unit can prioritize responses to highly relevant inquiries. It can also postpone responses to less relevant inquiries. Furthermore, the generation unit can automatically adjust the order of responses based on the relevance of the inquiry content. This enables the provision of appropriate responses based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input inquiry relevance data into a generation AI and have the generation AI perform the adjustment of the response order.

[0056] The service provider can select the optimal service delivery method by referring to the user's past inquiry history at the time of delivery. For example, the service provider can prioritize suggesting service delivery methods that the user has frequently used in the past. Furthermore, the service provider can prioritize providing relevant information based on the user's past inquiry content. In addition, the service provider can suggest the optimal service delivery method for a specific time period based on the user's past inquiry history. This allows for the provision of the optimal service delivery method based on past history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past inquiry history data into AI and have the AI ​​select the optimal service delivery method.

[0057] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can suggest a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can suggest a delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the delivery unit can suggest a concise and highly visible delivery method. This allows for the provision of the optimal delivery method based on device information. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0058] The storage unit can adjust the level of detail in the logs based on the category of the inquiry content when saving. For example, the storage unit can save detailed logs for important inquiries. For regular inquiries, it can save concise logs. Furthermore, for urgent inquiries, it can save logs that summarize the key points. This provides appropriate log saving based on category. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the category data of the inquiry content into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the logs.

[0059] The storage unit can weight logs based on when the inquiry content was submitted. For example, the storage unit can prioritize saving recently submitted inquiries. It can also postpone saving older inquiries. Furthermore, the storage unit can automatically weight logs based on submission time. This ensures appropriate log saving based on submission time. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input inquiry submission time data into AI and have the AI ​​perform log weighting.

[0060] The storage unit can adjust the order of logs based on the relevance of the query content during storage. For example, the storage unit can prioritize saving highly relevant query content. It can also postpone saving less relevant query content. Furthermore, the storage unit can automatically adjust the order of logs based on the relevance of the query content. This provides appropriate log saving based on relevance. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input query relevance data into AI and have the AI ​​perform the log order adjustment.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception department can prioritize suggesting identity verification methods that the user has frequently used in the past. Furthermore, the reception department can automatically generate relevant questions based on the user's past inquiries. In addition, the reception department can suggest the most suitable reception method for a specific time period based on the user's past inquiry history. This allows for the provision of the most suitable reception method based on past history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past inquiry history into an AI and have the AI ​​select the most suitable reception method.

[0063] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between query content during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on related query content. Furthermore, the analysis unit can analyze the interrelationships between query content and apply the most appropriate analysis method. In addition, the analysis unit can automatically improve the accuracy of its analysis based on the interrelationships between query content. This enables the provision of appropriate analysis based on interrelationships. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input interrelationship data of query content into AI and have the AI ​​perform the analysis accuracy improvement.

[0064] The service provider can select the optimal service delivery method by referring to the user's past inquiry history at the time of delivery. For example, the service provider can prioritize suggesting service delivery methods that the user has frequently used in the past. Furthermore, the service provider can prioritize providing relevant information based on the user's past inquiry content. In addition, the service provider can suggest the optimal service delivery method for a specific time period based on the user's past inquiry history. This allows for the provision of the optimal service delivery method based on past history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past inquiry history data into AI and have the AI ​​select the optimal service delivery method.

[0065] The reception desk can filter information based on the user's current situation and areas of interest during the reception process. For example, when a user enters their current situation, the reception desk can automatically generate relevant questions. Furthermore, the reception desk can prioritize displaying relevant information based on the user's areas of interest. In addition, the reception desk can suggest the most appropriate reception method according to the user's current situation. This enables appropriate responses tailored to the user's situation and interests. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current situation and areas of interest into an AI and have the AI ​​perform the filtering.

[0066] The analysis unit can perform analysis while considering the attribute information of the person submitting the inquiry. For example, the analysis unit can apply the most suitable analysis method based on the age group of the submitter. Furthermore, the analysis unit can apply the most suitable analysis method based on the gender of the submitter. In addition, the analysis unit can apply the most suitable analysis method based on the occupation of the submitter. This allows for the provision of appropriate analysis based on the submitter's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the submitter's attribute information data into AI and have the AI ​​perform the analysis.

[0067] The storage unit can weight logs based on when the inquiry content was submitted. For example, the storage unit can prioritize saving recently submitted inquiries. It can also postpone saving older inquiries. Furthermore, the storage unit can automatically weight logs based on submission time. This ensures appropriate log saving based on submission time. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input inquiry submission time data into AI and have the AI ​​perform log weighting.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk verifies the user's identity and receives the inquiry. The reception desk can verify the user's identity using methods such as ID verification, password authentication, or biometric authentication. The reception desk can also receive the inquiry in various formats, such as text, audio, or images. Step 2: The summarization unit uses AI to summarize the information received by the reception unit. The summarization unit can perform summaries based, for example, on the length of the text or the importance of the information being summarized. Step 3: The analysis unit uses AI to analyze customer attribute information based on the information summarized by the summarization unit. The analysis unit can perform the analysis using methods such as data mining, statistical analysis, and machine learning. Step 4: The generation unit uses a generation AI to generate answers based on the information analyzed by the analysis unit. The generation unit can generate answers using methods such as template-based generation or natural language generation. Step 5: The providing unit provides the answers generated by the generating unit. The providing unit can provide the answers in various ways, such as real-time provision or batch provision. Step 6: The storage unit logs the responses provided by the provision unit. The storage unit can save the logs using methods such as database storage or file storage.

[0070] (Example of form 2) The call center system according to an embodiment of the present invention is a system that eliminates IVR and realizes simple inquiry handling. With this call center system, the incoming customer only needs to provide "identity verification + inquiry content". The receiving AI summarizes the content, reads the age group, tone of voice and emotion, and the generating AI analyzes it while taking into account the customer's mood, etc. This enables immediate responses tailored to each customer's condition, reduces waiting times, and realizes speedy customer service. In addition, customer inquiries are logged and stored according to pre-classified categories, so the AI ​​can handle everything from answering the call to inquiry, confirmation, answering, and log saving in a seamless manner. This results in significant economies of scale and contributes to avoiding customer harassment problems. For example, the incoming customer provides "identity verification + inquiry content". At this time, they provide information such as "My name is Yamada Taro, and the inquiry is to confirm the invoice". Next, the receiving AI summarizes this information and reads the age group, tone of voice and emotion. For example, if the voice is that of a young man, it will be classified as "young male", and if the tone is calm, it will be classified as "calm". Next, the generating AI analyzes this information and generates responses tailored to each customer's condition. For example, a young male customer with a calm demeanor will receive a quick and concise response. In this way, waiting times are reduced, and speedy customer service is achieved. Furthermore, customer inquiries are logged and stored according to pre-classified categories. For example, an inquiry about "checking an invoice" is classified under the "billing-related" category and stored as a log. This makes it easy to look up and check information later. This system allows the AI ​​to handle everything from answering calls to inquiries, confirmations, responses, and log storage in a seamless manner, resulting in significant economies of scale. It also contributes to avoiding customer harassment issues and reducing employee turnover. For example, by having the AI ​​handle inquiries, emotional interactions can be avoided, reducing operator stress. Thus, this invention eliminates IVR in call centers and enables simple inquiry handling, achieving both cost optimization and improved customer satisfaction.This allows the call center system to respond quickly and accurately to customer inquiries, thereby improving customer satisfaction.

[0071] The call center system according to this embodiment comprises a reception unit, a summarization unit, an analysis unit, a generation unit, a provision unit, and a storage unit. The reception unit receives identity verification and inquiry content. The reception unit can perform identity verification using methods such as ID verification, password authentication, and biometric authentication. The reception unit can also receive inquiry content in the form of text, voice, images, etc. The summarization unit uses AI to summarize the information received by the reception unit. The summarization unit can perform summarization based on, for example, the length of the text or the importance of the information to be summarized. The analysis unit uses AI to analyze customer attribute information based on the information summarized by the summarization unit. The analysis unit can perform analysis using methods such as data mining, statistical analysis, and machine learning. The generation unit uses generation AI to generate answers based on the information analyzed by the analysis unit. The generation unit can generate answers using methods such as template-based generation and natural language generation. The provision unit provides the answers generated by the generation unit. The provision unit can provide answers using methods such as real-time provision and batch provision. The storage unit logs the responses provided by the provision unit. The storage unit can save the logs by methods such as database storage or file storage. This allows the call center system according to the embodiment to handle everything from identity verification to summarizing, analyzing, generating, providing, and logging inquiries. For example, the reception unit verifies the ID and receives the inquiry in text format. The summarization unit summarizes the received text and extracts important information. The analysis unit analyzes the customer's age group, tone of voice, and emotions based on the summarized information. The generation unit generates a response that matches the customer's condition based on the analyzed information. The provision unit provides the generated response in real time, and the storage unit saves the provided response in a database. This enables the call center system to respond quickly and accurately to customer inquiries and improve customer satisfaction.

[0072] The reception desk handles identity verification and receives inquiries. The reception desk can verify identity using methods such as ID verification, password authentication, and biometric authentication. Specifically, ID verification involves the user entering a pre-registered ID and password. Password authentication involves the user entering a password they have set, which the system then matches against a database. Biometric authentication uses technologies such as fingerprint recognition, facial recognition, and voice recognition to verify the user's biometric information. This allows the reception desk to perform identity verification quickly while ensuring high security. Furthermore, the reception desk can accept inquiries in various formats, including text, audio, and images. For example, in text format, it accepts content entered by the user in a chat box; in audio format, it converts what the user says over the phone into text using speech recognition technology; and in image format, it analyzes images sent by the user to extract the inquiry content. This allows the reception desk to handle diverse inquiry formats and improve user convenience. Additionally, the reception desk can temporarily store received inquiries and smoothly pass them on to the next processing step. This improves the overall efficiency of the system and reduces user waiting times.

[0073] The summarization unit uses AI to summarize information received by the reception unit. The summarization unit can perform summaries based, for example, on the length of the text and the importance of the information being summarized. Specifically, it uses natural language processing technology to extract important keywords and phrases from long texts and generate a summary. Because the AI ​​has been trained on a large amount of text data beforehand and possesses the ability to understand context and meaning, it can accurately extract important information. For example, if a user submits a long complaint, the summarization unit can extract the main issues and requirements from the complaint and summarize them into a short summary. Similarly, in the case of audio data, it can convert it to text using speech recognition technology and then perform a summary. This allows the summarization unit to efficiently process diverse data formats received from the reception unit and pass them on to the next analysis step. Furthermore, the summarization unit can collect user feedback to continuously improve its AI model in order to enhance the accuracy of the summaries. This enables the summarization unit to consistently provide highly accurate summaries and improve the overall efficiency and accuracy of the system.

[0074] The analysis unit uses AI to analyze customer attribute information based on the information summarized by the summarization unit. The analysis unit can perform analysis using methods such as data mining, statistical analysis, and machine learning. Specifically, it extracts attribute information such as customer age group, gender, region, and past inquiry history based on the summarized text data. The AI ​​statistically analyzes this data to predict customer behavior patterns and preferences. For example, by analyzing past inquiry history, it can identify what problems a particular customer frequently faces and predict future inquiry trends. It can also perform customer sentiment analysis. The AI ​​reads emotions from text data and determines the customer's current emotional state. This allows the analysis unit to understand not only customer attribute information but also their emotional state, providing information for more appropriate responses. Furthermore, the analysis unit can segment customers based on these analysis results. Customers are grouped based on specific attributes and behavior patterns, and optimal countermeasures are proposed for each group. This allows the analysis unit to build a foundation for responding to diverse customer needs and providing personalized services.

[0075] The generation unit uses generation AI to generate responses based on information analyzed by the analysis unit. The generation unit can generate responses using methods such as template-based generation and natural language generation. Specifically, the generation AI generates the optimal response based on customer attribute information and emotional state provided by the analysis unit. In template-based generation, pre-prepared response templates are used and customized according to the customer's specific situation. In natural language generation, the AI ​​understands the context and generates natural-sounding sentences. For example, if a customer inquires about a product defect, the generation AI analyzes the details of the defect and generates a response that proposes an appropriate solution. The generation AI can also consider the customer's emotional state and generate responses using appropriate tone and expression. This allows the generation unit to provide customers with quick and accurate responses, improving customer satisfaction. Furthermore, the generation unit can evaluate the quality of the generated responses and make corrections as needed. For example, if a generated response is inappropriate, the AI ​​analyzes the cause and reflects it in the next generation. This allows the generation unit to consistently provide high-quality responses.

[0076] The service provider delivers the answers generated by the generation provider. The service provider can deliver answers using methods such as real-time delivery or batch delivery. Specifically, real-time delivery enables rapid response by immediately sending generated answers to customers. For example, answers can be provided immediately after a customer makes an inquiry via a chatbot or voice assistant. Batch delivery generates answers to multiple inquiries at once and delivers them all at a set time. This allows the service provider to efficiently handle a large volume of inquiries. Furthermore, the service provider can flexibly select the method of delivering answers according to customer needs. For example, answers can be delivered using the method most convenient for the customer, such as email, SMS, or push notification. In addition, the service provider can monitor the receipt status of the delivered answers and customer reactions, and follow up as needed. This allows the service provider to respond to customers quickly and appropriately, improving customer satisfaction.

[0077] The storage unit logs the responses provided by the service provider. The storage unit can save logs using methods such as database storage or file storage. Specifically, it records detailed information such as the content of the response provided, the date and time of provision, and the customer's response, and stores it in a database. This allows the storage unit to centrally manage past inquiry and response histories. For example, if a customer makes another inquiry, the storage unit can refer to the past response history stored in the storage unit to provide a quick and accurate response. The storage unit can also analyze the stored data to improve the system and enhance services. For example, by analyzing past inquiry data, it can identify common inquiries and problems, leading to improvements in FAQs and response processes. Furthermore, the storage unit can implement measures such as encryption and access restrictions to ensure data security. This allows the storage unit to achieve efficient data management while protecting customer privacy.

[0078] The analysis unit can analyze the customer's age group, tone of voice, and emotions. For example, the analysis unit can analyze the tone and speed of the customer's voice to estimate their age group. It can also analyze the customer's way of speaking and word choice to classify their tone of voice. Furthermore, the analysis unit can analyze the emotions in the customer's voice to estimate emotions such as positive, negative, or neutral. This allows for more appropriate responses by analyzing customer attribute information in detail. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input customer voice data into AI and have the AI ​​perform the analysis of age group, tone of voice, and emotions.

[0079] The generation unit can generate responses that are appropriate to the customer's condition. For example, the generation unit can generate appropriate responses based on the customer's age group, tone of voice, and emotions. For example, it can provide concise and easy-to-understand responses to younger customers and polite and detailed responses to older customers. The generation unit can also generate positive or negative responses depending on the customer's emotions. This allows for the provision of appropriate responses according to the customer's state. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input customer attribute information into a generation AI and have the generation AI perform the generation of responses.

[0080] The storage unit can log and save inquiry content according to pre-classified categories. For example, the storage unit can classify inquiry content into categories such as "billing-related," "technical support," and "general inquiries," and save logs according to each category. This allows for efficient management of inquiry content and facilitates later retrieval and verification. Some or all of the above-described processes in the storage unit may be performed using AI, or they may not. For example, the storage unit can input inquiry content into AI and have the AI ​​perform category classification and log saving.

[0081] The service provider can provide the generated answers immediately. For example, it can provide answers in real time. For example, it can provide the generated answers immediately after a customer makes an inquiry. The service provider can also provide answers in batch processing. For example, it can provide answers in batches at regular time intervals. This enables rapid customer response. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated answers into the AI ​​and have the AI ​​provide the answers.

[0082] The reception desk can estimate the user's emotions and adjust the identity verification method based on the estimated emotions. For example, if the user is nervous, the reception desk can verify their identity using simple questions. If the user is relaxed, the reception desk can verify their identity using more detailed questions. Furthermore, if the user is in a hurry, the reception desk can quickly verify their identity using voice recognition. This enables flexible identity verification that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjust the identity verification method.

[0083] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception department can prioritize suggesting identity verification methods that the user has frequently used in the past. Furthermore, the reception department can automatically generate relevant questions based on the user's past inquiries. In addition, the reception department can suggest the most suitable reception method for a specific time period based on the user's past inquiry history. This allows for the provision of the most suitable reception method based on past history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past inquiry history into an AI and have the AI ​​select the most suitable reception method.

[0084] The reception desk can filter information based on the user's current situation and areas of interest during the reception process. For example, when a user enters their current situation, the reception desk can automatically generate relevant questions. Furthermore, the reception desk can prioritize displaying relevant information based on the user's areas of interest. In addition, the reception desk can suggest the most appropriate reception method according to the user's current situation. This enables appropriate responses tailored to the user's situation and interests. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current situation and areas of interest into an AI and have the AI ​​perform the filtering.

[0085] The reception desk can estimate the user's emotions and determine the priority of inquiries based on those emotions. For example, if the user is stressed, the reception desk can prioritize important inquiries. If the user is relaxed, the reception desk can prioritize normal inquiries. Furthermore, if the user is in a hurry, the reception desk can prioritize urgent inquiries. This enables prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​perform emotion estimation and inquiry prioritization.

[0086] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize processing inquiries related to that region. The reception desk can also prioritize displaying relevant information based on the user's geographical location. Furthermore, the reception desk can suggest the most appropriate reception method according to the user's current location. This enables appropriate responses based on geographical information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​determine the priority of highly relevant inquiries.

[0087] The reception desk can analyze a user's social media activity and receive relevant inquiries upon receiving a request. For example, the reception desk can automatically generate relevant inquiry content from the user's social media activity. Furthermore, the reception desk can prioritize displaying relevant information based on the user's social media activity. In addition, the reception desk can analyze the user's social media activity and propose the optimal reception method. This enables appropriate responses based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user social media activity data into AI and have the AI ​​generate relevant inquiries and select the reception method.

[0088] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is nervous, the summarization unit can provide a concise and clear summary. If the user is relaxed, the summarization unit can provide a detailed summary. Furthermore, if the user is in a hurry, the summarization unit can provide a summary that gets straight to the point. This allows for the provision of an appropriate summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of the summary's presentation.

[0089] The summarization unit can adjust the level of detail in the summary based on the importance of the inquiry content during summary generation. For example, the summarization unit can provide a detailed summary for important inquiries. It can also provide a concise summary for ordinary inquiries. Furthermore, it can provide a summary that gets straight to the point for urgent inquiries. This ensures that an appropriate summary is provided according to the importance of the inquiry content. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input inquiry importance data into the AI ​​and have the AI ​​adjust the level of detail in the summary.

[0090] The summarization unit can apply different summarization algorithms depending on the category of the inquiry when generating a summary. For example, the summarization unit can apply a detailed summarization algorithm for billing-related inquiries. It can also apply a concise summarization algorithm for technical support-related inquiries. Furthermore, it can apply a standard summarization algorithm for general inquiries. This ensures that appropriate summaries are provided according to the category. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input the category data of the inquiry into the AI ​​and have the AI ​​apply the summarization algorithm.

[0091] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is nervous, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a detailed summary. Furthermore, if the user is in a hurry, the summarization unit can provide a brief summary. This ensures that an appropriate summary is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and summary length adjustment.

[0092] The summarization unit can determine the priority of summaries based on when the inquiry content was submitted during the summarization process. For example, the summarization unit can prioritize summarizing recently submitted inquiries. It can also postpone summarizing inquiries submitted in the past. Furthermore, the summarization unit can automatically determine the priority of summaries based on the submission date. This allows for the provision of appropriate summaries based on the submission date. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input inquiry submission date data into the AI ​​and have the AI ​​perform the determination of summary priority.

[0093] The summarization unit can adjust the order of summaries based on the relevance of the query content during summary generation. For example, the summarization unit can prioritize summarizing highly relevant query content. It can also postpone summarizing less relevant query content. Furthermore, the summarization unit can automatically adjust the order of summaries based on the relevance of the query content. This allows for the provision of appropriate summaries based on relevance. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input query relevance data into AI and have the AI ​​perform the adjustment of the summary order.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is tense, the analysis unit can apply concise analysis criteria. If the user is relaxed, the analysis unit can apply detailed analysis criteria. Furthermore, if the user is in a hurry, the analysis unit can apply rapid analysis criteria. This allows for the provision of an appropriate analysis tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of analysis criteria.

[0095] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between query content during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on related query content. Furthermore, the analysis unit can analyze the interrelationships between query content and apply the most appropriate analysis method. In addition, the analysis unit can automatically improve the accuracy of its analysis based on the interrelationships between query content. This enables the provision of appropriate analysis based on interrelationships. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input interrelationship data of query content into AI and have the AI ​​perform the analysis accuracy improvement.

[0096] The analysis unit can perform analysis while considering the attribute information of the person submitting the inquiry. For example, the analysis unit can apply the most suitable analysis method based on the age group of the submitter. Furthermore, the analysis unit can apply the most suitable analysis method based on the gender of the submitter. In addition, the analysis unit can apply the most suitable analysis method based on the occupation of the submitter. This allows for the provision of appropriate analysis based on the submitter's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the submitter's attribute information data into AI and have the AI ​​perform the analysis.

[0097] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can prioritize displaying important analysis results. If the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results. This allows for the provision of appropriate analysis results tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjust the display order of the analysis results.

[0098] The analysis unit can perform analysis while considering the geographical distribution of the inquiry content. For example, the analysis unit can improve the accuracy of the analysis based on geographically related inquiry content. Furthermore, the analysis unit can analyze the geographical distribution of the inquiry content and apply the optimal analysis method. In addition, the analysis unit can automatically improve the accuracy of the analysis based on the geographical distribution of the inquiry content. This enables the provision of appropriate analysis based on geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data of the inquiry content into the AI ​​and have the AI ​​perform the analysis.

[0099] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the query during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature. Furthermore, the analysis unit can apply the most suitable analysis method by referring to relevant literature related to the query. In addition, the analysis unit can automatically improve the accuracy of its analysis based on relevant literature related to the query. This enables the provision of appropriate analysis based on relevant literature. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevant literature data for the query into the AI ​​and have the AI ​​perform the analysis.

[0100] The generation unit can estimate the user's emotions and adjust the expression of the generated response based on the estimated user emotions. For example, if the user is nervous, the generation unit can generate a concise and clear response. If the user is relaxed, the generation unit can generate a detailed response. Furthermore, if the user is in a hurry, the generation unit can generate a concise response. This allows for the provision of appropriate responses that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and adjust the expression of the response.

[0101] The generation unit can adjust the level of detail in the response based on the importance of the inquiry during generation. For example, the generation unit can generate a detailed response for important inquiries. It can also generate a concise response for typical inquiries. Furthermore, it can generate a concise response for urgent inquiries. This ensures that appropriate responses are provided according to the importance of the inquiry. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input inquiry importance data into the generation AI and have the generation AI adjust the level of detail in the response.

[0102] The generation unit can apply different generation algorithms depending on the category of the inquiry during generation. For example, the generation unit can apply a detailed generation algorithm for billing-related inquiries. It can also apply a concise generation algorithm for technical support-related inquiries. Furthermore, it can apply a standard generation algorithm for general inquiries. This ensures that appropriate answers are provided according to the category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the category data of the inquiry into the generation AI and have the generation AI execute the application of the generation algorithm.

[0103] The generation unit can estimate the user's emotions and adjust the length of the generated response based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a short, concise response. If the user is relaxed, the generation unit can generate a detailed response. Furthermore, if the user is in a hurry, the generation unit can generate a brief response. This allows for the provision of appropriate responses tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and response length adjustment.

[0104] The generation unit can determine the priority of responses based on the submission date of the inquiry during the generation process. For example, the generation unit can prioritize responses to recently submitted inquiries. It can also postpone responses to inquiries submitted in the past. Furthermore, the generation unit can automatically determine the priority of responses based on the submission date. This allows for the provision of appropriate responses based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input inquiry submission date data into a generation AI and have the generation AI perform the determination of response priority.

[0105] The generation unit can adjust the order of responses based on the relevance of the inquiry content during generation. For example, the generation unit can prioritize responses to highly relevant inquiries. It can also postpone responses to less relevant inquiries. Furthermore, the generation unit can automatically adjust the order of responses based on the relevance of the inquiry content. This enables the provision of appropriate responses based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input inquiry relevance data into a generation AI and have the generation AI perform the adjustment of the response order.

[0106] The service provider can estimate the user's emotions and adjust the way it provides answers based on those emotions. For example, if the user is nervous, the service provider can provide a concise and clear answer. If the user is relaxed, the service provider can provide a detailed answer. Furthermore, if the user is in a hurry, the service provider can provide a concise answer. This ensures that an appropriate service is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of the service provision method.

[0107] The service provider can select the optimal service delivery method by referring to the user's past inquiry history at the time of delivery. For example, the service provider can prioritize suggesting service delivery methods that the user has frequently used in the past. Furthermore, the service provider can prioritize providing relevant information based on the user's past inquiry content. In addition, the service provider can suggest the optimal service delivery method for a specific time period based on the user's past inquiry history. This allows for the provision of the optimal service delivery method based on past history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past inquiry history data into AI and have the AI ​​select the optimal service delivery method.

[0108] The service provider can estimate the user's emotions and adjust the order in which answers are provided based on the estimated emotions. For example, if the user is nervous, the service provider can prioritize providing important answers. If the user is relaxed, the service provider can provide detailed answers. Furthermore, if the user is in a hurry, the service provider can provide concise answers. This allows for the provision of an appropriate order of answers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of the order of answers.

[0109] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can suggest a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can suggest a delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the delivery unit can suggest a concise and highly visible delivery method. This allows for the provision of the optimal delivery method based on device information. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0110] The storage unit can estimate the user's emotions and adjust the log storage method based on the estimated emotions. For example, if the user is tense, the storage unit can apply a concise log storage method. If the user is relaxed, the storage unit can apply a detailed log storage method. Furthermore, if the user is in a hurry, the storage unit can apply a rapid log storage method. This allows for the provision of an appropriate log storage method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of the log storage method.

[0111] The storage unit can adjust the level of detail in the logs based on the category of the inquiry content when saving. For example, the storage unit can save detailed logs for important inquiries. For regular inquiries, it can save concise logs. Furthermore, for urgent inquiries, it can save logs that summarize the key points. This provides appropriate log saving based on category. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input the category data of the inquiry content into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the logs.

[0112] The storage unit can estimate the user's emotions and determine the priority of log saving based on the estimated emotions. For example, if the user is stressed, the storage unit can prioritize saving important logs. If the user is relaxed, the storage unit can save detailed logs. Furthermore, if the user is in a hurry, the storage unit can save concise logs. This provides an appropriate log saving priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and determine the priority of log saving.

[0113] The storage unit can weight logs based on when the inquiry content was submitted. For example, the storage unit can prioritize saving recently submitted inquiries. It can also postpone saving older inquiries. Furthermore, the storage unit can automatically weight logs based on submission time. This ensures appropriate log saving based on submission time. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input inquiry submission time data into AI and have the AI ​​perform log weighting.

[0114] The storage unit can adjust the order of logs based on the relevance of the query content during storage. For example, the storage unit can prioritize saving highly relevant query content. It can also postpone saving less relevant query content. Furthermore, the storage unit can automatically adjust the order of logs based on the relevance of the query content. This provides appropriate log saving based on relevance. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input query relevance data into AI and have the AI ​​perform the log order adjustment.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The reception department can analyze a user's past inquiry history and select the most suitable reception method. For example, the reception department can prioritize suggesting identity verification methods that the user has frequently used in the past. Furthermore, the reception department can automatically generate relevant questions based on the user's past inquiries. In addition, the reception department can suggest the most suitable reception method for a specific time period based on the user's past inquiry history. This allows for the provision of the most suitable reception method based on past history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past inquiry history into an AI and have the AI ​​select the most suitable reception method.

[0117] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is nervous, the summarization unit can provide a concise and clear summary. If the user is relaxed, the summarization unit can provide a detailed summary. Furthermore, if the user is in a hurry, the summarization unit can provide a summary that gets straight to the point. This allows for the provision of an appropriate summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of the summary's presentation.

[0118] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between query content during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on related query content. Furthermore, the analysis unit can analyze the interrelationships between query content and apply the most appropriate analysis method. In addition, the analysis unit can automatically improve the accuracy of its analysis based on the interrelationships between query content. This enables the provision of appropriate analysis based on interrelationships. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input interrelationship data of query content into AI and have the AI ​​perform the analysis accuracy improvement.

[0119] The generation unit can estimate the user's emotions and adjust the expression of the generated response based on the estimated user emotions. For example, if the user is nervous, the generation unit can generate a concise and clear response. If the user is relaxed, the generation unit can generate a detailed response. Furthermore, if the user is in a hurry, the generation unit can generate a concise response. This allows for the provision of appropriate responses that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and adjust the expression of the response.

[0120] The service provider can select the optimal service delivery method by referring to the user's past inquiry history at the time of delivery. For example, the service provider can prioritize suggesting service delivery methods that the user has frequently used in the past. Furthermore, the service provider can prioritize providing relevant information based on the user's past inquiry content. In addition, the service provider can suggest the optimal service delivery method for a specific time period based on the user's past inquiry history. This allows for the provision of the optimal service delivery method based on past history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past inquiry history data into AI and have the AI ​​select the optimal service delivery method.

[0121] The storage unit can estimate the user's emotions and adjust the log storage method based on the estimated emotions. For example, if the user is tense, the storage unit can apply a concise log storage method. If the user is relaxed, the storage unit can apply a detailed log storage method. Furthermore, if the user is in a hurry, the storage unit can apply a rapid log storage method. This allows for the provision of an appropriate log storage method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of the log storage method.

[0122] The reception desk can filter information based on the user's current situation and areas of interest during the reception process. For example, when a user enters their current situation, the reception desk can automatically generate relevant questions. Furthermore, the reception desk can prioritize displaying relevant information based on the user's areas of interest. In addition, the reception desk can suggest the most appropriate reception method according to the user's current situation. This enables appropriate responses tailored to the user's situation and interests. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current situation and areas of interest into an AI and have the AI ​​perform the filtering.

[0123] The analysis unit can perform analysis while considering the attribute information of the person submitting the inquiry. For example, the analysis unit can apply the most suitable analysis method based on the age group of the submitter. Furthermore, the analysis unit can apply the most suitable analysis method based on the gender of the submitter. In addition, the analysis unit can apply the most suitable analysis method based on the occupation of the submitter. This allows for the provision of appropriate analysis based on the submitter's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the submitter's attribute information data into AI and have the AI ​​perform the analysis.

[0124] The service provider can estimate the user's emotions and adjust the order in which answers are provided based on the estimated emotions. For example, if the user is nervous, the service provider can prioritize providing important answers. If the user is relaxed, the service provider can provide detailed answers. Furthermore, if the user is in a hurry, the service provider can provide concise answers. This allows for the provision of an appropriate order of answers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI and have the AI ​​perform emotion estimation and adjustment of the order of answers.

[0125] The storage unit can weight logs based on when the inquiry content was submitted. For example, the storage unit can prioritize saving recently submitted inquiries. It can also postpone saving older inquiries. Furthermore, the storage unit can automatically weight logs based on submission time. This ensures appropriate log saving based on submission time. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input inquiry submission time data into AI and have the AI ​​perform log weighting.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The reception desk verifies the user's identity and receives the inquiry. The reception desk can verify the user's identity using methods such as ID verification, password authentication, or biometric authentication. The reception desk can also receive the inquiry in various formats, such as text, audio, or images. Step 2: The summarization unit uses AI to summarize the information received by the reception unit. The summarization unit can perform summaries based, for example, on the length of the text or the importance of the information being summarized. Step 3: The analysis unit uses AI to analyze customer attribute information based on the information summarized by the summarization unit. The analysis unit can perform the analysis using methods such as data mining, statistical analysis, and machine learning. Step 4: The generation unit uses a generation AI to generate answers based on the information analyzed by the analysis unit. The generation unit can generate answers using methods such as template-based generation or natural language generation. Step 5: The providing unit provides the answers generated by the generating unit. The providing unit can provide the answers in various ways, such as real-time provision or batch provision. Step 6: The storage unit logs the responses provided by the provision unit. The storage unit can save the logs using methods such as database storage or file storage.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, summarization unit, analysis unit, generation unit, provision unit, and storage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and performs ID verification and password authentication. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12 and summarizes the received information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the summarized information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated response. The storage unit is implemented by the database 24 of the data processing unit 12 and logs and saves the provided response. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, summarization unit, analysis unit, generation unit, provision unit, and storage unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and performs ID verification and password authentication. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the received information. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the summarized information. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated response. The storage unit is implemented by the database 24 of the data processing unit 12 and logs and saves the provided response. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, summarization unit, analysis unit, generation unit, provision unit, and storage unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and performs ID verification and password authentication. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12 and summarizes the received information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the summarized information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides the generated response. The storage unit is implemented by the database 24 of the data processing unit 12 and logs and saves the provided response. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the reception unit, summarization unit, analysis unit, generation unit, provision unit, and storage unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and performs ID verification and password authentication. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12 and summarizes the received information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the summarized information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a response based on the analyzed information. The provision unit is implemented by the speaker 240 of the robot 414 and provides the generated response. The storage unit is implemented by the database 24 of the data processing unit 12 and logs and saves the provided response. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0191] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) The reception desk handles identity verification and inquiries, A summarization unit that summarizes the information received by the reception unit, An analysis unit analyzes customer attribute information based on the information summarized by the summarization unit, A generation unit that generates an answer based on the information analyzed by the analysis unit, A providing unit that provides the answer generated by the generation unit, A storage unit that logs and stores the answers provided by the aforementioned providing unit, A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the customer's age group, tone of voice, and emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate responses tailored to the customer's condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned storage unit is The inquiry content is saved as a log according to pre-classified categories. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated answer immediately. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the identity verification method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is During registration, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the inquiries it receives based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving an inquiry, the system prioritizes processing inquiries that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and receives related inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the query. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the query content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, When generating summaries, the priority of summaries is determined based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the query content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, consider the interrelationships between query content to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the attribute information of the person who submitted the inquiry will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the geographical distribution of the query content will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we refer to relevant literature related to the query to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts how the generated responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the level of detail in the response is adjusted based on the importance of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, different generation algorithms are applied depending on the category of the query content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and adjusts the length of the response generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating the response, the priority of the answer is determined based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the order of responses is adjusted based on the relevance of the query content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which responses are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned storage unit is The system estimates the user's emotions and adjusts the log storage method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned storage unit is When saving, adjust the log detail level based on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned storage unit is The system estimates the user's emotions and determines the priority of log retention based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned storage unit is When saving, the logs are weighted based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned storage unit is When saving, the log order is adjusted based on the relevance of the query content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk handles identity verification and inquiries, A summarization unit that summarizes the information received by the reception unit, An analysis unit analyzes customer attribute information based on the information summarized by the summarization unit, A generation unit that generates an answer based on the information analyzed by the analysis unit, A providing unit that provides the answer generated by the generation unit, A storage unit that logs and stores the answers provided by the aforementioned providing unit, A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the customer's age group, tone of voice, and emotions. The system according to feature 1.

3. The generating unit is Generate responses tailored to the customer's condition. The system according to feature 1.

4. The aforementioned storage unit is Inquiry content is saved as a log according to pre-classified categories. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated answer immediately. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the identity verification method based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past inquiry history and select the most suitable method of handling inquiries. The system according to feature 1.

8. The aforementioned reception unit is During registration, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the inquiries it receives based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving an inquiry, the system prioritizes processing inquiries that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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