System

Abstract

An object of the system according to the embodiment is to realize an unmanned automatic response service based on various customer records and transaction records.SOLUTION: A system includes a data analysis unit, an answer generation unit, a telephone answering unit, and an Internet answering unit. The data analysis unit analyzes customer records and transaction records. The answer generation unit generates an answer on the basis of a result analyzed by the data analysis unit. The telephone answering unit provides the answer generated by the answer generation unit through a telephone. The Internet response unit provides the answer generated by the answer generation unit through the Internet.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to realize an unmanned automatic response service based on a wide variety of customer records and transaction records.

[0005] The system according to the embodiment aims to realize an unmanned automated response service based on a wide variety of customer records and transaction records. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, an answer generation unit, a telephone response unit, and an internet response unit. The data analysis unit analyzes customer records and transaction records. The answer generation unit generates an answer based on the results of the analysis by the data analysis unit. The telephone response unit provides the answer generated by the answer generation unit over the telephone. The internet response unit provides the answer generated by the answer generation unit over the internet. [Effects of the Invention]

[0007] The system according to the embodiment can realize an unmanned automated response service based on a wide variety of customer records and transaction records. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automated guidance system according to an embodiment of the present invention utilizes generative AI to provide automated telephone and internet guidance services for companies (banks, insurance companies, automobile companies, etc.). This system automatically provides appropriate answers to customer inquiries based on a wide variety of data, such as customer records and transaction records. This allows the automated guidance system to respond to customer inquiries quickly and accurately.

[0029] The automated guidance system according to the embodiment includes a data analysis unit, a response generation unit, a telephone response unit, and an internet response unit. The data analysis unit analyzes customer records and transaction records. For example, the data analysis unit analyzes a customer's purchase history and inquiry history to understand the customer's behavioral patterns. The data analysis unit can also analyze transaction dates and times and transaction details to understand the customer's transaction trends. The data analysis unit can also analyze data using statistical analysis and machine learning algorithms. For example, the data analysis unit can predict future transactions based on the customer's past transaction data. The response generation unit generates responses based on the results of the analysis by the data analysis unit. For example, the response generation unit generates appropriate responses to customer inquiries using natural language generation technology. The response generation unit can also perform template-based response generation. For example, the answer generation unit generates responses to customer inquiries based on a prepared answer template. The telephone response unit provides the responses generated by the response generation unit over the telephone. For example, the telephone response unit accepts customer inquiries using speech recognition technology and provides responses using speech synthesis technology. The telephone response unit can also analyze the customer's tone of voice and speaking style to provide a response that corresponds to the customer's emotional state. For example, if the customer is nervous, the telephone response unit can provide a response that relaxes the customer. The internet response unit provides the response generated by the response generation unit via the internet. For example, the internet response unit can provide an answer to a customer's inquiry using a chatbot. The internet response unit can also provide an answer via a web interface. For example, the internet response unit can provide an appropriate answer when a customer makes an inquiry via a website. This allows the automated guidance system according to the embodiment to respond to customer inquiries quickly and accurately. For example, when a customer makes an inquiry by telephone, the customer can obtain the necessary information without the intervention of an operator. Also, when a customer makes an inquiry via a website, the customer can obtain an appropriate answer in real time.

[0030] The data analysis unit analyzes the inquiry history, predicts the content of the next inquiry, and can prepare a response in advance. For example, the data analysis unit allows the generation AI to analyze a customer's past inquiry history and predict the content of the next inquiry. For example, for a customer who has frequently made balance inquiries in the past, the latest balance information is automatically provided the next time they inquire. The data analysis unit also allows the generation AI to predict the content of the next inquiry based on the customer's inquiry history and prepare a response in advance. For example, for a customer who frequently checks the status of their loan application, the latest application status is automatically provided. The data analysis unit also allows the generation AI to learn customer behavior patterns and predict the content of the next inquiry. For example, for a customer who frequently checks the details of their insurance policy, the policy details are automatically provided the next time they inquire. This allows for a quick response to the next inquiry.

[0031] The data analysis unit learns behavioral patterns and can proactively provide information at the optimal timing. For example, the data analysis unit allows the generation AI to learn customer behavioral patterns and proactively provide information at the optimal timing. For example, if a customer inquires about their balance at the end of each month, the balance information will be automatically provided at the end of the month. The data analysis unit also analyzes customer behavioral patterns and the generation AI will provide information at the optimal timing. For example, to a customer whose insurance contract is due for renewal, the generation AI will automatically send them a notice about the renewal procedure. The data analysis unit also allows the generation AI to learn customer behavioral patterns and proactively provide information. For example, to a customer whose vehicle is due for maintenance, the generation AI will automatically send them a notice about making a maintenance appointment. This allows information to be provided to customers at the optimal timing.

[0032] The data analysis unit can analyze social media activity and provide appropriate answers to related inquiries. For example, the data analysis unit allows the generation AI to analyze a customer's social media activity and provide appropriate answers to related inquiries. For example, if a customer posts a question about insurance on social media, the generation AI automatically answers that question. The data analysis unit also analyzes social media activity and allows the generation AI to provide appropriate answers to related inquiries. For example, if a customer posts about vehicle maintenance, the generation AI provides maintenance appointment information. The data analysis unit also allows the generation AI to analyze a customer's social media activity and provide appropriate answers to related inquiries. For example, if a customer posts about banking services, the generation AI provides information about those services. This makes it possible to provide appropriate answers based on social media activity.

[0033] The data analysis unit can integrate customer data from different companies to enable cross-industry inquiry handling. For example, the generation AI in the data analysis unit integrates customer data from different companies to enable cross-industry inquiry handling. For example, data from a bank and an insurance company can be integrated to allow customers to make inquiries about both services at once. The data analysis unit also integrates customer data from different companies to enable the generation AI to enable cross-industry inquiry handling. For example, data from an automobile company and an insurance company can be integrated to allow customers to make inquiries about vehicle insurance at once. The data analysis unit also integrates customer data from different companies to enable cross-industry inquiry handling. For example, data from a bank and an automobile company can be integrated to allow customers to make inquiries about vehicle loans at once. This makes it possible to handle inquiries across industries by integrating data from different companies.

[0034] The telephone answering unit can also respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, the telephone answering unit uses a generation AI to respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, it can respond to inquiries in multiple languages ​​such as English, Spanish, and Chinese. The telephone answering unit also uses a multilingual generation AI to respond to telephone inquiries in different languages. For example, if a customer makes an inquiry in Japanese, it will provide an answer in Japanese. The telephone answering unit also uses a generation AI to respond to telephone inquiries in different languages ​​and provide a multilingual service. For example, if a customer makes an inquiry in French, it will provide an answer in French. This makes it possible to provide a multilingual automated telephone assistance service.

[0035] The telephone response unit can analyze geographical location information and provide region-specific information. For example, the generation AI analyzes the geographical location information of a customer in the telephone response unit and provides region-specific information. For example, if a customer lives in a specific region, information on bank branches in that region is provided. The generation AI also provides region-specific information based on the customer's geographical location information. For example, if a customer has an insurance policy in a specific region, information on insurance agencies in that region is provided. The generation AI also analyzes the customer's geographical location information and provides region-specific information. For example, if a customer has vehicle maintenance in a specific region, information on maintenance services in that region is provided. This makes it possible to provide region-specific information.

[0036] The internet response unit can analyze website browsing history and predict and provide the next information that will be required. In the internet response unit, for example, the generation AI analyzes a customer's website browsing history and predicts the next information that will be required. For example, if a customer is in the process of purchasing a vehicle, the generation AI automatically guides the customer to the next required documents and procedures. In addition, the internet response unit can predict the next information that will be required based on the customer's website browsing history. For example, if a customer is checking the details of an insurance contract, the generation AI automatically guides the customer to the next required procedures and documents. In addition, the internet response unit can analyze a customer's website browsing history and predict the next information that will be required. For example, if a customer is in the process of applying for a bank loan, the generation AI automatically guides the customer to the next required documents and procedures. This makes it possible to provide the next required information based on the customer's website browsing history.

[0037] The Internet response unit analyzes the input content in real time and can provide appropriate answers even when the input is in progress. For example, the generation AI in the Internet response unit analyzes the input content of a customer in real time and can provide appropriate answers even when the input is in progress. For example, while a customer is entering information for a vehicle purchase procedure, the generation AI automatically guides the customer to the necessary documents and procedures. The Internet response unit also analyzes the input content of a customer in real time and can provide appropriate answers even when the input is in progress. For example, while a customer is entering details of an insurance contract, the generation AI automatically guides the customer to the next necessary procedures and documents. The Internet response unit also analyzes the input content of a customer in real time and can provide appropriate answers even when the input is in progress. For example, while a customer is entering details of a bank loan application, the generation AI automatically guides the customer to the necessary documents and procedures. This makes it possible to provide appropriate answers in real time based on the input content of a customer.

[0038] The internet response unit can analyze device information and provide a response optimized for the device. In the internet response unit, for example, the generation AI analyzes the customer's device information and provides a response optimized for the device. For example, a mobile-friendly response is provided for a customer using a smartphone. Also, in the internet response unit, the generation AI analyzes the customer's device information and provides a response optimized for the device. For example, a tablet is provided for a customer using a tablet. Also, in the internet response unit, the generation AI analyzes the customer's device information and provides a response optimized for the device. For example, a desktop is provided for a customer using a desktop. This makes it possible to provide a response optimized for the customer's device.

[0039] The internet response unit operates as a browser extension and can provide appropriate information even outside the website. For example, the generation AI operates as a browser extension for the customer and provides appropriate information even outside the website. For example, when the customer is browsing other websites, relevant information is displayed in a pop-up. The internet response unit also operates as a browser extension and provides appropriate information to the customer even outside the website. For example, when the customer is checking email, relevant information is provided. The internet response unit also operates as a browser extension for the customer and provides appropriate information even outside the website. For example, when the customer is browsing social media, relevant information is provided. This makes it possible to provide appropriate information even outside the website.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The data analysis unit can analyze a customer's purchasing history and suggest related products and services. For example, if a customer has previously purchased products from a specific brand, new products from that brand or related products will be suggested. The data analysis unit can also use the generation AI to predict a customer's preferences and interests based on the customer's purchasing history and make personalized suggestions. For example, if a customer frequently purchases outdoor equipment, it can provide information on new camping equipment and outdoor events. The data analysis unit can also analyze a customer's purchasing history and use the generation AI to make suggestions tailored to the customer's lifestyle. For example, it can suggest health foods and fitness-related services to health-conscious customers. This allows for more personalized suggestions to be made based on the customer's purchasing history.

[0042] The data analysis unit can analyze customer feedback and identify areas for improvement in services. For example, it can analyze feedback provided by customers and identify frequently pointed out problems. The data analysis unit can also enable the generation AI to suggest areas for improvement in services based on customer feedback. For example, if a customer is dissatisfied with a particular function, it can propose improvements to that function. The data analysis unit can also analyze customer feedback and enable the generation AI to propose new services or functions. For example, if a customer requests a new function, it can propose implementation plans for that function. This makes it possible to improve services and make new proposals based on customer feedback.

[0043] The data analysis unit can analyze customer behavior patterns and provide promotions at the optimal timing. For example, if a customer frequently shops online during a specific time period, the generation AI can provide promotions that coincide with that time period. The data analysis unit can also enable the generation AI to predict the optimal timing for promotions based on customer behavior patterns. For example, if a customer tends to shop on weekends, the generation AI can provide promotions that coincide with weekends. The data analysis unit can also analyze customer behavior patterns and enable the generation AI to provide promotions that match the customer's lifestyle. For example, if a customer tends to purchase fitness-related products, the generation AI can provide fitness-related promotions. This makes it possible to provide promotions at the optimal timing based on the customer's behavior patterns.

[0044] The data analysis unit can analyze a customer's social media activity and suggest relevant marketing campaigns. For example, if a customer mentions a specific brand on social media, the data analysis unit can suggest campaigns for that brand. The data analysis unit can also enable the generation AI to suggest marketing campaigns tailored to the customer's interests based on social media activity. For example, if a customer frequently posts about travel, the data analysis unit can suggest travel-related campaigns. The data analysis unit can also analyze a customer's social media activity and enable the generation AI to suggest marketing campaigns tailored to the customer's lifestyle. For example, if a customer posts about being health-conscious, the data analysis unit can suggest health-related campaigns. This makes it possible to suggest more personalized marketing campaigns based on the customer's social media activity.

[0045] The data analysis unit can integrate customer data from different companies to realize cross-industry marketing campaigns. For example, data from a bank and an insurance company can be integrated so that customers can receive campaigns for both services at once. The data analysis unit can also integrate customer data from different companies so that generation AI can realize cross-industry marketing campaigns. For example, data from an automobile company and an insurance company can be integrated so that customers can receive campaigns for vehicle insurance at once. The data analysis unit can also integrate customer data from different companies so that generation AI can realize cross-industry marketing campaigns. For example, data from a bank and an automobile company can be integrated so that customers can receive campaigns for vehicle loans at once. This makes cross-industry marketing campaigns possible by integrating data between different companies.

[0046] The telephone answering unit can also respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, the generation AI can respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, inquiries in multiple languages ​​such as English, Spanish, and Chinese can be handled. The telephone answering unit can also respond to telephone inquiries in different languages ​​using a multilingual generation AI. For example, if a customer makes an inquiry in Japanese, an answer will be provided in Japanese. The telephone answering unit can also respond to telephone inquiries in different languages ​​using the generation AI to provide a multilingual service. For example, if a customer makes an inquiry in French, an answer will be provided in French. This makes it possible to provide a multilingual automated telephone assistance service.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The data analysis department analyzes customer records and transaction records. For example, it analyzes a customer's purchase history and inquiry history to understand their behavioral patterns. It can also analyze transaction dates and times and transaction details to understand a customer's trading trends. It can also analyze the data using statistical analysis and machine learning algorithms to predict future transactions based on the customer's past transaction data. Step 2: The answer generation unit generates answers based on the results of the analysis by the data analysis unit. For example, it uses natural language generation technology to generate appropriate answers to customer inquiries. It also performs template-based answer generation, generating answers to customer inquiries based on answer templates prepared in advance. Step 3: The telephone answering unit provides the answer generated by the answer generating unit over the telephone. For example, it accepts customer inquiries using voice recognition technology and provides answers using voice synthesis technology. It can also analyze the customer's tone of voice and speaking style to provide a response that matches the customer's emotional state. For example, if the customer is nervous, it can provide a response that will relax them. Step 4: The internet response unit provides the answer generated by the answer generation unit via the internet. For example, it provides an answer to the customer's inquiry using a chatbot. It can also provide the answer via a web interface. For example, it provides an appropriate answer when a customer makes an inquiry via a website.

[0049] (Example 2) The automated guidance system according to an embodiment of the present invention utilizes generative AI to provide automated telephone and internet guidance services for companies (banks, insurance companies, automobile companies, etc.). This system automatically provides appropriate answers to customer inquiries based on a wide variety of data, such as customer records and transaction records. This allows the automated guidance system to respond to customer inquiries quickly and accurately.

[0050] The automated guidance system according to the embodiment includes a data analysis unit, a response generation unit, a telephone response unit, and an internet response unit. The data analysis unit analyzes customer records and transaction records. For example, the data analysis unit analyzes a customer's purchase history and inquiry history to understand the customer's behavioral patterns. The data analysis unit can also analyze transaction dates and times and transaction details to understand the customer's transaction trends. The data analysis unit can also analyze data using statistical analysis and machine learning algorithms. For example, the data analysis unit can predict future transactions based on the customer's past transaction data. The response generation unit generates responses based on the results of the analysis by the data analysis unit. For example, the response generation unit generates appropriate responses to customer inquiries using natural language generation technology. The response generation unit can also perform template-based response generation. For example, the answer generation unit generates responses to customer inquiries based on a prepared answer template. The telephone response unit provides the responses generated by the response generation unit over the telephone. For example, the telephone response unit accepts customer inquiries using speech recognition technology and provides responses using speech synthesis technology. The telephone response unit can also analyze the customer's tone of voice and speaking style to provide a response that corresponds to the customer's emotional state. For example, if the customer is nervous, the telephone response unit can provide a response that relaxes the customer. The internet response unit provides the response generated by the response generation unit via the internet. For example, the internet response unit can provide an answer to a customer's inquiry using a chatbot. The internet response unit can also provide an answer via a web interface. For example, the internet response unit can provide an appropriate answer when a customer makes an inquiry via a website. This allows the automated guidance system according to the embodiment to respond to customer inquiries quickly and accurately. For example, when a customer makes an inquiry by telephone, the customer can obtain the necessary information without the intervention of an operator. Also, when a customer makes an inquiry via a website, the customer can obtain an appropriate answer in real time.

[0051] The data analysis unit analyzes the inquiry history, predicts the content of the next inquiry, and can prepare a response in advance. For example, the data analysis unit allows the generation AI to analyze a customer's past inquiry history and predict the content of the next inquiry. For example, for a customer who has frequently made balance inquiries in the past, the latest balance information is automatically provided the next time they inquire. The data analysis unit also allows the generation AI to predict the content of the next inquiry based on the customer's inquiry history and prepare a response in advance. For example, for a customer who frequently checks the status of their loan application, the latest application status is automatically provided. The data analysis unit also allows the generation AI to learn customer behavior patterns and predict the content of the next inquiry. For example, for a customer who frequently checks the details of their insurance policy, the policy details are automatically provided the next time they inquire. This allows for a quick response to the next inquiry.

[0052] The data analysis unit learns behavioral patterns and can proactively provide information at the optimal timing. For example, the data analysis unit allows the generation AI to learn customer behavioral patterns and proactively provide information at the optimal timing. For example, if a customer inquires about their balance at the end of each month, the balance information will be automatically provided at the end of the month. The data analysis unit also analyzes customer behavioral patterns and the generation AI will provide information at the optimal timing. For example, to a customer whose insurance contract is due for renewal, the generation AI will automatically send them a notice about the renewal procedure. The data analysis unit also allows the generation AI to learn customer behavioral patterns and proactively provide information. For example, to a customer whose vehicle is due for maintenance, the generation AI will automatically send them a notice about making a maintenance appointment. This allows information to be provided to customers at the optimal timing.

[0053] The data analysis unit can use the emotion estimation function to analyze the emotional state of the customer and generate a response that corresponds to the emotion. In the data analysis unit, for example, the generation AI uses the emotion estimation function to analyze the emotional state of the customer. For example, if the customer is feeling stressed, the generation AI provides a response that relaxes the customer. The data analysis unit also analyzes the emotional state of the customer, and the generation AI generates a response that corresponds to the emotion. For example, if the customer is feeling anxious, the generation AI provides a response that reassures the customer. The data analysis unit also uses the emotion estimation function to analyze the emotional state of the customer, and generate a response that corresponds to the emotion. For example, if the customer is feeling angry, the generation AI provides a response that responds calmly. This makes it possible to provide an appropriate response that corresponds to the customer's emotions.

[0054] The data analysis unit can analyze social media activity and provide appropriate answers to related inquiries. For example, the data analysis unit allows the generation AI to analyze a customer's social media activity and provide appropriate answers to related inquiries. For example, if a customer posts a question about insurance on social media, the generation AI automatically answers that question. The data analysis unit also analyzes social media activity and allows the generation AI to provide appropriate answers to related inquiries. For example, if a customer posts about vehicle maintenance, the generation AI provides maintenance appointment information. The data analysis unit also allows the generation AI to analyze a customer's social media activity and provide appropriate answers to related inquiries. For example, if a customer posts about banking services, the generation AI provides information about those services. This makes it possible to provide appropriate answers based on social media activity.

[0055] The data analysis unit can integrate customer data from different companies to enable cross-industry inquiry handling. For example, the generation AI in the data analysis unit integrates customer data from different companies to enable cross-industry inquiry handling. For example, data from a bank and an insurance company can be integrated to allow customers to make inquiries about both services at once. The data analysis unit also integrates customer data from different companies to enable the generation AI to enable cross-industry inquiry handling. For example, data from an automobile company and an insurance company can be integrated to allow customers to make inquiries about vehicle insurance at once. The data analysis unit also integrates customer data from different companies to enable cross-industry inquiry handling. For example, data from a bank and an automobile company can be integrated to allow customers to make inquiries about vehicle loans at once. This makes it possible to handle inquiries across industries by integrating data from different companies.

[0056] The data analysis unit can use the emotion estimation function to generate a personalized marketing message based on the customer's emotions. The data analysis unit, for example, uses the emotion estimation function to cause the generation AI to generate a personalized marketing message based on the customer's emotions. For example, if the customer is feeling happy, the generation AI provides a positive message that matches that emotion. The data analysis unit also analyzes the customer's emotions, and the generation AI generates a personalized marketing message based on the emotions. For example, if the customer is feeling anxious, the generation AI provides a message that reassures the customer. The data analysis unit also uses the emotion estimation function to cause the generation AI to generate a personalized marketing message based on the customer's emotions. For example, if the customer is excited, the generation AI provides an energetic message that matches that emotion. This makes it possible to provide a personalized marketing message based on the customer's emotions.

[0057] The telephone response unit can analyze the tone of voice or speaking style and provide a response that corresponds to the emotional state of the customer. In the telephone response unit, for example, the generation AI analyzes the tone of voice and speaking style of the customer and provides a response that corresponds to the customer's emotional state. For example, if the customer is nervous, the generation AI provides a response that relaxes the customer. In addition, the telephone response unit analyzes the tone of voice of the customer and the generation AI provides a response that corresponds to the emotional state. For example, if the customer is angry, the generation AI provides a response that responds calmly. In addition, the telephone response unit analyzes the way the customer speaks and provides a response that corresponds to the emotional state. For example, if the customer is sad, the generation AI provides a comforting response. This makes it possible to provide an appropriate response that corresponds to the customer's emotional state.

[0058] The telephone answering unit uses the emotion estimation function to generate a voice tone and speaking style that corresponds to the customer's emotions, thereby enabling more natural dialogue. For example, the telephone answering unit uses the emotion estimation function so that the generation AI generates a voice tone and speaking style that corresponds to the customer's emotions. For example, if the customer is happy, the generation AI replies in a bright tone. The telephone answering unit also analyzes the customer's emotions, and the generation AI generates a voice tone and speaking style that corresponds to the emotions. For example, if the customer is feeling anxious, the generation AI replies in a calm tone. The telephone answering unit also uses the emotion estimation function so that the generation AI generates a voice tone and speaking style that corresponds to the customer's emotions. For example, if the customer is angry, the generation AI replies in a calm and gentle tone. This makes it possible to provide natural dialogue that corresponds to the customer's emotions.

[0059] The telephone answering unit can also respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, the telephone answering unit uses a generation AI to respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, it can respond to inquiries in multiple languages ​​such as English, Spanish, and Chinese. The telephone answering unit also uses a multilingual generation AI to respond to telephone inquiries in different languages. For example, if a customer makes an inquiry in Japanese, it will provide an answer in Japanese. The telephone answering unit also uses a generation AI to respond to telephone inquiries in different languages ​​and provide a multilingual service. For example, if a customer makes an inquiry in French, it will provide an answer in French. This makes it possible to provide a multilingual automated telephone assistance service.

[0060] The telephone response unit can analyze geographical location information and provide region-specific information. For example, the generation AI analyzes the geographical location information of a customer in the telephone response unit and provides region-specific information. For example, if a customer lives in a specific region, information on bank branches in that region is provided. The generation AI also provides region-specific information based on the customer's geographical location information. For example, if a customer has an insurance policy in a specific region, information on insurance agencies in that region is provided. The generation AI also analyzes the customer's geographical location information and provides region-specific information. For example, if a customer has vehicle maintenance in a specific region, information on maintenance services in that region is provided. This makes it possible to provide region-specific information.

[0061] The internet response unit can analyze website browsing history and predict and provide the next information that will be required. In the internet response unit, for example, the generation AI analyzes a customer's website browsing history and predicts the next information that will be required. For example, if a customer is in the process of purchasing a vehicle, the generation AI automatically guides the customer to the next required documents and procedures. In addition, the internet response unit can predict the next information that will be required based on the customer's website browsing history. For example, if a customer is checking the details of an insurance contract, the generation AI automatically guides the customer to the next required procedures and documents. In addition, the internet response unit can analyze a customer's website browsing history and predict the next information that will be required. For example, if a customer is in the process of applying for a bank loan, the generation AI automatically guides the customer to the next required documents and procedures. This makes it possible to provide the next required information based on the customer's website browsing history.

[0062] The Internet response unit analyzes the input content in real time and can provide appropriate answers even when the input is in progress. For example, the generation AI in the Internet response unit analyzes the input content of a customer in real time and can provide appropriate answers even when the input is in progress. For example, while a customer is entering information for a vehicle purchase procedure, the generation AI automatically guides the customer to the necessary documents and procedures. The Internet response unit also analyzes the input content of a customer in real time and can provide appropriate answers even when the input is in progress. For example, while a customer is entering details of an insurance contract, the generation AI automatically guides the customer to the next necessary procedures and documents. The Internet response unit also analyzes the input content of a customer in real time and can provide appropriate answers even when the input is in progress. For example, while a customer is entering details of a bank loan application, the generation AI automatically guides the customer to the necessary documents and procedures. This makes it possible to provide appropriate answers in real time based on the input content of a customer.

[0063] The internet response unit can use the emotion estimation function to dynamically change the design and color tone of the website according to the customer's emotions. For example, the internet response unit uses the emotion estimation function so that the generation AI dynamically changes the design and color tone of the website according to the customer's emotions. For example, if the customer is feeling stressed, the color tone is changed to a relaxing one. The internet response unit also analyzes the customer's emotions, and the generation AI dynamically changes the design and color tone of the website according to the emotions. For example, if the customer is nervous, the color tone is changed to a calming one. The internet response unit also uses the emotion estimation function so that the generation AI dynamically changes the design and color tone of the website according to the customer's emotions. For example, if the customer is happy, the color tone is changed to a brighter one. This makes it possible to dynamically change the design and color tone of the website according to the customer's emotions.

[0064] The internet response unit can analyze device information and provide a response optimized for the device. In the internet response unit, for example, the generation AI analyzes the customer's device information and provides a response optimized for the device. For example, a mobile-friendly response is provided for a customer using a smartphone. Also, in the internet response unit, the generation AI analyzes the customer's device information and provides a response optimized for the device. For example, a tablet is provided for a customer using a tablet. Also, in the internet response unit, the generation AI analyzes the customer's device information and provides a response optimized for the device. For example, a desktop is provided for a customer using a desktop. This makes it possible to provide a response optimized for the customer's device.

[0065] The internet response unit operates as a browser extension and can provide appropriate information even outside the website. For example, the generation AI operates as a browser extension for the customer and provides appropriate information even outside the website. For example, when the customer is browsing other websites, relevant information is displayed in a pop-up. The internet response unit also operates as a browser extension and provides appropriate information to the customer even outside the website. For example, when the customer is checking email, relevant information is provided. The internet response unit also operates as a browser extension for the customer and provides appropriate information even outside the website. For example, when the customer is browsing social media, relevant information is provided. This makes it possible to provide appropriate information even outside the website.

[0066] The internet response unit uses the emotion estimation function to provide an interactive chatbot based on the customer's emotions, thereby enabling more personalized dialogue. For example, the internet response unit uses the emotion estimation function so that the generation AI provides an interactive chatbot based on the customer's emotions. For example, if the customer is feeling stressed, the generation AI will engage in a dialogue that will relax the customer. The internet response unit also analyzes the customer's emotions, and the generation AI provides an interactive chatbot based on the emotions. For example, if the customer is nervous, the generation AI will engage in a calm dialogue. The internet response unit also uses the emotion estimation function so that the generation AI provides an interactive chatbot based on the customer's emotions. For example, if the customer is happy, the generation AI will engage in a cheerful dialogue. This makes it possible to provide personalized dialogue based on the customer's emotions.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The data analysis unit can analyze a customer's purchasing history and suggest related products and services. For example, if a customer has previously purchased products from a specific brand, new products from that brand or related products will be suggested. The data analysis unit can also use the generation AI to predict a customer's preferences and interests based on the customer's purchasing history and make personalized suggestions. For example, if a customer frequently purchases outdoor equipment, it can provide information on new camping equipment and outdoor events. The data analysis unit can also analyze a customer's purchasing history and use the generation AI to make suggestions tailored to the customer's lifestyle. For example, it can suggest health foods and fitness-related services to health-conscious customers. This allows for more personalized suggestions to be made based on the customer's purchasing history.

[0069] The data analysis unit can analyze customer feedback and identify areas for improvement in services. For example, it can analyze feedback provided by customers and identify frequently pointed out problems. The data analysis unit can also enable the generation AI to suggest areas for improvement in services based on customer feedback. For example, if a customer is dissatisfied with a particular function, it can propose improvements to that function. The data analysis unit can also analyze customer feedback and enable the generation AI to propose new services or functions. For example, if a customer requests a new function, it can propose implementation plans for that function. This makes it possible to improve services and make new proposals based on customer feedback.

[0070] The data analysis unit can analyze customer behavior patterns and provide promotions at the optimal timing. For example, if a customer frequently shops online during a specific time period, the generation AI can provide promotions that coincide with that time period. The data analysis unit can also enable the generation AI to predict the optimal timing for promotions based on customer behavior patterns. For example, if a customer tends to shop on weekends, the generation AI can provide promotions that coincide with weekends. The data analysis unit can also analyze customer behavior patterns and enable the generation AI to provide promotions that match the customer's lifestyle. For example, if a customer tends to purchase fitness-related products, the generation AI can provide fitness-related promotions. This makes it possible to provide promotions at the optimal timing based on the customer's behavior patterns.

[0071] The data analysis unit can use the emotion estimation function to provide customer support based on the emotional state of the customer. For example, if a customer is feeling stressed, the generation AI can provide support that helps them relax. The data analysis unit can also analyze the emotional state of the customer, allowing the generation AI to provide customer support that corresponds to the emotion. For example, if a customer is feeling anxious, the generation AI can provide support that reassures the customer. The data analysis unit can also use the emotion estimation function to allow the generation AI to provide customer support based on the emotional state of the customer. For example, if a customer is feeling angry, the generation AI can provide support that responds calmly. This makes it possible to provide appropriate customer support that corresponds to the customer's emotions.

[0072] The data analysis unit can analyze a customer's social media activity and suggest relevant marketing campaigns. For example, if a customer mentions a specific brand on social media, the data analysis unit can suggest campaigns for that brand. The data analysis unit can also enable the generation AI to suggest marketing campaigns tailored to the customer's interests based on social media activity. For example, if a customer frequently posts about travel, the data analysis unit can suggest travel-related campaigns. The data analysis unit can also analyze a customer's social media activity and enable the generation AI to suggest marketing campaigns tailored to the customer's lifestyle. For example, if a customer posts about being health-conscious, the data analysis unit can suggest health-related campaigns. This makes it possible to suggest more personalized marketing campaigns based on the customer's social media activity.

[0073] The data analysis unit can integrate customer data from different companies to realize cross-industry marketing campaigns. For example, data from a bank and an insurance company can be integrated so that customers can receive campaigns for both services at once. The data analysis unit can also integrate customer data from different companies so that generation AI can realize cross-industry marketing campaigns. For example, data from an automobile company and an insurance company can be integrated so that customers can receive campaigns for vehicle insurance at once. The data analysis unit can also integrate customer data from different companies so that generation AI can realize cross-industry marketing campaigns. For example, data from a bank and an automobile company can be integrated so that customers can receive campaigns for vehicle loans at once. This makes cross-industry marketing campaigns possible by integrating data between different companies.

[0074] The data analysis unit can use the emotion estimation function to provide personalized promotions based on the customer's emotions. For example, if the customer is feeling happy, the generation AI can provide a positive promotion that matches that emotion. The data analysis unit can also analyze the customer's emotions, allowing the generation AI to provide personalized promotions based on those emotions. For example, if the customer is feeling anxious, the generation AI can provide a promotion that reassures the customer. The data analysis unit can also use the emotion estimation function to allow the generation AI to provide personalized promotions based on the customer's emotions. For example, if the customer is excited, the generation AI can provide an energetic promotion that matches that emotion. In this way, personalized promotions based on the customer's emotions can be provided.

[0075] The telephone response unit can analyze the tone of voice or speaking style and provide a response that corresponds to the emotional state of the customer. For example, if the customer is nervous, the generation AI will provide a response that relaxes them. The telephone response unit can also analyze the tone of voice of the customer and provide a response that corresponds to the emotional state. For example, if the customer is angry, the generation AI will provide a response that responds calmly. The telephone response unit can also analyze the way the customer speaks and provide a response that corresponds to the emotional state. For example, if the customer is sad, the generation AI will provide a comforting response. This makes it possible to provide an appropriate response that corresponds to the emotional state of the customer.

[0076] The telephone answering unit can also respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, the generation AI can respond to telephone inquiries in different languages ​​and provide a multilingual automated telephone assistance service. For example, inquiries in multiple languages ​​such as English, Spanish, and Chinese can be handled. The telephone answering unit can also respond to telephone inquiries in different languages ​​using a multilingual generation AI. For example, if a customer makes an inquiry in Japanese, an answer will be provided in Japanese. The telephone answering unit can also respond to telephone inquiries in different languages ​​using the generation AI to provide a multilingual service. For example, if a customer makes an inquiry in French, an answer will be provided in French. This makes it possible to provide a multilingual automated telephone assistance service.

[0077] The internet response unit can use the emotion estimation function to dynamically change the design and color tone of the website according to the customer's emotions. For example, using the emotion estimation function, the generation AI dynamically changes the design and color tone of the website according to the customer's emotions. For example, if the customer is feeling stressed, the color tone is changed to a relaxing one. The internet response unit can also analyze the customer's emotions and the generation AI can dynamically change the design and color tone of the website according to the emotions. For example, if the customer is nervous, the color tone is changed to a calming one. The internet response unit can also use the emotion estimation function to dynamically change the design and color tone of the website according to the customer's emotions. For example, if the customer is happy, the color tone is changed to a brighter one. This makes it possible to dynamically change the design and color tone of the website according to the customer's emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The data analysis department analyzes customer records and transaction records. For example, it analyzes a customer's purchase history and inquiry history to understand their behavioral patterns. It can also analyze transaction dates and times and transaction details to understand a customer's trading trends. It can also analyze the data using statistical analysis and machine learning algorithms to predict future transactions based on the customer's past transaction data. Step 2: The answer generation unit generates answers based on the results of the analysis by the data analysis unit. For example, it uses natural language generation technology to generate appropriate answers to customer inquiries. It also performs template-based answer generation, generating answers to customer inquiries based on answer templates prepared in advance. Step 3: The telephone answering unit provides the answer generated by the answer generating unit over the telephone. For example, it accepts customer inquiries using voice recognition technology and provides answers using voice synthesis technology. It can also analyze the customer's tone of voice and speaking style to provide a response that matches the customer's emotional state. For example, if the customer is nervous, it can provide a response that will relax them. Step 4: The internet response unit provides the answer generated by the answer generation unit via the internet. For example, it provides an answer to the customer's inquiry using a chatbot. It can also provide the answer via a web interface. For example, it provides an appropriate answer when a customer makes an inquiry via a website.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] 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.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data analysis department that analyzes customer records and transaction records; a response generation unit that generates a response based on the results of the analysis by the data analysis unit; a telephone answering unit that provides the answer generated by the answer generating unit via telephone; an internet response unit that provides the answer generated by the answer generation unit via the internet; A system characterized by:

2. The data analysis unit Analyze the inquiry history, predict the content of the next inquiry, and prepare a response in advance.

2. The system of claim 1.

3. The data analysis unit Learns behavioral patterns and proactively provides information at the optimal time 2. The system of claim 1.

4. The data analysis unit Analyze the customer's emotional state and generate responses based on that emotion 2. The system of claim 1.

5. The data analysis unit Analyzing social media activity and providing relevant responses to relevant inquiries 2. The system of claim 1.

6. The data analysis unit Integrate customer data from different companies to handle cross-industry inquiries 2. The system of claim 1.

7. The data analysis unit Generate personalized marketing messages based on customer sentiment 2. The system of claim 1.

8. The telephone answering unit Analyze voice tone or speaking style to respond according to the customer's emotional state 2. The system of claim 1.

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