System

Abstract

An object of the system according to the embodiment is to efficiently classify feedbacks and complaints from customers and to appropriately deal with them.SOLUTION: A system includes a message evaluation part, a complaint detection part, a feedback transfer part, an automatic response generation part, and a countermeasure proposal part. The message evaluator evaluates the customer's message. The complaint detection unit detects a malicious complaint or a false report from the message evaluated by the message evaluation unit. The feedback forwarding unit forwards the valid feedback detected by the complaint detection unit to the response team. The automatic response generator generates an automatic response to the fraudulent complaint detected by the complaint detector. The countermeasure proposing section proposes a further countermeasure against the unjust complaint detected by the complaint detecting section.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 technologies have had the problem of making it difficult to efficiently classify customer feedback and complaints and respond appropriately.

[0005] The system according to the embodiment aims to efficiently classify feedback and complaints from customers and respond appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a message evaluation unit, a complaint detection unit, a feedback transfer unit, an automatic response generation unit, and a countermeasure proposal unit. The message evaluation unit evaluates customer messages. The complaint detection unit detects malicious complaints or false declarations from the messages evaluated by the message evaluation unit. The feedback transfer unit transfers legitimate feedback detected by the complaint detection unit to a response team. The automatic response generation unit generates an automatic response to a fraudulent complaint detected by the complaint detection unit. The countermeasure proposal unit proposes further countermeasures to a fraudulent complaint detected by the complaint detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently classify feedback and complaints from customers and respond appropriately. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) An AI service according to an embodiment of the present invention is a system that analyzes customer feedback and complaints and automatically distinguishes between constructive and malicious comments. The system uses language analysis technology to evaluate the tone, wording, and content of customer messages to detect malicious or false claims. Furthermore, the system quickly forwards legitimate feedback to a response team, automatically generates appropriate responses for fraudulent feedback, and, in some cases, suggests options for further action. This enables the AI ​​service to efficiently process customer feedback and complaints and improve a company's response capabilities.

[0029] An AI service according to an embodiment includes a message evaluation unit, a complaint detection unit, a feedback transfer unit, an automatic response generation unit, and a countermeasure proposal unit. The message evaluation unit evaluates customer messages. For example, the message evaluation unit uses a generation AI to analyze the tone, wording, and content of customer messages and check for offensive language or inappropriate expressions. The complaint detection unit detects malicious complaints or false statements from the messages evaluated by the message evaluation unit. For example, the complaint detection unit compares the messages with past data and determines that if the same customer repeatedly makes similar complaints, the complaint is likely to be false. The feedback transfer unit transfers legitimate feedback detected by the complaint detection unit to a support team. For example, the feedback transfer unit transfers constructive feedback such as, "This product is difficult to use, so please improve the manual." The automatic response generation unit generates an automatic response to fraudulent complaints detected by the complaint detection unit. For example, the automatic response generation unit generates a response such as, "This complaint has been received in the past with similar content and has already been handled." The countermeasure suggestion unit proposes further countermeasures for fraudulent claims detected by the claim detection unit. For example, if a customer repeatedly submits false claims, the countermeasure suggestion unit proposes adding the customer to a blacklist. This enables the AI ​​service according to the embodiment to efficiently process customer feedback and complaints and improve a company's responsiveness. For example, the AI ​​service can improve customer satisfaction by quickly responding to legitimate feedback, and prevent the company from wasting resources by taking appropriate countermeasures for fraudulent claims.

[0030] The message evaluation unit analyzes the cultural elements or region-specific expressions behind the message, enabling highly accurate evaluation. The message evaluation unit, for example, analyzes the cultural elements behind the message. For example, it understands expressions and phrases that are unique to a particular region or culture, and evaluates the message based on those expressions. This improves the accuracy of message evaluation by taking cultural elements and region-specific expressions into consideration.

[0031] The complaint detection unit can detect false claims with a high degree of accuracy by comparing it with complaint data and performing pattern recognition. The complaint detection unit, for example, uses generative AI to compare it with past complaint data and perform pattern recognition. For example, if the same customer repeatedly makes the same complaint, the likelihood that the complaint is false increases. This allows it to compare it with past complaint data and detect false claims with a high degree of accuracy.

[0032] The complaint detection unit analyzes not only the content of the complaint but also the time period and frequency of the complaints, making it possible to detect abnormal patterns. The complaint detection unit, for example, analyzes not only the content of the complaint but also the time period and frequency of the complaints. For example, it determines whether complaints that are submitted in large numbers during a specific time period are abnormal. In this way, by analyzing not only the content of the complaint but also the time period and frequency of the complaints, it is possible to detect abnormal patterns.

[0033] The feedback forwarding unit can use the generation AI to analyze the content of the feedback in detail, extract specific improvements, and forward them to the response team. The feedback forwarding unit can use the generation AI to analyze the content of the feedback in detail, extract specific improvements from customer feedback, and forward them to the response team. This allows the content of the feedback to be analyzed in detail, extract specific improvements, and forward them to the response team.

[0034] The feedback forwarding unit can evaluate the importance of feedback and prioritize the feedback before forwarding. The feedback forwarding unit, for example, evaluates the importance of feedback and prioritizes the feedback before forwarding. For example, the feedback forwarding unit analyzes customer feedback and prioritizes the feedback with high importance before forwarding it to the support team. This allows the importance of feedback to be evaluated and prioritized before forwarding.

[0035] The automatic response generation unit can learn from past response data and generate appropriate and effective automatic responses. The automatic response generation unit can learn from past response data and generate more appropriate and effective automatic responses, for example, using a generation AI. For example, it can generate new responses based on successful response patterns in the past. This allows it to learn from past response data and generate more appropriate and effective automatic responses.

[0036] The automatic response generation unit can customize the tone and wording of the response according to the content of the complaint, and respond appropriately. The automatic response generation unit customizes the tone and wording of the response according to the content of the complaint. For example, it generates a calm and polite response for an aggressive complaint. This allows the tone and wording of the response to be customized according to the content of the complaint, making it possible to respond more appropriately.

[0037] The countermeasure proposal unit can use the generation AI to learn from past countermeasure data and propose optimal countermeasures. The countermeasure proposal unit can, for example, use the generation AI to learn from past countermeasure data and propose optimal countermeasures. For example, it can propose new countermeasures based on countermeasures that have been successful in the past. This makes it possible to learn from past countermeasure data and propose optimal countermeasures.

[0038] The countermeasure proposal unit can set the priority of countermeasures according to the content and frequency of complaints and propose effective countermeasures. The countermeasure proposal unit sets the priority of countermeasures according to, for example, the content and frequency of complaints. For example, countermeasures are proposed preferentially for serious complaints or complaints that occur frequently. This makes it possible to set the priority of countermeasures according to the content and frequency of complaints and propose effective countermeasures.

[0039] The Countermeasure Proposal Department can incorporate best practices from other companies and industries to propose effective countermeasures. The Countermeasure Proposal Department, for example, incorporates best practices from other companies and industries when proposing countermeasures. For example, it can propose effective countermeasures based on success stories from other companies in the same industry. This allows it to incorporate best practices from other companies and industries to propose effective countermeasures.

[0040] The countermeasure proposal unit can predict future risks and propose preventive measures using data analysis and a prediction model. The countermeasure proposal unit, for example, uses data analysis and a prediction model to propose countermeasures. For example, it predicts future risks based on past data and proposes preventive measures. In this way, it is possible to predict future risks and propose preventive measures using data analysis and a prediction model.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

[0043] When analyzing customer feedback and complaints, AI services can analyze the cultural elements and regional expressions behind the messages to provide highly accurate evaluations. For example, they can understand expressions and phrases unique to specific regions and cultures and evaluate messages accordingly. They can also analyze feedback from customers of different cultural backgrounds, taking cultural differences into account. Furthermore, they can identify regional issues and needs and propose appropriate responses.

[0044] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

[0045] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

[0046] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

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

[0048] Step 1: The message evaluation unit evaluates the customer's message. For example, using generative AI, it analyzes the tone, wording, and content of the customer's message to check for any offensive or inappropriate language. Step 2: The complaint detection unit detects malicious complaints and false declarations from the messages evaluated by the message evaluation unit. For example, by comparing the results with past data, if the same customer repeatedly makes the same complaint, it determines that the complaint is likely to be false. Step 3: The feedback forwarding unit forwards the legitimate feedback detected by the complaint detection unit to the support team. For example, constructive feedback such as "It's difficult to understand how to use this product, so please improve the manual" is forwarded to the support team. Step 4: The automatic response generator generates an automatic response to the fraudulent complaint detected by the complaint detector. For example, it generates a response such as, "This complaint has been received in the past with similar content and has already been handled." Step 5: The countermeasure suggestion unit suggests further countermeasures for fraudulent claims detected by the claim detection unit. For example, if a customer repeatedly submits false claims, it suggests adding the customer to a blacklist.

[0049] (Example 2) An AI service according to an embodiment of the present invention is a system that analyzes customer feedback and complaints and automatically distinguishes between constructive and malicious comments. The system uses language analysis technology to evaluate the tone, wording, and content of customer messages to detect malicious or false claims. Furthermore, the system quickly forwards legitimate feedback to a response team, automatically generates appropriate responses for fraudulent feedback, and, in some cases, suggests options for further action. This enables the AI ​​service to efficiently process customer feedback and complaints and improve a company's response capabilities.

[0050] An AI service according to an embodiment includes a message evaluation unit, a complaint detection unit, a feedback transfer unit, an automatic response generation unit, and a countermeasure proposal unit. The message evaluation unit evaluates customer messages. For example, the message evaluation unit uses a generation AI to analyze the tone, wording, and content of customer messages and check for offensive language or inappropriate expressions. The complaint detection unit detects malicious complaints or false statements from the messages evaluated by the message evaluation unit. For example, the complaint detection unit compares the messages with past data and determines that if the same customer repeatedly makes similar complaints, the complaint is likely to be false. The feedback transfer unit transfers legitimate feedback detected by the complaint detection unit to a support team. For example, the feedback transfer unit transfers constructive feedback such as, "This product is difficult to use, so please improve the manual." The automatic response generation unit generates an automatic response to fraudulent complaints detected by the complaint detection unit. For example, the automatic response generation unit generates a response such as, "This complaint has been received in the past with similar content and has already been handled." The countermeasure suggestion unit proposes further countermeasures for fraudulent claims detected by the claim detection unit. For example, if a customer repeatedly submits false claims, the countermeasure suggestion unit proposes adding the customer to a blacklist. This enables the AI ​​service according to the embodiment to efficiently process customer feedback and complaints and improve a company's responsiveness. For example, the AI ​​service can improve customer satisfaction by quickly responding to legitimate feedback, and prevent the company from wasting resources by taking appropriate countermeasures for fraudulent claims.

[0051] The message evaluation unit can analyze the emotional nuances of messages in detail and detect changes in emotions. The message evaluation unit can use, for example, generative AI to analyze the emotional nuances of messages in detail. For example, it can detect subtle changes in emotions contained in customers' messages and quantify aggressive tones or the degree of dissatisfaction. This allows for a detailed analysis of changes in customers' emotions and enables more appropriate responses.

[0052] The message evaluation unit analyzes the cultural elements or region-specific expressions behind the message, enabling highly accurate evaluation. The message evaluation unit, for example, analyzes the cultural elements behind the message. For example, it understands expressions and phrases that are unique to a particular region or culture, and evaluates the message based on those expressions. This improves the accuracy of message evaluation by taking cultural elements and region-specific expressions into consideration.

[0053] The message evaluation unit can estimate the user's emotions in real time using the emotion estimation function and perform evaluation according to changes in emotions. The message evaluation unit estimates the user's emotions in real time using, for example, the emotion estimation function. For example, it analyzes the user's facial expression and voice when entering a message to detect changes in emotions. This makes it possible to estimate the user's emotions in real time and perform evaluation according to changes in emotions.

[0054] The complaint detection unit can detect false claims with a high degree of accuracy by comparing it with complaint data and performing pattern recognition. The complaint detection unit, for example, uses generative AI to compare it with past complaint data and perform pattern recognition. For example, if the same customer repeatedly makes the same complaint, the likelihood that the complaint is false increases. This allows it to compare it with past complaint data and detect false claims with a high degree of accuracy.

[0055] The complaint detection unit analyzes not only the content of the complaint but also the time period and frequency of the complaints, making it possible to detect abnormal patterns. The complaint detection unit, for example, analyzes not only the content of the complaint but also the time period and frequency of the complaints. For example, it determines whether complaints that are submitted in large numbers during a specific time period are abnormal. In this way, by analyzing not only the content of the complaint but also the time period and frequency of the complaints, it is possible to detect abnormal patterns.

[0056] The complaint detection unit can use the emotion estimation function to analyze the emotion of the complaint submitter and determine whether or not the emotion is malicious. The complaint detection unit, for example, uses the emotion estimation function to analyze the emotion of the complaint submitter. For example, if the emotion of anger or dissatisfaction is strong, there is a high possibility that the complaint is malicious. This makes it possible to analyze the emotion of the complaint submitter and determine whether or not the emotion is malicious.

[0057] The feedback forwarding unit can use the generation AI to analyze the content of the feedback in detail, extract specific improvements, and forward them to the response team. The feedback forwarding unit can use the generation AI to analyze the content of the feedback in detail, extract specific improvements from customer feedback, and forward them to the response team. This allows the content of the feedback to be analyzed in detail, extract specific improvements, and forward them to the response team.

[0058] The feedback forwarding unit can evaluate the importance of feedback and prioritize the feedback before forwarding. The feedback forwarding unit, for example, evaluates the importance of feedback and prioritizes the feedback before forwarding. For example, the feedback forwarding unit analyzes customer feedback and prioritizes the feedback with high importance before forwarding it to the support team. This allows the importance of feedback to be evaluated and prioritized before forwarding.

[0059] The feedback forwarding unit can use the emotion estimation function to analyze the emotion of the feedback submitter and prioritize forward emotionally important feedback. The feedback forwarding unit, for example, uses the emotion estimation function to analyze the emotion of the feedback submitter. For example, feedback with a strong positive emotion can be prioritized and forwarded to the response team. This allows emotionally important feedback to be prioritized and forwarded.

[0060] The automatic response generation unit can learn from past response data and generate appropriate and effective automatic responses. The automatic response generation unit can learn from past response data and generate more appropriate and effective automatic responses, for example, using a generation AI. For example, it can generate new responses based on successful response patterns in the past. This allows it to learn from past response data and generate more appropriate and effective automatic responses.

[0061] The automatic response generation unit can customize the tone and wording of the response according to the content of the complaint, and respond appropriately. The automatic response generation unit customizes the tone and wording of the response according to the content of the complaint. For example, it generates a calm and polite response for an aggressive complaint. This allows the tone and wording of the response to be customized according to the content of the complaint, making it possible to respond more appropriately.

[0062] The automatic response generation unit can use the emotion estimation function to analyze the emotion of the complaint submitter and generate an appropriate response according to the emotion. The automatic response generation unit, for example, uses the emotion estimation function to analyze the emotion of the complaint submitter. For example, if the emotion of anger or dissatisfaction is strong, a calm and polite response is generated. This makes it possible to analyze the emotion of the complaint submitter and generate an appropriate response according to the emotion.

[0063] The countermeasure proposal unit can use the generation AI to learn from past countermeasure data and propose optimal countermeasures. The countermeasure proposal unit can, for example, use the generation AI to learn from past countermeasure data and propose optimal countermeasures. For example, it can propose new countermeasures based on countermeasures that have been successful in the past. This makes it possible to learn from past countermeasure data and propose optimal countermeasures.

[0064] The countermeasure proposal unit can set the priority of countermeasures according to the content and frequency of complaints and propose effective countermeasures. The countermeasure proposal unit sets the priority of countermeasures according to, for example, the content and frequency of complaints. For example, countermeasures are proposed preferentially for serious complaints or complaints that occur frequently. This makes it possible to set the priority of countermeasures according to the content and frequency of complaints and propose effective countermeasures.

[0065] The Countermeasure Proposal Department can incorporate best practices from other companies and industries to propose effective countermeasures. The Countermeasure Proposal Department, for example, incorporates best practices from other companies and industries when proposing countermeasures. For example, it can propose effective countermeasures based on success stories from other companies in the same industry. This allows it to incorporate best practices from other companies and industries to propose effective countermeasures.

[0066] The countermeasure proposal unit can predict future risks and propose preventive measures using data analysis and a prediction model. The countermeasure proposal unit, for example, uses data analysis and a prediction model to propose countermeasures. For example, it predicts future risks based on past data and proposes preventive measures. In this way, it is possible to predict future risks and propose preventive measures using data analysis and a prediction model.

[0067] The countermeasure proposal unit can use the emotion estimation function to monitor the emotion of the complaint submitter in real time and propose appropriate countermeasures according to the emotion. The countermeasure proposal unit, for example, uses the emotion estimation function to monitor the emotion of the complaint submitter in real time. For example, if the emotion score is high, the countermeasure proposal unit proposes countermeasures including a prompt response and an apology. This makes it possible to monitor the emotion of the complaint submitter in real time and propose appropriate countermeasures according to the emotion.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

[0070] When analyzing customer feedback and complaints, the AI ​​service can use its emotion estimation function to estimate customer emotions in real time and respond appropriately based on those emotions. For example, if a customer is very angry, it can generate a response that includes a quick response and an apology. Also, if a customer is dissatisfied, it can make specific suggestions to resolve their dissatisfaction. Furthermore, if a customer provides positive feedback, it can use that feedback to identify areas for further improvement and increase customer satisfaction.

[0071] When analyzing customer feedback and complaints, AI services can analyze the cultural elements and regional expressions behind the messages to provide highly accurate evaluations. For example, they can understand expressions and phrases unique to specific regions and cultures and evaluate messages accordingly. They can also analyze feedback from customers of different cultural backgrounds, taking cultural differences into account. Furthermore, they can identify regional issues and needs and propose appropriate responses.

[0072] When analyzing customer feedback and complaints, the AI ​​service can use its emotion estimation function to estimate customer emotions in real time and respond appropriately based on those emotions. For example, if a customer is very angry, it can generate a response that includes a quick response and an apology. Also, if a customer is dissatisfied, it can make specific suggestions to resolve their dissatisfaction. Furthermore, if a customer provides positive feedback, it can use that feedback to identify areas for further improvement and increase customer satisfaction.

[0073] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

[0074] When analyzing customer feedback and complaints, the AI ​​service can use its emotion estimation function to estimate customer emotions in real time and respond appropriately based on those emotions. For example, if a customer is very angry, it can generate a response that includes a quick response and an apology. Also, if a customer is dissatisfied, it can make specific suggestions to resolve their dissatisfaction. Furthermore, if a customer provides positive feedback, it can use that feedback to identify areas for further improvement and increase customer satisfaction.

[0075] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

[0076] When analyzing customer feedback and complaints, the AI ​​service can use its emotion estimation function to estimate customer emotions in real time and respond appropriately based on those emotions. For example, if a customer is very angry, it can generate a response that includes a quick response and an apology. Also, if a customer is dissatisfied, it can make specific suggestions to resolve their dissatisfaction. Furthermore, if a customer provides positive feedback, it can use that feedback to identify areas for further improvement and increase customer satisfaction.

[0077] AI services can not only analyze customer feedback and complaints, but also analyze customers' purchasing history and behavioral patterns to predict potential problems. For example, if a customer who frequently purchases a particular product suddenly stops purchasing, the reason can be analyzed and appropriate measures can be proposed. It is also possible to predict future complaints based on the content of feedback and complaints submitted by customers in the past and take measures in advance. Furthermore, by analyzing customer behavioral patterns, it is possible to understand the circumstances in which customers tend to file complaints and propose preventative measures.

[0078] When analyzing customer feedback and complaints, the AI ​​service can use its emotion estimation function to estimate customer emotions in real time and respond appropriately based on those emotions. For example, if a customer is very angry, it can generate a response that includes a quick response and an apology. Also, if a customer is dissatisfied, it can make specific suggestions to resolve their dissatisfaction. Furthermore, if a customer provides positive feedback, it can use that feedback to identify areas for further improvement and increase customer satisfaction.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The message evaluation unit evaluates the customer's message. For example, using generative AI, it analyzes the tone, wording, and content of the customer's message to check for any offensive or inappropriate language. Step 2: The complaint detection unit detects malicious complaints and false declarations from the messages evaluated by the message evaluation unit. For example, by comparing the results with past data, if the same customer repeatedly makes the same complaint, it determines that the complaint is likely to be false. Step 3: The feedback forwarding unit forwards the legitimate feedback detected by the complaint detection unit to the support team. For example, constructive feedback such as "It's difficult to understand how to use this product, so please improve the manual" is forwarded to the support team. Step 4: The automatic response generator generates an automatic response to the fraudulent complaint detected by the complaint detector. For example, it generates a response such as, "This complaint has been received in the past with similar content and has already been handled." Step 5: The countermeasure suggestion unit suggests further countermeasures for fraudulent claims detected by the claim detection unit. For example, if a customer repeatedly submits false claims, it suggests adding the customer to a blacklist.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0117] 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] 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).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 message evaluation unit that evaluates messages from customers; a complaint detection unit that detects malicious complaints or false declarations from the messages evaluated by the message evaluation unit; a feedback transfer unit that transfers the legitimate feedback detected by the complaint detection unit to a response team; an automatic response generation unit that generates an automatic response to the fraudulent complaint detected by the complaint detection unit; a countermeasure suggestion unit that proposes further countermeasures against fraudulent claims detected by the claim detection unit. A system characterized by:

2. The message evaluation unit Analyzing the emotional nuances of the message in detail and detecting changes in emotion 2. The system of claim 1.

3. The complaint detection unit By comparing the data with claim data and performing pattern recognition, false declarations can be detected with high accuracy.

2. The system of claim 1.

4. The feedback transfer unit Using the generating AI, the content of the feedback is analyzed in detail, specific improvements are extracted, and the results are forwarded to the response team.

2. The system of claim 1.

5. The automatic response generation unit Learn from past response data to generate appropriate and effective automated responses 2. The system of claim 1.

6. The measure proposal unit Using the generation AI, the system learns from past countermeasure data and proposes optimal countermeasures.

2. The system of claim 1.

7. The message evaluation unit Using emotion estimation function, estimate user emotions in real time and evaluate them according to changes in emotions.

2. The system of claim 1.

8. The feedback transfer unit Using emotion estimation functionality to analyze the emotions of feedback submitters and prioritize forwarding emotionally significant feedback 2. The system of claim 1.

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