System

Abstract

An object of a system according to an embodiment is to improve reception quality with respect to inquiries from customers.SOLUTION: A system includes an inquiry analysis unit, an answer generation unit, and a support unit. An inquiry analysis part analyzes inquiry contents from a customer. The answer generation unit generates an appropriate answer based on the content analyzed by the inquiry analysis unit. The support unit presents the answer generated by the answer generation unit to the operator.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 of inconsistent response quality to customer inquiries.

[0005] The system according to the embodiment aims to improve the quality of responses to inquiries from customers. [Means for solving the problem]

[0006] The system according to the embodiment includes an inquiry analysis unit, an answer generation unit, and a support unit. The inquiry analysis unit analyzes the content of an inquiry from a customer. The answer generation unit generates an appropriate answer based on the content analyzed by the inquiry analysis unit. The support unit presents the answer generated by the answer generation unit to an operator. [Effects of the Invention]

[0007] The system according to the embodiment can improve the quality of responses to inquiries from customers. [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) The call center response quality improvement system according to the embodiment of the present invention is a system in which a generation AI generates appropriate responses to customer inquiries and supports operators. As a result, the call center response quality improvement system can improve the efficiency and quality of responses.

[0029] A call center response quality improvement system according to an embodiment includes an inquiry analysis unit, an answer generation unit, and a support unit. The inquiry analysis unit analyzes customer inquiries. For example, the generation AI analyzes customer inquiries using natural language processing technology and understands the content of the inquiries. The inquiry analysis unit can also convert the spoken inquiry into text and analyze it using speech recognition technology. For example, if a customer inquires, "How do I return a product?", the generation AI analyzes the inquiry and generates an appropriate answer. The answer generation unit generates an appropriate answer based on the content analyzed by the inquiry analysis unit. For example, the generation AI generates an optimal answer for the inquiry using a pre-finished model. The generation AI can also extract and generate an appropriate answer from an FAQ database. For example, the generation AI generates an answer that includes specific steps, such as, "To return a product, please follow the steps below: 1. Pack the product, 2. Attach the return label, and 3. Send it to the specified address." The support unit presents the answer generated by the answer generation unit to an operator. For example, the generation AI provides appropriate answers in real time when an operator is responding to a customer. In addition, the support unit allows the generation AI to present appropriate answers so that operators can respond immediately to customer inquiries. As a result, the call center response quality improvement system according to the embodiment can respond quickly and appropriately to customer inquiries. For example, when a customer makes an inquiry, waiting time is reduced and a quick response is obtained, thereby improving customer satisfaction.

[0030] The inquiry analysis unit can generate more accurate answers by referencing the customer's past inquiry history and performing personalized analysis. For example, the generation AI in the inquiry analysis unit refers to the customer's past inquiry history and performs personalized analysis. For example, for a customer who has made a similar inquiry in the past, the optimal answer is generated based on previous answers. The inquiry analysis unit can also retrieve the customer's past inquiry history from a database and analyze it. For example, if a customer has inquired about how to return a product in the past, the optimal answer is generated based on that history. In this way, by referencing the customer's past inquiry history, more accurate answers can be generated.

[0031] The inquiry analysis unit can collect inquiry content from other communication channels such as customer social media or email, and perform an integrated analysis. For example, the generation AI in the inquiry analysis unit collects inquiry content from customer social media and email, and performs an integrated analysis. For example, it analyzes messages on Twitter and Facebook and centrally manages inquiry content. The inquiry analysis unit can also obtain data from an email server and analyze inquiry content using text analysis technology. For example, when a customer makes an inquiry via email, it analyzes the content and generates an appropriate response. This allows for an integrated analysis of inquiry content from multiple communication channels.

[0032] The inquiry analysis unit can have a multilingual analysis function so that it can respond to inquiries in different languages. For example, the generation AI in the inquiry analysis unit has a multilingual analysis function, so it can respond to inquiries in different languages. For example, it analyzes inquiries in multiple languages ​​such as English, Spanish, and Chinese. The inquiry analysis unit can also analyze the content of inquiries in different languages ​​using a translation engine. For example, if a customer makes an inquiry in French, the content is translated and analyzed. This makes it possible to respond to inquiries in different languages.

[0033] The answer generation unit can generate personalized answers by referencing the customer's past purchase history and behavioral data. For example, the answer generation unit uses a generation AI to refer to the customer's past purchase history and generate personalized answers. For example, in response to an inquiry about a product purchased in the past, the optimal answer is provided based on the purchase history. The answer generation unit can also analyze customer behavioral data to generate personalized answers. For example, in response to an inquiry about a product that the customer viewed on a website, the optimal answer is provided based on that behavioral data. In this way, personalized answers can be generated by referencing the customer's past purchase history and behavioral data.

[0034] The answer generation unit can introduce an algorithm that generates multiple answer candidates and selects the optimal answer from among them. For example, the answer generation unit introduces an algorithm in which a generation AI generates multiple answer candidates and selects the optimal answer from among them. For example, the answer candidates are scored and the answer with the highest score is provided. The answer generation unit can also generate answer candidates using a machine learning algorithm and select the optimal answer. For example, the optimal answer is selected based on the customer's past response history and the content of the current inquiry. This allows the optimal answer to be selected from multiple answer candidates.

[0035] The answer generation unit generates answers that include visual content, and can provide customers with information that is visually easy to understand. For example, the answer generation unit uses a generation AI to generate answers that include visual content, and can provide customers with information that is visually easy to understand. For example, the answer may include a video that explains how to use a product. The answer generation unit can also generate answers that include images and infographics. For example, the features of a product may be explained using diagrams. This allows answers that include visual content to be generated, and can provide customers with information that is visually easy to understand.

[0036] The answer generation unit can work in cooperation with other AI systems to generate answers that reflect the latest information in real time. For example, the generation AI of the answer generation unit works in cooperation with an inventory management system to generate answers that reflect the latest inventory information in real time. For example, it immediately answers the stock status of a product. The answer generation unit can also work in cooperation with a delivery system to generate answers that reflect the latest delivery status. For example, it answers the delivery status of a product in real time. This makes it possible to work in cooperation with other AI systems to generate answers that reflect the latest information in real time.

[0037] The support unit can learn the past response history of the agent and provide support that is optimized for each agent. For example, the support unit uses a generation AI to learn the past response history of the agent and provide support that is optimized for each agent. For example, it presents the optimal answer based on the agent's areas of expertise and past success stories. The support unit can also retrieve and analyze the agent's response history from a database. For example, it can provide optimal support based on feedback from customers that the agent has dealt with in the past. This makes it possible to provide support that is optimized for each agent.

[0038] The support department can provide real-time feedback to operators while they are responding to calls, helping them improve their response skills. For example, the generative AI can provide real-time feedback to operators while they are responding to calls, helping them improve their response skills. For example, it can provide immediate advice on the language and tone used during the call. The support department can also analyze the content of the operator's response in real time and suggest areas for improvement. For example, it can suggest more effective ways of responding based on the customer's reaction. This makes it possible to provide real-time feedback to operators while they are responding to calls, helping them improve their response skills.

[0039] The support department can automatically evaluate the responses of operators and generate performance reports periodically. For example, the support department uses a generation AI to automatically evaluate the responses of operators and generate performance reports periodically. For example, the evaluation is based on the quality of the response and customer satisfaction. The support department can also use a machine learning algorithm to evaluate the responses of operators and suggest areas for improvement. For example, the speed and accuracy of the response is evaluated and reflected in the performance report. This makes it possible to automatically evaluate the responses of operators and generate performance reports periodically.

[0040] The support department can present other operators' success stories in real time while the operator is responding to a call, allowing them to use them as reference. For example, the generation AI can present other operators' success stories in real time while the operator is responding to a call, allowing them to use them as reference. For example, it can suggest the optimal way to respond based on past success stories. The support department can also retrieve other operators' success stories from a database and present them in real time. For example, it can suggest effective ways to respond based on customer feedback. This allows other operators' success stories to be presented in real time while the operator is responding to a call, allowing them to use them as reference.

[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] The inquiry analysis unit can generate more accurate answers by referencing the customer's past inquiry history and performing personalized analysis. For example, the generation AI can refer to the customer's past inquiry history and perform personalized analysis. For example, for a customer who has made a similar inquiry in the past, the optimal answer can be generated based on the previous answers. The inquiry analysis unit can also retrieve and analyze the customer's past inquiry history from a database. For example, if a customer has previously inquired about how to return a product, the optimal answer can be generated based on that history. In this way, by referencing the customer's past inquiry history, more accurate answers can be generated.

[0043] The inquiry analysis unit can collect inquiry content from other communication channels, such as customers' social media or email, and perform integrated analysis. For example, the generation AI collects inquiry content from customers' social media and email, and performs integrated analysis. For example, it analyzes messages from Twitter and Facebook and centrally manages inquiry content. The inquiry analysis unit can also obtain data from the email server and analyze inquiry content using text analysis technology. For example, when a customer makes an inquiry via email, it analyzes the content and generates an appropriate response. This allows for integrated analysis of inquiry content from multiple communication channels.

[0044] The inquiry analysis unit can have a multilingual analysis function so that it can handle inquiries in different languages. For example, the generation AI can have a multilingual analysis function and handle inquiries in different languages. For example, it can analyze inquiries in multiple languages ​​such as English, Spanish, and Chinese. The inquiry analysis unit can also use a translation engine to analyze the content of inquiries in different languages. For example, if a customer makes an inquiry in French, the content is translated and analyzed. This allows inquiries in different languages ​​to be handled.

[0045] The answer generation unit can generate personalized answers by referencing a customer's past purchase history and behavioral data. For example, the generation AI references a customer's past purchase history to generate personalized answers. For example, in response to an inquiry about a product purchased in the past, the optimal answer is provided based on the purchase history. The answer generation unit can also analyze customer behavioral data to generate personalized answers. For example, in response to an inquiry about a product a customer viewed on a website, the optimal answer is provided based on that behavioral data. In this way, personalized answers can be generated by referencing a customer's past purchase history and behavioral data.

[0046] The answer generation unit can introduce an algorithm that generates multiple answer candidates and selects the optimal answer from them. For example, the generation AI generates multiple answer candidates and introduces an algorithm that selects the optimal answer from them. For example, the answer candidates are scored and the answer with the highest score is provided. The answer generation unit can also generate answer candidates using a machine learning algorithm and select the optimal answer. For example, the optimal answer is selected based on the customer's past response history and the content of the current inquiry. This allows the optimal answer to be selected from multiple answer candidates.

[0047] The answer generation unit generates answers that include visual content, allowing the customer to understand information that is easy to understand visually. For example, the generation AI generates answers that include visual content, allowing the customer to understand information that is easy to understand visually. For example, the answer may include a video that explains how to use a product. The answer generation unit can also generate answers that include images and infographics. For example, the features of a product may be explained using illustrations. This allows the generation of answers that include visual content, allowing the customer to understand information that is easy to understand visually.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The inquiry analysis unit analyzes the content of the customer inquiry. For example, the generation AI uses natural language processing technology to analyze the content of the customer inquiry and understand its content. The inquiry analysis unit can also use speech recognition technology to convert the content of the voice inquiry into text and analyze it. Step 2: The answer generation unit generates an appropriate answer based on the content analyzed by the inquiry analysis unit. For example, the generation AI uses a pre-fine-tuned model to generate the optimal answer to the inquiry. The generation AI can also extract and generate appropriate answers from an FAQ database. Step 3: The support unit presents the answer generated by the answer generation unit to the operator. For example, the generation AI provides appropriate answers in real time when the operator is dealing with a customer. The support unit also allows the generation AI to present appropriate answers so that the operator can immediately respond to customer inquiries.

[0050] (Example 2) The call center response quality improvement system according to the embodiment of the present invention is a system in which a generation AI generates appropriate responses to customer inquiries and supports operators. As a result, the call center response quality improvement system can improve the efficiency and quality of responses.

[0051] A call center response quality improvement system according to an embodiment includes an inquiry analysis unit, an answer generation unit, and a support unit. The inquiry analysis unit analyzes customer inquiries. For example, the generation AI analyzes customer inquiries using natural language processing technology and understands the content of the inquiries. The inquiry analysis unit can also convert the spoken inquiry into text and analyze it using speech recognition technology. For example, if a customer inquires, "How do I return a product?", the generation AI analyzes the inquiry and generates an appropriate answer. The answer generation unit generates an appropriate answer based on the content analyzed by the inquiry analysis unit. For example, the generation AI generates an optimal answer for the inquiry using a pre-finished model. The generation AI can also extract and generate an appropriate answer from an FAQ database. For example, the generation AI generates an answer that includes specific steps, such as, "To return a product, please follow the steps below: 1. Pack the product, 2. Attach the return label, and 3. Send it to the specified address." The support unit presents the answer generated by the answer generation unit to an operator. For example, the generation AI provides appropriate answers in real time when an operator is responding to a customer. In addition, the support unit allows the generation AI to present appropriate answers so that operators can respond immediately to customer inquiries. As a result, the call center response quality improvement system according to the embodiment can respond quickly and appropriately to customer inquiries. For example, when a customer makes an inquiry, waiting time is reduced and a quick response is obtained, thereby improving customer satisfaction.

[0052] The inquiry analysis unit can analyze the tone and speed of a customer's voice, infer their emotional state, and determine the urgency of the inquiry. For example, the inquiry analysis unit uses a generation AI to analyze the tone and speed of a customer's voice in real time to infer their emotional state. For example, if the voice tone is high and the speed is fast, it will determine that the urgency is high and will respond to the inquiry as a priority. The inquiry analysis unit can also use voice analysis technology to analyze the tone and speed of a customer's voice to infer their emotional state. For example, if the voice tone is low and the speed is slow, it will determine that the urgency is low. This allows the urgency to be determined based on the customer's emotional state, allowing for a prompt response.

[0053] The inquiry analysis unit can generate more accurate answers by referencing the customer's past inquiry history and performing personalized analysis. For example, the generation AI in the inquiry analysis unit refers to the customer's past inquiry history and performs personalized analysis. For example, for a customer who has made a similar inquiry in the past, the optimal answer is generated based on previous answers. The inquiry analysis unit can also retrieve the customer's past inquiry history from a database and analyze it. For example, if a customer has inquired about how to return a product in the past, the optimal answer is generated based on that history. In this way, by referencing the customer's past inquiry history, more accurate answers can be generated.

[0054] The inquiry analysis unit can use an emotion estimation function to analyze the emotional state of the customer in real time and respond according to the emotion. For example, the generation AI in the inquiry analysis unit can analyze the emotional state of the customer in real time and respond according to the emotion. For example, if the customer is angry, the response can be made in a calm tone. The inquiry analysis unit can also analyze the emotional state of the customer using an emotion estimation algorithm and respond accordingly. For example, if the customer is sad, the response can be made in a gentle tone. In this way, customer satisfaction can be improved by responding according to the customer's emotional state.

[0055] The inquiry analysis unit can collect inquiry content from other communication channels such as customer social media or email, and perform an integrated analysis. For example, the generation AI in the inquiry analysis unit collects inquiry content from customer social media and email, and performs an integrated analysis. For example, it analyzes messages on Twitter and Facebook and centrally manages inquiry content. The inquiry analysis unit can also obtain data from an email server and analyze inquiry content using text analysis technology. For example, when a customer makes an inquiry via email, it analyzes the content and generates an appropriate response. This allows for an integrated analysis of inquiry content from multiple communication channels.

[0056] The inquiry analysis unit can have a multilingual analysis function so that it can respond to inquiries in different languages. For example, the generation AI in the inquiry analysis unit has a multilingual analysis function, so it can respond to inquiries in different languages. For example, it analyzes inquiries in multiple languages ​​such as English, Spanish, and Chinese. The inquiry analysis unit can also analyze the content of inquiries in different languages ​​using a translation engine. For example, if a customer makes an inquiry in French, the content is translated and analyzed. This makes it possible to respond to inquiries in different languages.

[0057] The inquiry analysis unit can use the emotion estimation function to generate a customized response script according to the emotional state of the customer. The inquiry analysis unit, for example, uses the emotion estimation function to generate a customized response script according to the emotional state of the customer. For example, if the customer is angry, it provides a calm and polite script. The inquiry analysis unit can also customize the response script by having the generation AI analyze the emotional state of the customer. For example, if the customer is feeling anxious, it provides a reassuring script. This makes it possible to generate a customized response script according to the emotional state of the customer.

[0058] The answer generation unit can generate personalized answers by referencing the customer's past purchase history and behavioral data. For example, the answer generation unit uses a generation AI to refer to the customer's past purchase history and generate personalized answers. For example, in response to an inquiry about a product purchased in the past, the optimal answer is provided based on the purchase history. The answer generation unit can also analyze customer behavioral data to generate personalized answers. For example, in response to an inquiry about a product that the customer viewed on a website, the optimal answer is provided based on that behavioral data. In this way, personalized answers can be generated by referencing the customer's past purchase history and behavioral data.

[0059] The answer generation unit can introduce an algorithm that generates multiple answer candidates and selects the optimal answer from among them. For example, the answer generation unit introduces an algorithm in which a generation AI generates multiple answer candidates and selects the optimal answer from among them. For example, the answer candidates are scored and the answer with the highest score is provided. The answer generation unit can also generate answer candidates using a machine learning algorithm and select the optimal answer. For example, the optimal answer is selected based on the customer's past response history and the content of the current inquiry. This allows the optimal answer to be selected from multiple answer candidates.

[0060] The answer generation unit can use the emotion estimation function to generate answers with adjusted tone and expression according to the emotional state of the customer. The answer generation unit, for example, uses the emotion estimation function to generate answers with adjusted tone and expression according to the emotional state of the customer. For example, if the customer is angry, the answer is provided in a calm and polite tone. The answer generation unit can also adjust the tone and expression by having the generation AI analyze the emotional state of the customer. For example, if the customer is happy, the answer is provided in a bright tone. This makes it possible to generate answers with adjusted tone and expression according to the emotional state of the customer.

[0061] The answer generation unit generates answers that include visual content, and can provide customers with information that is visually easy to understand. For example, the answer generation unit uses a generation AI to generate answers that include visual content, and can provide customers with information that is visually easy to understand. For example, the answer may include a video that explains how to use a product. The answer generation unit can also generate answers that include images and infographics. For example, the features of a product may be explained using diagrams. This allows answers that include visual content to be generated, and can provide customers with information that is visually easy to understand.

[0062] The answer generation unit can work in cooperation with other AI systems to generate answers that reflect the latest information in real time. For example, the generation AI of the answer generation unit works in cooperation with an inventory management system to generate answers that reflect the latest inventory information in real time. For example, it immediately answers the stock status of a product. The answer generation unit can also work in cooperation with a delivery system to generate answers that reflect the latest delivery status. For example, it answers the delivery status of a product in real time. This makes it possible to work in cooperation with other AI systems to generate answers that reflect the latest information in real time.

[0063] The answer generation unit can automatically generate a follow-up message according to the emotional state of the customer using the emotion estimation function. The answer generation unit, for example, automatically generates a follow-up message according to the emotional state of the customer using the emotion estimation function. For example, if the customer is dissatisfied, a message including an apology and a solution is sent. The answer generation unit can also generate a follow-up message by using the generation AI to analyze the emotional state of the customer. For example, if the customer is satisfied, a message of gratitude is sent. This makes it possible to automatically generate a follow-up message according to the emotional state of the customer.

[0064] The support unit can learn the past response history of the agent and provide support that is optimized for each agent. For example, the support unit uses a generation AI to learn the past response history of the agent and provide support that is optimized for each agent. For example, it presents the optimal answer based on the agent's areas of expertise and past success stories. The support unit can also retrieve and analyze the agent's response history from a database. For example, it can provide optimal support based on feedback from customers that the agent has dealt with in the past. This makes it possible to provide support that is optimized for each agent.

[0065] The support department can provide real-time feedback to operators while they are responding to calls, helping them improve their response skills. For example, the generative AI can provide real-time feedback to operators while they are responding to calls, helping them improve their response skills. For example, it can provide immediate advice on the language and tone used during the call. The support department can also analyze the content of the operator's response in real time and suggest areas for improvement. For example, it can suggest more effective ways of responding based on the customer's reaction. This makes it possible to provide real-time feedback to operators while they are responding to calls, helping them improve their response skills.

[0066] The support unit can use the emotion estimation function to analyze the emotional state of the operator and provide advice to reduce stress. For example, the support unit can use the emotion estimation function to analyze the emotional state of the operator and provide advice to reduce stress. For example, if the operator is feeling stressed, the support unit can provide advice to relax. The support unit can also use the generation AI to analyze the emotional state of the operator and provide specific guidelines for stress management. For example, if the operator is tired, the support unit can suggest that they take a break. This makes it possible to analyze the emotional state of the operator and provide advice to reduce stress.

[0067] The support department can automatically evaluate the responses of operators and generate performance reports periodically. For example, the support department uses a generation AI to automatically evaluate the responses of operators and generate performance reports periodically. For example, the evaluation is based on the quality of the response and customer satisfaction. The support department can also use a machine learning algorithm to evaluate the responses of operators and suggest areas for improvement. For example, the speed and accuracy of the response is evaluated and reflected in the performance report. This makes it possible to automatically evaluate the responses of operators and generate performance reports periodically.

[0068] The support department can present other operators' success stories in real time while the operator is responding to a call, allowing them to use them as reference. For example, the generation AI can present other operators' success stories in real time while the operator is responding to a call, allowing them to use them as reference. For example, it can suggest the optimal way to respond based on past success stories. The support department can also retrieve other operators' success stories from a database and present them in real time. For example, it can suggest effective ways to respond based on customer feedback. This allows other operators' success stories to be presented in real time while the operator is responding to a call, allowing them to use them as reference.

[0069] The support unit can use the emotion estimation function to suggest break timings based on the operator's emotional state. For example, the support unit can use the emotion estimation function to suggest break timings based on the operator's emotional state. For example, if the operator is tired, it will suggest that they take a break. The support unit can also use the generation AI to analyze the operator's emotional state and suggest break timings. For example, if the operator is feeling stressed, it will suggest that they take a break to relax. This makes it possible to suggest break timings based on the operator's emotional state.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The inquiry analysis unit can analyze the tone and speed of a customer's voice to infer their emotional state and determine the urgency of the inquiry. For example, the generation AI can analyze the tone and speed of a customer's voice in real time to infer their emotional state. For example, if the voice tone is high and the speed is fast, it can determine that the urgency is high and respond to the inquiry as a priority. The inquiry analysis unit can also use voice analysis technology to analyze the tone and speed of a customer's voice to infer their emotional state. For example, if the voice tone is low and the speed is slow, it can determine that the urgency is low. This allows the system to determine the urgency based on the customer's emotional state and respond quickly.

[0072] The inquiry analysis unit can generate more accurate answers by referencing the customer's past inquiry history and performing personalized analysis. For example, the generation AI can refer to the customer's past inquiry history and perform personalized analysis. For example, for a customer who has made a similar inquiry in the past, the optimal answer can be generated based on the previous answers. The inquiry analysis unit can also retrieve and analyze the customer's past inquiry history from a database. For example, if a customer has previously inquired about how to return a product, the optimal answer can be generated based on that history. In this way, by referencing the customer's past inquiry history, more accurate answers can be generated.

[0073] The inquiry analysis unit can use an emotion estimation function to analyze the emotional state of the customer in real time and respond according to that emotion. For example, the generation AI can analyze the emotional state of the customer in real time and respond according to that emotion. For example, if the customer is angry, it will respond in a calm tone. The inquiry analysis unit can also use an emotion estimation algorithm to analyze the emotional state of the customer and respond accordingly. For example, if the customer is sad, it will respond in a gentle tone. This improves customer satisfaction by responding according to the customer's emotional state.

[0074] The inquiry analysis unit can collect inquiry content from other communication channels, such as customers' social media or email, and perform integrated analysis. For example, the generation AI collects inquiry content from customers' social media and email, and performs integrated analysis. For example, it analyzes messages from Twitter and Facebook and centrally manages inquiry content. The inquiry analysis unit can also obtain data from the email server and analyze inquiry content using text analysis technology. For example, when a customer makes an inquiry via email, it analyzes the content and generates an appropriate response. This allows for integrated analysis of inquiry content from multiple communication channels.

[0075] The inquiry analysis unit can have a multilingual analysis function so that it can handle inquiries in different languages. For example, the generation AI can have a multilingual analysis function and handle inquiries in different languages. For example, it can analyze inquiries in multiple languages ​​such as English, Spanish, and Chinese. The inquiry analysis unit can also use a translation engine to analyze the content of inquiries in different languages. For example, if a customer makes an inquiry in French, the content is translated and analyzed. This allows inquiries in different languages ​​to be handled.

[0076] The inquiry analysis unit can use the emotion estimation function to generate a customized response script according to the customer's emotional state. For example, the emotion estimation function can be used to generate a customized response script according to the customer's emotional state. For example, if the customer is angry, a calm and polite script can be provided. The inquiry analysis unit can also customize the response script by having the generation AI analyze the customer's emotional state. For example, if the customer is feeling anxious, a reassuring script can be provided. This makes it possible to generate a customized response script according to the customer's emotional state.

[0077] The answer generation unit can generate personalized answers by referencing a customer's past purchase history and behavioral data. For example, the generation AI references a customer's past purchase history to generate personalized answers. For example, in response to an inquiry about a product purchased in the past, the optimal answer is provided based on the purchase history. The answer generation unit can also analyze customer behavioral data to generate personalized answers. For example, in response to an inquiry about a product a customer viewed on a website, the optimal answer is provided based on that behavioral data. In this way, personalized answers can be generated by referencing a customer's past purchase history and behavioral data.

[0078] The answer generation unit can introduce an algorithm that generates multiple answer candidates and selects the optimal answer from them. For example, the generation AI generates multiple answer candidates and introduces an algorithm that selects the optimal answer from them. For example, the answer candidates are scored and the answer with the highest score is provided. The answer generation unit can also generate answer candidates using a machine learning algorithm and select the optimal answer. For example, the optimal answer is selected based on the customer's past response history and the content of the current inquiry. This allows the optimal answer to be selected from multiple answer candidates.

[0079] The answer generation unit can use the emotion estimation function to generate answers with adjusted tone and expression according to the customer's emotional state. For example, the emotion estimation function can be used to generate answers with adjusted tone and expression according to the customer's emotional state. For example, if the customer is angry, the answer can be provided in a calm and polite tone. The answer generation unit can also adjust the tone and expression by having the generation AI analyze the customer's emotional state. For example, if the customer is happy, the answer can be provided in a bright tone. This makes it possible to generate answers with adjusted tone and expression according to the customer's emotional state.

[0080] The answer generation unit generates answers that include visual content, allowing the customer to understand information that is easy to understand visually. For example, the generation AI generates answers that include visual content, allowing the customer to understand information that is easy to understand visually. For example, the answer may include a video that explains how to use a product. The answer generation unit can also generate answers that include images and infographics. For example, the features of a product may be explained using illustrations. This allows the generation of answers that include visual content, allowing the customer to understand information that is easy to understand visually.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The inquiry analysis unit analyzes the content of the customer inquiry. For example, the generation AI uses natural language processing technology to analyze the content of the customer inquiry and understand its content. The inquiry analysis unit can also use speech recognition technology to convert the content of the voice inquiry into text and analyze it. Step 2: The answer generation unit generates an appropriate answer based on the content analyzed by the inquiry analysis unit. For example, the generation AI uses a pre-fine-tuned model to generate the optimal answer to the inquiry. The generation AI can also extract and generate appropriate answers from an FAQ database. Step 3: The support unit presents the answer generated by the answer generation unit to the operator. For example, the generation AI provides appropriate answers in real time when the operator is dealing with a customer. The support unit also allows the generation AI to present appropriate answers so that the operator can immediately respond to customer inquiries.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. an inquiry analysis unit that analyzes the content of inquiries from customers; a response generation unit that generates an appropriate response based on the content analyzed by the query analysis unit; a support unit that presents the answer generated by the answer generation unit to an operator. A system characterized by:

2. The query analysis unit Analyzing the tone and speed of the customer's voice, inferring their emotional state and determining the urgency of the inquiry 2. The system of claim 1.

3. The query analysis unit Collect and comprehensively analyze the content of the inquiry from other communication channels such as the customer's social media or email.

2. The system of claim 1.

4. The answer generation unit Generate personalized responses by referencing the customer's past purchase history and behavioral data 2. The system of claim 1.

5. The answer generation unit The answer is generated to include visual content, providing the customer with visually easy-to-understand information.

2. The system of claim 1.

6. The support unit Learns the past response history of the operator and provides support optimized for each operator 2. The system of claim 1.

7. The support unit Analyzing the operator's emotional state and providing advice to reduce stress 2. The system of claim 1.

8. The support unit Propose break timing according to the operator's emotional state 2. The system of claim 1.

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