System

Abstract

An object of a system according to an embodiment is to grasp a situation of a customer and provide appropriate support.SOLUTION: A system includes a collection part, an analysis part, and an answer part. The collection part collects use service information and failure information of a customer. The analysis unit analyzes the information collected by the collection unit and grasps the situation of the customer. The answer unit provides a specific answer based on the analysis result obtained by the analysis unit.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] With conventional technology, it is difficult to provide appropriate support tailored to the customer's situation, and mechanical responses can lead to a decline in customer satisfaction.

[0005] The system according to the embodiment aims to understand the situation of the customer and provide appropriate support. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a response unit. The collection unit collects information on services used by customers and fault information. The analysis unit analyzes the information collected by the collection unit to understand the customer's situation. The response unit provides a specific response based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the situation of the customer and provide appropriate support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A user support system according to an embodiment of the present invention uses AI to recognize customer background information and provide a warm and sympathetic service. The user support system collects and analyzes information about the services and outages a customer uses and provides appropriate responses. For example, when an inquiry is received, the user support system automatically identifies the customer's service information and outage information related to that service. The user support system then associates the customer's strong tone with the above event. This allows the AI ​​to instantly respond with the information the customer is looking for, such as "What's happening now?" and "When will the outage be fixed?" This immediately calms the customer's mood, ultimately leading to improved customer satisfaction. This allows the user support system to recognize customer background information and provide a warm and sympathetic service. For example, by collecting and analyzing information about the services and outages a customer uses and providing appropriate responses, the customer's mood immediately calms down, ultimately leading to improved customer satisfaction.

[0029] A user support system according to an embodiment includes a collection unit, an analysis unit, and a response unit. The collection unit collects information about services used by customers and failure information. For example, the collection unit collects information such as the service's operating status and past failure history. The collection unit can also automatically collect information about services used by customers and failure information using AI. The analysis unit analyzes the information collected by the collection unit to understand the customer's situation. For example, the analysis unit analyzes the customer's tone of voice and past inquiries to understand the current situation. The analysis unit can also analyze the collected information using AI. The response unit provides an appropriate response based on the analysis results obtained by the analysis unit. For example, the response unit provides a specific response such as, "The service is currently experiencing a failure, and recovery work is underway. The estimated recovery time is ____." The response unit can also automatically generate an appropriate response based on the analysis results using AI. This allows the user support system according to an embodiment to recognize the customer's background information and provide a warm and welcoming response. For example, by collecting and analyzing information about services used by customers and failure information and providing an appropriate response, the customer's reaction is immediately reduced, ultimately leading to improved customer service.

[0030] The user support system includes a tone analysis unit that analyzes a customer's tone and provides feedback to the answering unit. The tone analysis unit analyzes a customer's tone and provides feedback to the answering unit. For example, the tone analysis unit analyzes a customer's tone using voice recognition technology. The tone analysis unit can also analyze a customer's tone using sentiment analysis technology. Furthermore, the tone analysis unit can analyze a customer's tone using text analysis technology. In this way, the tone analysis unit can analyze a customer's tone and provide feedback to the answering unit, thereby providing a more appropriate answer. For example, by analyzing a customer's tone and providing feedback to the answering unit, it can provide an appropriate answer according to the customer's emotions.

[0031] The user support system includes a profile analysis unit that analyzes customer profile information and provides feedback to the answering unit. The profile analysis unit analyzes customer profile information and provides feedback to the answering unit. For example, the profile analysis unit analyzes profile information such as the customer's age, gender, occupation, and hobbies. The profile analysis unit can also analyze customer profile information using AI. This allows the profile analysis unit to analyze the customer's profile information and provide feedback to the answering unit, thereby providing more appropriate answers. For example, analyzing the customer's profile information and providing feedback to the answering unit allows the system to provide appropriate answers tailored to the customer's needs.

[0032] The collection unit can analyze the customer's past usage history and select a specific information collection method. For example, the collection unit prioritizes collecting information on services that the customer has frequently used in the past. The collection unit can also re-collect failure information that the customer has inquired about in the past. Furthermore, the collection unit can automatically collect related information from the customer's past usage history. This allows the collection unit to select the optimal information collection method by analyzing the customer's past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the customer's past usage history data into a generation AI and have the generation AI select the optimal information collection method.

[0033] The collection unit can filter information based on the customer's current situation and areas of interest at the time of collection. For example, the collection unit prioritizes collection of information related to services currently being used by the customer. The collection unit can also filter and collect information related to areas of interest to the customer. Furthermore, the collection unit can collect only necessary information depending on the customer's current situation. This allows the collection unit to collect only necessary information by filtering information based on the customer's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the customer's current situation to a generation AI and have the generation AI perform information filtering.

[0034] The collection unit can select a specific collection means depending on the customer's input method at the time of collection. For example, if a customer makes a voice inquiry, the collection unit can collect information by analyzing the voice data. In addition, if a customer makes a text inquiry, the collection unit can also collect information by analyzing the text data. Furthermore, if a customer sends an image, the collection unit can also collect information by analyzing the image data. This allows the collection unit to efficiently collect information by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the optimal collection means.

[0035] The collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information when collecting the information. For example, if the customer is in a specific area, the collection unit prioritizes collecting outage information related to that area. In addition, if the customer is traveling, the collection unit can also collect information related to the customer's travel destination. Furthermore, if the customer is at home, the collection unit can collect service information around the customer's home. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's geographical location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0036] The collection unit can analyze the customer's social media activities at the time of collection and collect related information. For example, the collection unit can collect information about services mentioned by the customer on social media. The collection unit can also analyze the content of the customer's social media posts and collect related error information. Furthermore, the collection unit can also refer to the activities of the customer's friends on social media. In this way, the collection unit can collect related information by analyzing the customer's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's social media data into a generation AI and have the generation AI collect related information.

[0037] The collection unit can customize the collection method by reflecting the customer's past feedback at the time of collection. The collection unit can adjust the collection method based on, for example, feedback provided by the customer in the past. The collection unit can also prioritize the collection of necessary information from the customer's past feedback. Furthermore, the collection unit can also optimize the collection means by reflecting the customer's feedback. In this way, the collection unit can optimize the collection method by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past feedback data into the generation AI and have the generation AI customize the collection method.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the information collected during the analysis. For example, the analysis unit performs a detailed analysis of important fault information. The analysis unit can also perform a concise analysis of service usage information. Furthermore, the analysis unit can perform a key analysis of past inquiry records. This allows the analysis unit to perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the collected information to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a fault analysis algorithm to fault information. The analysis unit can also apply a service analysis algorithm to service usage information. Furthermore, the analysis unit can apply a history analysis algorithm to past inquiry records. In this way, the analysis unit can perform highly accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0040] The analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during analysis. The analysis unit, for example, performs the current analysis based on the customer's past analysis results. The analysis unit can also extract relevant information from the past analysis results and reflect it in the analysis. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the customer's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0041] The analysis unit can determine the analysis priority based on the time when the information was submitted during analysis. For example, the analysis unit gives the highest priority to analyzing the most recent fault information. The analysis unit can also determine the analysis priority by referring to past inquiry records. Furthermore, the analysis unit can perform a concise analysis for information that was submitted a long time ago. This allows the analysis unit to perform efficient analysis by determining the analysis priority based on the time when the information was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information related to fault information. The analysis unit can also prioritize analysis of information related to service usage information. Furthermore, the analysis unit can also prioritize analysis of information related to past inquiry records. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0043] The analysis unit can adjust the use of technical terminology during analysis according to the customer's level of expertise. For example, if the customer has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the customer's level of expertise. This allows the analysis unit to provide analysis results that are easier to understand by adjusting the use of technical terminology according to the customer's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0044] The answering unit can adjust the level of detail of the answer based on the importance of the information when answering. For example, the answering unit provides a detailed answer for important fault information. The answering unit can also provide a concise answer for used service information. Furthermore, the answering unit can provide an answer that focuses on the main points for past inquiry records. In this way, the answering unit can provide an efficient answer by adjusting the level of detail of the answer based on the importance of the information. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input information importance data to the generating AI and have the generating AI adjust the level of detail of the answer.

[0045] The answering unit can apply different answering algorithms depending on the category of information when answering. For example, the answering unit applies a fault answering algorithm to fault information. The answering unit can also apply a service answering algorithm to service usage information. Furthermore, the answering unit can apply a history answering algorithm to past inquiry records. In this way, the answering unit can provide highly accurate answers by applying different answering algorithms depending on the category of information. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input information category data to the generation AI and cause the generation AI to apply different answering algorithms.

[0046] The answering unit can improve the accuracy of the answer by referring to the customer's past answer results when answering. The answering unit, for example, provides a current answer based on the customer's past answer results. The answering unit can also extract relevant information from the past answer results and reflect it in the answer. Furthermore, the answering unit can adjust the answering algorithm by referring to the customer's past answer results. In this way, the answering unit can improve the accuracy of the answer by referring to the customer's past answer results. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the customer's past answer result data into the generation AI and have the generation AI improve the accuracy of the answer.

[0047] The answering unit can determine the priority of answers based on the time when the information was submitted when making an answer. For example, the answering unit gives the most recent fault information the highest priority. The answering unit can also determine the priority of answers by referring to past inquiry records. Furthermore, the answering unit can provide a concise answer for information that was submitted a long time ago. This allows the answering unit to provide an efficient answer by determining the priority of answers based on the time when the information was submitted. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input information submission time data into the generation AI and have the generation AI determine the priority of answers.

[0048] The answering unit can adjust the order of answers based on the relevance of information when answering. For example, the answering unit prioritizes information related to fault information. The answering unit can also prioritize information related to service usage information. Furthermore, the answering unit can also prioritize information related to past inquiry records. This allows the answering unit to adjust the order of answers based on the relevance of information, thereby providing an efficient answer. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input information relevance data to the generating AI and have the generating AI adjust the order of answers.

[0049] The answering unit can adjust the use of technical terminology in the answer depending on the customer's level of expertise when answering. For example, if the customer has technical expertise, the answering unit can use a lot of technical terminology in the answer. Alternatively, if the customer does not have technical expertise, the answering unit can use simple language in the answer. Furthermore, the answering unit can adjust the level of detail in the answer depending on the customer's level of expertise. This allows the answering unit to provide an answer that is easier to understand by adjusting the use of technical terminology in the answer depending on the customer's level of expertise. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input customer's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0050] The tone analysis unit can improve the accuracy of the analysis by referring to the customer's past tone data during tone analysis. The tone analysis unit, for example, analyzes the current tone based on the customer's past tone data. The tone analysis unit can also extract relevant information from the past tone data and reflect it in the analysis. Furthermore, the tone analysis unit can adjust the analysis algorithm by referring to the customer's past tone data. In this way, the tone analysis unit can improve the accuracy of the tone analysis by referring to the customer's past tone data. Some or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input the customer's past tone data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0051] The tone analysis unit can customize the means of analysis based on the customer's current situation during tone analysis. For example, if the customer is nervous, the tone analysis unit uses a tone analysis means that reduces tension. Furthermore, if the customer is relaxed, the tone analysis unit can also use a relaxed tone analysis means. Furthermore, if the customer is in a hurry, the tone analysis unit can also use a quick tone analysis means. In this way, the tone analysis unit can perform more appropriate tone analysis by customizing the means of analysis based on the customer's current situation. Some or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input data on the customer's current situation into the generation AI and have the generation AI customize the analysis means.

[0052] The inflection analysis unit can perform inflection analysis taking into account the geographical location information of the customer. For example, if the customer is in a specific region, the inflection analysis unit can perform inflection analysis taking into account the dialect and accent of that region. Furthermore, if the customer is traveling, the inflection analysis unit can perform inflection analysis taking into account the language and culture of the destination. Furthermore, if the customer is at home, the inflection analysis unit can perform inflection analysis taking into account the language and culture of the area around the customer's home. This allows the inflection analysis unit to perform more appropriate inflection analysis by taking into account the geographical location information of the customer. Some or all of the above-mentioned processing in the inflection analysis unit may be performed using, or without, AI, for example. For example, the inflection analysis unit can input the customer's geographical location information data into the generation AI and have the generation AI perform the analysis.

[0053] The tone analysis unit can analyze the customer's social media activity during tone analysis to improve the accuracy of the analysis. The tone analysis unit performs analysis, for example, by referring to the tone used by the customer on social media. The tone analysis unit can also analyze the content of the customer's social media posts to analyze the related tone. Furthermore, the tone analysis unit can analyze the related tone by referring to the activity of the customer's friends on social media. In this way, the tone analysis unit can improve the accuracy of the tone analysis by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input the customer's social media data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0054] The profile analysis unit can improve the accuracy of the analysis by referring to the customer's past profile data during profile analysis. For example, the profile analysis unit analyzes the current profile based on the customer's past profile data. The profile analysis unit can also extract relevant information from the past profile data and reflect it in the analysis. Furthermore, the profile analysis unit can adjust the analysis algorithm by referring to the customer's past profile data. In this way, the profile analysis unit can improve the accuracy of the profile analysis by referring to the customer's past profile data. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's past profile data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0055] The profile analysis unit can customize the analysis means based on the customer's current situation during profile analysis. For example, if the customer is nervous, the profile analysis unit can use a profile analysis means that reduces tension. Also, if the customer is relaxed, the profile analysis unit can use a relaxed profile analysis means. Furthermore, if the customer is in a hurry, the profile analysis unit can use a quick profile analysis means. This allows the profile analysis unit to customize the analysis means based on the customer's current situation, thereby enabling more appropriate profile analysis. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's current situation data into the generation AI and have the generation AI customize the analysis means.

[0056] The profile analysis unit can take into account the customer's geographical location information when analyzing the profile. For example, if the customer is in a specific region, the profile analysis unit can take into account the culture and customs of that region when analyzing the profile. Furthermore, if the customer is traveling, the profile analysis unit can also take into account the culture and customs of the destination when analyzing the profile. Furthermore, if the customer is at home, the profile analysis unit can take into account the culture and customs of the area around the customer's home when analyzing the profile. This allows the profile analysis unit to perform more appropriate profile analysis by taking into account the customer's geographical location information. Some or all of the above-described processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's geographical location information data into the generation AI and have the generation AI perform the analysis.

[0057] The profile analysis unit can analyze a customer's social media activity during profile analysis to improve the accuracy of the analysis. For example, the profile analysis unit performs analysis by referring to the profile information used by the customer on social media. The profile analysis unit can also analyze the content of a customer's social media posts and analyze related profile information. Furthermore, the profile analysis unit can analyze related profile information by referring to the activities of the customer's friends on social media. In this way, the profile analysis unit can improve the accuracy of the profile analysis by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's social media data into a generation AI and have the generation AI improve the accuracy of the analysis.

[0058] The profile analysis unit can customize the analysis method by reflecting the customer's past feedback when analyzing the profile. The profile analysis unit can adjust the analysis method based on, for example, feedback provided by the customer in the past. The profile analysis unit can also prioritize analysis of necessary information from the customer's past feedback. Furthermore, the profile analysis unit can also optimize the analysis means by reflecting the customer's feedback. In this way, the profile analysis unit can optimize the analysis method by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's past feedback data into the generation AI and have the generation AI customize the analysis method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The user support system may further include a purchase history analysis unit that analyzes a customer's past purchase history and provides feedback to the response unit. The purchase history analysis unit analyzes information about products and services purchased in the past by the customer and provides information related to the current inquiry. For example, if a customer makes an inquiry about a product purchased in the past, detailed information about that product and related support information can be collected preferentially and provided to the response unit. The purchase history analysis unit may also analyze feedback and ratings on products purchased in the past by the customer and provide these to the response unit, thereby enabling the system to provide a more appropriate response. Furthermore, the purchase history analysis unit may analyze a customer's purchasing patterns and provide information to prepare for future inquiries.

[0061] The user support system may further include a social media analysis unit that analyzes customers' social media activities and provides feedback to the response unit. The social media analysis unit analyzes information and comments posted by customers on social media and provides information related to the current inquiry. For example, if a customer expresses dissatisfaction with a particular service on social media, collecting that information and providing it to the response unit will enable a faster and more appropriate response. The social media analysis unit may also analyze customers' social media activity patterns and provide information to prepare for future inquiries. Furthermore, the social media analysis unit may analyze the activities of customers' friends and followers and provide related information.

[0062] The user support system may further include a geographic information analysis unit that analyzes the customer's geographic location information and provides feedback to the answering unit. The geographic information analysis unit analyzes information about the customer's current location and places the customer has visited in the past, and provides information related to the current inquiry. For example, if the customer is using a service in a specific area, it can prioritize collecting fault information and service information related to that area and provide it to the answering unit. Furthermore, if the customer is traveling, the geographic information analysis unit can provide a more appropriate answer by providing information related to the customer's travel destination. Furthermore, the geographic information analysis unit can analyze the customer's geographic movement patterns and provide information to prepare for future inquiries.

[0063] The user support system may further include a feedback analysis unit that analyzes past customer feedback and provides feedback to the response unit. The feedback analysis unit analyzes feedback provided by customers in the past and provides information related to the current inquiry. For example, if a customer has previously expressed dissatisfaction with a particular service, collecting that information and providing it to the response unit will enable a faster and more appropriate response. The feedback analysis unit may also provide information to prepare for future inquiries based on past customer feedback. Furthermore, the feedback analysis unit may reflect customer feedback to optimize the content and method of responses.

[0064] The user support system can further include an inquiry history analysis unit that analyzes the customer's past inquiry history and provides feedback to the response unit. The inquiry history analysis unit analyzes the content and results of inquiries made by the customer in the past and provides information related to the current inquiry. For example, if a customer has previously inquired about the same problem, the system can refer to the response method and results at that time to provide a faster and more appropriate response. The inquiry history analysis unit can also provide information to prepare for future inquiries from the customer's past inquiry history. Furthermore, the inquiry history analysis unit can analyze customer inquiry patterns and optimize the content and method of responses.

[0065] The user support system may further include a purchase history analysis unit that analyzes a customer's past purchase history and provides feedback to the response unit. The purchase history analysis unit analyzes information about products and services purchased in the past by the customer and provides information related to the current inquiry. For example, if a customer makes an inquiry about a product purchased in the past, detailed information about that product and related support information can be collected preferentially and provided to the response unit. The purchase history analysis unit may also analyze feedback and ratings on products purchased in the past by the customer and provide these to the response unit, thereby enabling the system to provide a more appropriate response. Furthermore, the purchase history analysis unit may analyze a customer's purchasing patterns and provide information to prepare for future inquiries.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects information about the services used by customers and information about outages. For example, the collection unit collects information about the operation status of the service and past outage history. The collection unit can also use AI to automatically collect information about the services used by customers and outage information. Step 2: The analysis unit analyzes the information collected by the collection unit to understand the customer's situation. For example, the analysis unit analyzes the customer's tone of voice and past inquiry records to understand the current situation. The analysis unit can also analyze the collected information using AI. Step 3: The answering unit provides an appropriate answer based on the analysis results obtained by the analyzing unit. For example, the answering unit may provide a specific answer such as, "Currently, the service is experiencing an outage and recovery work is underway. The estimated time of recovery is ____." The answering unit can also automatically generate an appropriate answer based on the analysis results using AI.

[0068] (Example 2) A user support system according to an embodiment of the present invention uses AI to recognize customer background information and provide a warm and sympathetic service. The user support system collects and analyzes information about the services and outages a customer uses and provides appropriate responses. For example, when an inquiry is received, the user support system automatically identifies the customer's service information and outage information related to that service. The user support system then associates the customer's strong tone with the above event. This allows the AI ​​to instantly respond with the information the customer is looking for, such as "What's happening now?" and "When will the outage be fixed?" This immediately calms the customer's mood, ultimately leading to improved customer satisfaction. This allows the user support system to recognize customer background information and provide a warm and sympathetic service. For example, by collecting and analyzing information about the services and outages a customer uses and providing appropriate responses, the customer's mood immediately calms down, ultimately leading to improved customer satisfaction.

[0069] A user support system according to an embodiment includes a collection unit, an analysis unit, and a response unit. The collection unit collects information about services used by customers and failure information. For example, the collection unit collects information such as the service's operating status and past failure history. The collection unit can also automatically collect information about services used by customers and failure information using AI. The analysis unit analyzes the information collected by the collection unit to understand the customer's situation. For example, the analysis unit analyzes the customer's tone of voice and past inquiries to understand the current situation. The analysis unit can also analyze the collected information using AI. The response unit provides an appropriate response based on the analysis results obtained by the analysis unit. For example, the response unit provides a specific response such as, "The service is currently experiencing a failure, and recovery work is underway. The estimated recovery time is ____." The response unit can also automatically generate an appropriate response based on the analysis results using AI. This allows the user support system according to an embodiment to recognize the customer's background information and provide a warm and welcoming response. For example, by collecting and analyzing information about services used by customers and failure information and providing an appropriate response, the customer's reaction is immediately reduced, ultimately leading to improved customer service.

[0070] The user support system includes a tone analysis unit that analyzes a customer's tone and provides feedback to the answering unit. The tone analysis unit analyzes a customer's tone and provides feedback to the answering unit. For example, the tone analysis unit analyzes a customer's tone using voice recognition technology. The tone analysis unit can also analyze a customer's tone using sentiment analysis technology. Furthermore, the tone analysis unit can analyze a customer's tone using text analysis technology. In this way, the tone analysis unit can analyze a customer's tone and provide feedback to the answering unit, thereby providing a more appropriate answer. For example, by analyzing a customer's tone and providing feedback to the answering unit, it can provide an appropriate answer according to the customer's emotions.

[0071] The user support system includes a profile analysis unit that analyzes customer profile information and provides feedback to the answering unit. The profile analysis unit analyzes customer profile information and provides feedback to the answering unit. For example, the profile analysis unit analyzes profile information such as the customer's age, gender, occupation, and hobbies. The profile analysis unit can also analyze customer profile information using AI. This allows the profile analysis unit to analyze the customer's profile information and provide feedback to the answering unit, thereby providing more appropriate answers. For example, analyzing the customer's profile information and providing feedback to the answering unit allows the system to provide appropriate answers tailored to the customer's needs.

[0072] The collection unit can estimate the customer's emotions and determine the priority of information to be collected based on the estimated customer emotions. For example, if the customer is dissatisfied, the collection unit can prioritize collecting failure information. Furthermore, if the customer is confused, the collection unit can prioritize collecting service usage information. Furthermore, if the customer is calm, the collection unit can prioritize collecting past inquiry records. Thus, the collection unit can collect more appropriate information by prioritizing information based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input customer emotion data into the generation AI and have the generation AI perform emotion estimation.

[0073] The collection unit can analyze the customer's past usage history and select a specific information collection method. For example, the collection unit prioritizes collecting information on services that the customer has frequently used in the past. The collection unit can also re-collect failure information that the customer has inquired about in the past. Furthermore, the collection unit can automatically collect related information from the customer's past usage history. This allows the collection unit to select the optimal information collection method by analyzing the customer's past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the customer's past usage history data into a generation AI and have the generation AI select the optimal information collection method.

[0074] The collection unit can filter information based on the customer's current situation and areas of interest at the time of collection. For example, the collection unit prioritizes collection of information related to services currently being used by the customer. The collection unit can also filter and collect information related to areas of interest to the customer. Furthermore, the collection unit can collect only necessary information depending on the customer's current situation. This allows the collection unit to collect only necessary information by filtering information based on the customer's current situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the customer's current situation to a generation AI and have the generation AI perform information filtering.

[0075] The collection unit can select a specific collection means depending on the customer's input method at the time of collection. For example, if a customer makes a voice inquiry, the collection unit can collect information by analyzing the voice data. In addition, if a customer makes a text inquiry, the collection unit can also collect information by analyzing the text data. Furthermore, if a customer sends an image, the collection unit can also collect information by analyzing the image data. This allows the collection unit to efficiently collect information by selecting the optimal collection means depending on the customer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the customer's input data into a generation AI and have the generation AI select the optimal collection means.

[0076] The collection unit can estimate the customer's emotions and adjust the level of detail of the information to be collected based on the estimated customer emotions. For example, if the customer is feeling anxious, the collection unit can collect detailed information about the problem. Furthermore, if the customer is relaxed, the collection unit can collect concise information. Furthermore, if the customer is in a hurry, the collection unit can collect information that focuses on the main points. This allows the collection unit to adjust the level of detail of the information based on the customer's emotions, thereby collecting more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input customer emotion data into the generation AI and have the generation AI adjust the level of detail of the information.

[0077] The collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information when collecting the information. For example, if the customer is in a specific area, the collection unit prioritizes collecting outage information related to that area. In addition, if the customer is traveling, the collection unit can also collect information related to the customer's travel destination. Furthermore, if the customer is at home, the collection unit can collect service information around the customer's home. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's geographical location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0078] The collection unit can analyze the customer's social media activities at the time of collection and collect related information. For example, the collection unit can collect information about services mentioned by the customer on social media. The collection unit can also analyze the content of the customer's social media posts and collect related error information. Furthermore, the collection unit can also refer to the activities of the customer's friends on social media. In this way, the collection unit can collect related information by analyzing the customer's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the customer's social media data into a generation AI and have the generation AI collect related information.

[0079] The collection unit can customize the collection method by reflecting the customer's past feedback at the time of collection. The collection unit can adjust the collection method based on, for example, feedback provided by the customer in the past. The collection unit can also prioritize the collection of necessary information from the customer's past feedback. Furthermore, the collection unit can also optimize the collection means by reflecting the customer's feedback. In this way, the collection unit can optimize the collection method by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past feedback data into the generation AI and have the generation AI customize the collection method.

[0080] The analysis unit can estimate the customer's emotions and determine the analysis priorities based on the estimated customer emotions. For example, if the customer is dissatisfied, the analysis unit can prioritize analyzing fault information. Furthermore, if the customer is confused, the analysis unit can prioritize analyzing service usage information. Furthermore, if the customer is calm, the analysis unit can prioritize analyzing past inquiry records. This allows the analysis unit to determine the analysis priorities based on the customer's emotions, thereby enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the information collected during the analysis. For example, the analysis unit performs a detailed analysis of important fault information. The analysis unit can also perform a concise analysis of service usage information. Furthermore, the analysis unit can perform a key analysis of past inquiry records. This allows the analysis unit to perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the collected information to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a fault analysis algorithm to fault information. The analysis unit can also apply a service analysis algorithm to service usage information. Furthermore, the analysis unit can apply a history analysis algorithm to past inquiry records. In this way, the analysis unit can perform highly accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results during analysis. The analysis unit, for example, performs the current analysis based on the customer's past analysis results. The analysis unit can also extract relevant information from the past analysis results and reflect it in the analysis. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the customer's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0084] The analysis unit can estimate the customer's emotions and adjust the order of analysis based on the estimated customer emotions. For example, if the customer is feeling anxious, the analysis unit can prioritize analyzing fault information. Furthermore, if the customer is relaxed, the analysis unit can prioritize analyzing service usage information. Furthermore, if the customer is in a hurry, the analysis unit can prioritize analyzing key points. This allows the analysis unit to adjust the order of analysis based on the customer's emotions, thereby enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI adjust the order of analysis.

[0085] The analysis unit can determine the analysis priority based on the time when the information was submitted during analysis. For example, the analysis unit gives the highest priority to analyzing the most recent fault information. The analysis unit can also determine the analysis priority by referring to past inquiry records. Furthermore, the analysis unit can perform a concise analysis for information that was submitted a long time ago. This allows the analysis unit to perform efficient analysis by determining the analysis priority based on the time when the information was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information related to fault information. The analysis unit can also prioritize analysis of information related to service usage information. Furthermore, the analysis unit can also prioritize analysis of information related to past inquiry records. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0087] The analysis unit can adjust the use of technical terminology during analysis according to the customer's level of expertise. For example, if the customer has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the customer's level of expertise. This allows the analysis unit to provide analysis results that are easier to understand by adjusting the use of technical terminology according to the customer's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0088] The answering unit can estimate the customer's emotions and adjust the way the answer is expressed based on the estimated customer's emotions. For example, if the customer is feeling anxious, the answering unit can respond in a reassuring way. If the customer is relaxed, the answering unit can also respond in a friendly way. Furthermore, if the customer is in a hurry, the answering unit can also respond in a concise and quick way. This allows the answering unit to provide a more appropriate answer by adjusting the way the answer is expressed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the answering unit can be performed using AI, for example, or without AI. For example, the answering unit can input customer emotion data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0089] The answering unit can adjust the level of detail of the answer based on the importance of the information when answering. For example, the answering unit provides a detailed answer for important fault information. The answering unit can also provide a concise answer for used service information. Furthermore, the answering unit can provide an answer that focuses on the main points for past inquiry records. In this way, the answering unit can provide an efficient answer by adjusting the level of detail of the answer based on the importance of the information. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input information importance data to the generating AI and have the generating AI adjust the level of detail of the answer.

[0090] The answering unit can apply different answering algorithms depending on the category of information when answering. For example, the answering unit applies a fault answering algorithm to fault information. The answering unit can also apply a service answering algorithm to service usage information. Furthermore, the answering unit can apply a history answering algorithm to past inquiry records. In this way, the answering unit can provide highly accurate answers by applying different answering algorithms depending on the category of information. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input information category data to the generation AI and cause the generation AI to apply different answering algorithms.

[0091] The answering unit can improve the accuracy of the answer by referring to the customer's past answer results when answering. The answering unit, for example, provides a current answer based on the customer's past answer results. The answering unit can also extract relevant information from the past answer results and reflect it in the answer. Furthermore, the answering unit can adjust the answering algorithm by referring to the customer's past answer results. In this way, the answering unit can improve the accuracy of the answer by referring to the customer's past answer results. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the customer's past answer result data into the generation AI and have the generation AI improve the accuracy of the answer.

[0092] The answering unit can estimate the customer's emotions and adjust the length of the answer based on the estimated customer emotions. For example, if the customer is feeling anxious, the answering unit can provide a detailed answer. If the customer is relaxed, the answering unit can also provide a concise answer. If the customer is in a hurry, the answering unit can also provide a short answer that focuses on the main points. This allows the answering unit to adjust the length of the answer based on the customer's emotions and provide a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the answering unit can be performed using AI, or without AI. For example, the answering unit can input customer emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0093] The answering unit can determine the priority of answers based on the time when the information was submitted when making an answer. For example, the answering unit gives the most recent fault information the highest priority. The answering unit can also determine the priority of answers by referring to past inquiry records. Furthermore, the answering unit can provide a concise answer for information that was submitted a long time ago. This allows the answering unit to provide an efficient answer by determining the priority of answers based on the time when the information was submitted. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input information submission time data into the generation AI and have the generation AI determine the priority of answers.

[0094] The answering unit can adjust the order of answers based on the relevance of information when answering. For example, the answering unit prioritizes information related to fault information. The answering unit can also prioritize information related to service usage information. Furthermore, the answering unit can also prioritize information related to past inquiry records. This allows the answering unit to adjust the order of answers based on the relevance of information, thereby providing an efficient answer. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input information relevance data to the generating AI and have the generating AI adjust the order of answers.

[0095] The answering unit can adjust the use of technical terminology in the answer depending on the customer's level of expertise when answering. For example, if the customer has technical expertise, the answering unit can use a lot of technical terminology in the answer. Alternatively, if the customer does not have technical expertise, the answering unit can use simple language in the answer. Furthermore, the answering unit can adjust the level of detail in the answer depending on the customer's level of expertise. This allows the answering unit to provide an answer that is easier to understand by adjusting the use of technical terminology in the answer depending on the customer's level of expertise. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input customer's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0096] The tone analysis unit can estimate the customer's emotions and adjust the tone analysis criteria based on the estimated customer's emotions. For example, if the customer is angry, the tone analysis unit can set a criterion for analyzing a strong tone. Furthermore, if the customer is sad, the tone analysis unit can set a criterion for analyzing a sad tone. Furthermore, if the customer is happy, the tone analysis unit can set a criterion for analyzing a happy tone. This allows the tone analysis unit to adjust the tone analysis criteria based on the customer's emotions, thereby enabling more appropriate tone analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the tone analysis unit can be performed using, for example, AI, or without AI. For example, the tone analysis unit can input customer emotion data into the generation AI and have the generation AI adjust the tone analysis criteria.

[0097] The tone analysis unit can improve the accuracy of the analysis by referring to the customer's past tone data during tone analysis. The tone analysis unit, for example, analyzes the current tone based on the customer's past tone data. The tone analysis unit can also extract relevant information from the past tone data and reflect it in the analysis. Furthermore, the tone analysis unit can adjust the analysis algorithm by referring to the customer's past tone data. In this way, the tone analysis unit can improve the accuracy of the tone analysis by referring to the customer's past tone data. Some or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input the customer's past tone data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0098] The tone analysis unit can customize the means of analysis based on the customer's current situation during tone analysis. For example, if the customer is nervous, the tone analysis unit uses a tone analysis means that reduces tension. Furthermore, if the customer is relaxed, the tone analysis unit can also use a relaxed tone analysis means. Furthermore, if the customer is in a hurry, the tone analysis unit can also use a quick tone analysis means. In this way, the tone analysis unit can perform more appropriate tone analysis by customizing the means of analysis based on the customer's current situation. Some or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input data on the customer's current situation into the generation AI and have the generation AI customize the analysis means.

[0099] The tone analysis unit can estimate the customer's emotions and determine the priority of tone analysis based on the estimated customer's emotions. For example, if the customer is angry, the tone analysis unit can prioritize analyzing a strong tone. Furthermore, if the customer is sad, the tone analysis unit can prioritize analyzing a sad tone. Furthermore, if the customer is happy, the tone analysis unit can prioritize analyzing a happy tone. This allows the tone analysis unit to determine the priority of tone analysis based on the customer's emotions, thereby enabling more appropriate tone analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the tone analysis unit can be performed using, for example, AI, or without AI. For example, the tone analysis unit can input customer emotion data into the generation AI and have the generation AI determine the priority of tone analysis.

[0100] The inflection analysis unit can perform inflection analysis taking into account the geographical location information of the customer. For example, if the customer is in a specific region, the inflection analysis unit can perform inflection analysis taking into account the dialect and accent of that region. Furthermore, if the customer is traveling, the inflection analysis unit can perform inflection analysis taking into account the language and culture of the destination. Furthermore, if the customer is at home, the inflection analysis unit can perform inflection analysis taking into account the language and culture of the area around the customer's home. This allows the inflection analysis unit to perform more appropriate inflection analysis by taking into account the geographical location information of the customer. Some or all of the above-mentioned processing in the inflection analysis unit may be performed using, or without, AI, for example. For example, the inflection analysis unit can input the customer's geographical location information data into the generation AI and have the generation AI perform the analysis.

[0101] The tone analysis unit can analyze the customer's social media activity during tone analysis to improve the accuracy of the analysis. The tone analysis unit performs analysis, for example, by referring to the tone used by the customer on social media. The tone analysis unit can also analyze the content of the customer's social media posts to analyze the related tone. Furthermore, the tone analysis unit can analyze the related tone by referring to the activity of the customer's friends on social media. In this way, the tone analysis unit can improve the accuracy of the tone analysis by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input the customer's social media data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0102] The profile analysis unit can estimate the customer's emotions and adjust the profile analysis criteria based on the estimated customer's emotions. For example, if the customer is angry, the profile analysis unit can set profile analysis criteria that reflect the customer's angry emotion. Furthermore, if the customer is sad, the profile analysis unit can set profile analysis criteria that reflect the customer's sad emotion. Furthermore, if the customer is happy, the profile analysis unit can set profile analysis criteria that reflect the customer's happy emotion. This allows the profile analysis unit to adjust the profile analysis criteria based on the customer's emotions, thereby enabling more appropriate profile analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the profile analysis unit can be performed using, for example, AI, or without AI. For example, the profile analysis unit can input the customer's emotion data into the generation AI and have the generation AI adjust the profile analysis criteria.

[0103] The profile analysis unit can improve the accuracy of the analysis by referring to the customer's past profile data during profile analysis. For example, the profile analysis unit analyzes the current profile based on the customer's past profile data. The profile analysis unit can also extract relevant information from the past profile data and reflect it in the analysis. Furthermore, the profile analysis unit can adjust the analysis algorithm by referring to the customer's past profile data. In this way, the profile analysis unit can improve the accuracy of the profile analysis by referring to the customer's past profile data. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's past profile data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0104] The profile analysis unit can customize the analysis means based on the customer's current situation during profile analysis. For example, if the customer is nervous, the profile analysis unit can use a profile analysis means that reduces tension. Also, if the customer is relaxed, the profile analysis unit can use a relaxed profile analysis means. Furthermore, if the customer is in a hurry, the profile analysis unit can use a quick profile analysis means. This allows the profile analysis unit to customize the analysis means based on the customer's current situation, thereby enabling more appropriate profile analysis. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's current situation data into the generation AI and have the generation AI customize the analysis means.

[0105] The profile analysis unit can estimate the customer's emotions and determine the priority of profile analysis based on the estimated customer's emotions. For example, if the customer is angry, the profile analysis unit can prioritize profile analysis that reflects the emotion of anger. Furthermore, if the customer is sad, the profile analysis unit can prioritize profile analysis that reflects the emotion of sadness. Furthermore, if the customer is happy, the profile analysis unit can prioritize profile analysis that reflects the emotion of joy. This allows the profile analysis unit to prioritize profile analysis based on the customer's emotions, thereby enabling more appropriate profile analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the profile analysis unit can be performed using, for example, AI, or without AI. For example, the profile analysis unit can input customer emotion data into the generation AI and have the generation AI determine the priority of profile analysis.

[0106] The profile analysis unit can take into account the customer's geographical location information when analyzing the profile. For example, if the customer is in a specific region, the profile analysis unit can take into account the culture and customs of that region when analyzing the profile. Furthermore, if the customer is traveling, the profile analysis unit can also take into account the culture and customs of the destination when analyzing the profile. Furthermore, if the customer is at home, the profile analysis unit can take into account the culture and customs of the area around the customer's home when analyzing the profile. This allows the profile analysis unit to perform more appropriate profile analysis by taking into account the customer's geographical location information. Some or all of the above-described processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's geographical location information data into the generation AI and have the generation AI perform the analysis.

[0107] The profile analysis unit can analyze a customer's social media activity during profile analysis to improve the accuracy of the analysis. For example, the profile analysis unit performs analysis by referring to the profile information used by the customer on social media. The profile analysis unit can also analyze the content of a customer's social media posts and analyze related profile information. Furthermore, the profile analysis unit can analyze related profile information by referring to the activities of the customer's friends on social media. In this way, the profile analysis unit can improve the accuracy of the profile analysis by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's social media data into a generation AI and have the generation AI improve the accuracy of the analysis.

[0108] The profile analysis unit can customize the analysis method by reflecting the customer's past feedback when analyzing the profile. The profile analysis unit can adjust the analysis method based on, for example, feedback provided by the customer in the past. The profile analysis unit can also prioritize analysis of necessary information from the customer's past feedback. Furthermore, the profile analysis unit can also optimize the analysis means by reflecting the customer's feedback. In this way, the profile analysis unit can optimize the analysis method by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the profile analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the profile analysis unit can input the customer's past feedback data into the generation AI and have the generation AI customize the analysis method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, response unit, tone analysis unit, and profile analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect customer service usage information and fault information using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand the customer's situation. The response unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response based on the analysis results. The tone analysis unit analyzes the customer's tone using the microphone 38B of the smart device 14 and provides feedback to the response unit. The profile analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the customer's profile information and provides feedback to the response unit. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, response unit, tone analysis unit, and profile analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect customer service usage information and fault information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand the customer's situation. The response unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response based on the analysis results. The tone analysis unit analyzes the customer's tone using the microphone 238 of the smart glasses 214 and provides feedback to the response unit. The profile analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the customer's profile information and provides feedback to the response unit. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, response unit, tone analysis unit, and profile analysis unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect customer service usage information and fault information using the camera 42 or microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand the customer's situation. The response unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response based on the analysis results. The tone analysis unit analyzes the customer's tone using the microphone 238 of the headset terminal 314 and provides feedback to the response unit. The profile analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the customer's profile information and provides feedback to the response unit. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, response unit, tone analysis unit, and profile analysis unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect customer service usage information and fault information using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand the customer's situation. The response unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides an appropriate response based on the analysis results. The tone analysis unit analyzes the customer's tone using the microphone 238 of the robot 414 and provides feedback to the response unit. The profile analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the customer's profile information and provides feedback to the response unit.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The user support system may further include a purchase history analysis unit that analyzes a customer's past purchase history and provides feedback to the response unit. The purchase history analysis unit analyzes information about products and services purchased in the past by the customer and provides information related to the current inquiry. For example, if a customer makes an inquiry about a product purchased in the past, detailed information about that product and related support information can be collected preferentially and provided to the response unit. The purchase history analysis unit may also analyze feedback and ratings on products purchased in the past by the customer and provide these to the response unit, thereby enabling the system to provide a more appropriate response. Furthermore, the purchase history analysis unit may analyze a customer's purchasing patterns and provide information to prepare for future inquiries.

[0111] The user support system may further include a social media analysis unit that analyzes customers' social media activities and provides feedback to the response unit. The social media analysis unit analyzes information and comments posted by customers on social media and provides information related to the current inquiry. For example, if a customer expresses dissatisfaction with a particular service on social media, collecting that information and providing it to the response unit will enable a faster and more appropriate response. The social media analysis unit may also analyze customers' social media activity patterns and provide information to prepare for future inquiries. Furthermore, the social media analysis unit may analyze the activities of customers' friends and followers and provide related information.

[0112] The user support system may further include a geographic information analysis unit that analyzes the customer's geographic location information and provides feedback to the answering unit. The geographic information analysis unit analyzes information about the customer's current location and places the customer has visited in the past, and provides information related to the current inquiry. For example, if the customer is using a service in a specific area, it can prioritize collecting fault information and service information related to that area and provide it to the answering unit. Furthermore, if the customer is traveling, the geographic information analysis unit can provide a more appropriate answer by providing information related to the customer's travel destination. Furthermore, the geographic information analysis unit can analyze the customer's geographic movement patterns and provide information to prepare for future inquiries.

[0113] The user support system can further include a tone adjustment unit that estimates the customer's emotions and adjusts the tone of a response based on the estimated customer emotions. The tone adjustment unit analyzes the customer's emotions and provides the response to the response unit, thereby enabling the response to be provided in a more appropriate tone. For example, if the customer is angry, the response can be provided in a calm and collected tone. If the customer is sad, the response can be provided in a comforting tone. Furthermore, if the customer is happy, the response can be provided in a bright and friendly tone. In this way, the tone adjustment unit can adjust the tone of the response based on the customer's emotions, thereby achieving a more appropriate response.

[0114] The user support system may further include a feedback analysis unit that analyzes past customer feedback and provides feedback to the response unit. The feedback analysis unit analyzes feedback provided by customers in the past and provides information related to the current inquiry. For example, if a customer has previously expressed dissatisfaction with a particular service, collecting that information and providing it to the response unit will enable a faster and more appropriate response. The feedback analysis unit may also provide information to prepare for future inquiries based on past customer feedback. Furthermore, the feedback analysis unit may reflect customer feedback to optimize the content and method of responses.

[0115] The user support system can further include a detail adjustment unit that estimates the customer's emotions and adjusts the level of detail of the response based on the estimated customer emotions. The detail adjustment unit analyzes the customer's emotions and provides the response unit with the level of detail, thereby providing a response with a more appropriate level of detail. For example, if the customer is feeling anxious, detailed information can be provided to give the customer a sense of security. If the customer is relaxed, concise information can be provided. Furthermore, if the customer is in a hurry, a short response that hits the main points can be provided. In this way, the detail adjustment unit can adjust the level of detail of the response based on the customer's emotions, thereby achieving a more appropriate response.

[0116] The user support system can further include an inquiry history analysis unit that analyzes the customer's past inquiry history and provides feedback to the response unit. The inquiry history analysis unit analyzes the content and results of inquiries made by the customer in the past and provides information related to the current inquiry. For example, if a customer has previously inquired about the same problem, the system can refer to the response method and results at that time to provide a faster and more appropriate response. The inquiry history analysis unit can also provide information to prepare for future inquiries from the customer's past inquiry history. Furthermore, the inquiry history analysis unit can analyze customer inquiry patterns and optimize the content and method of responses.

[0117] The user support system may further include a priority determination unit that estimates the customer's emotions and determines the priority of responses based on the estimated customer emotions. The priority determination unit analyzes the customer's emotions and provides the response unit with information to provide responses with a more appropriate priority. For example, if a customer is angry, the inquiry can be handled with the highest priority. Also, if a customer is confused, the inquiry can be handled with the highest priority. Furthermore, if the customer is calm, the inquiry can be handled with normal priority. In this way, the priority determination unit can achieve more appropriate responses by determining the priority of responses based on the customer's emotions.

[0118] The user support system can further include an expression method adjustment unit that estimates the customer's emotions and adjusts the way in which a response is expressed based on the estimated customer emotions. The expression method adjustment unit analyzes the customer's emotions and provides the response to the response unit, thereby enabling the response to be provided in a more appropriate expression method. For example, if the customer is feeling anxious, the response can be provided in an expression that gives a sense of security. If the customer is relaxed, the response can be provided in a friendly expression method. Furthermore, if the customer is in a hurry, the response can be provided in a concise and quick expression method. In this way, the expression method adjustment unit can achieve a more appropriate response by adjusting the way in which a response is expressed based on the customer's emotions.

[0119] The user support system may further include a purchase history analysis unit that analyzes a customer's past purchase history and provides feedback to the response unit. The purchase history analysis unit analyzes information about products and services purchased in the past by the customer and provides information related to the current inquiry. For example, if a customer makes an inquiry about a product purchased in the past, detailed information about that product and related support information can be collected preferentially and provided to the response unit. The purchase history analysis unit may also analyze feedback and ratings on products purchased in the past by the customer and provide these to the response unit, thereby enabling the system to provide a more appropriate response. Furthermore, the purchase history analysis unit may analyze a customer's purchasing patterns and provide information to prepare for future inquiries.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects information about the services used by customers and information about outages. For example, the collection unit collects information about the operation status of the service and past outage history. The collection unit can also use AI to automatically collect information about the services used by customers and outage information. Step 2: The analysis unit analyzes the information collected by the collection unit to understand the customer's situation. For example, the analysis unit analyzes the customer's tone of voice and past inquiry records to understand the current situation. The analysis unit can also analyze the collected information using AI. Step 3: The answering unit provides an appropriate answer based on the analysis results obtained by the analyzing unit. For example, the answering unit may provide a specific answer such as, "Currently, the service is experiencing an outage and recovery work is underway. The estimated time of recovery is ____." The answering unit can also automatically generate an appropriate answer based on the analysis results using AI.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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 AI 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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 AI 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 collection department that collects customer service information and fault information; an analysis unit that analyzes the information collected by the collection unit and grasps the customer's situation; a response unit that provides a specific response based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. Equipped with a tone analyzer that analyzes the tone of the customer's speech and provides feedback to the answerer 2. The system of claim 1.

3. A profile analysis section is provided that analyzes customer profile information and provides feedback to the response section.

2. The system of claim 1.

4. The collecting unit Estimate customer sentiment and prioritize the information to be collected based on the estimated sentiment 2. The system of claim 1.

5. The collecting unit Analyze your past usage history and select specific information collection methods 2. The system of claim 1.

6. The collecting unit Filtering based on your current circumstances and interests at the time of collection 2. The system of claim 1.

7. The collecting unit Select specific collection methods according to your input method at the time of collection 2. The system of claim 1.

8. The collecting unit Infer customer sentiment and adjust the level of detail of information collected based on the estimated sentiment 2. The system of claim 1.

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