system

Abstract

The system according to the embodiment aims to improve the efficiency of customer support and reduce costs. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's question. The analysis unit analyzes the question received by the reception unit. The generation unit generates an appropriate answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not sufficiently streamline customer support, leaving room for cost reduction.

[0005] The system according to the embodiment aims to improve the efficiency of customer support and reduce costs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's question. The analysis unit analyzes the question received by the reception unit. The generation unit generates an appropriate answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of customer support and reduce costs. [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 customer support system according to an embodiment of the present invention provides customer support to end users using a generation AI and a messaging app. In this customer support system, a company loads its past customer support history into the generation AI and uses a messaging app as an interface with the user. Users can receive support using a familiar messaging app. The generation AI analyzes the past history and generates appropriate answers to user questions. This system allows companies to respond with minimal support staff, thereby reducing costs. For example, a company loads its past customer support history into the generation AI. The generation AI then analyzes the past support history to obtain data for generating appropriate answers to user questions. For example, the generation AI extracts frequently asked questions and their answers from the past support history and learns from them. Next, users can receive support using a familiar messaging app. The user enters a question through the messaging app, which is then sent to the generation AI. The generation AI analyzes the user's question and generates an appropriate answer. For example, if a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on the past support history and provides it to the user. This system allows companies to respond with a minimum number of support staff, reducing costs. Generative AI analyzes past support history and generates appropriate answers, reducing the burden on support staff and realizing efficient customer support. This allows the customer support system to provide efficient customer support to end users.

[0029] A customer support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user questions. User questions include, but are not limited to, how to return a product, checking the status of an order, and technical support. The reception unit can receive user questions, for example, through a messaging app. The analysis unit analyzes the questions received by the reception unit. The analysis unit, for example, uses a generation AI to analyze past customer support history and acquire data for generating appropriate answers to the user questions. The generation AI, for example, extracts frequently asked questions and their answers from the past support history and learns from them. The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The generation unit, for example, uses the generation AI to generate appropriate answers to the user questions. For example, when a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on the past support history. The provision unit provides the answer generated by the generation unit. The provision unit can provide the answer to the user, for example, through a messaging app. This allows the customer support system according to the embodiment to efficiently accept and analyze user questions, and generate and provide answers.

[0030] The reception unit can receive a user's question through a messaging app. Examples of messaging apps include, but are not limited to, LINE (registered trademark), WhatsApp (registered trademark), and Facebook (registered trademark) Messenger. The reception unit can receive a question through, for example, a messaging app that the user normally uses. This allows the user to ask a question using a messaging app that the user is familiar with. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's question received through the messaging app to a generation AI and have the generation AI analyze the question.

[0031] The analysis unit can analyze past customer support history and acquire data for generating appropriate answers to user questions. Past customer support history includes, but is not limited to, support tickets, chat logs, and email histories. The analysis unit, for example, uses a generation AI to analyze the past customer support history and acquire data for generating appropriate answers to user questions. The generation AI, for example, extracts frequently asked questions and their answers from the past support history and learns from them. This allows the analysis unit to analyze the past customer support history and acquire data for generating appropriate answers. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the past customer support history into the generation AI and cause the generation AI to acquire data for generating appropriate answers.

[0032] The generation unit can generate an appropriate answer based on the user's question. The generation unit generates an appropriate answer to the user's question, for example, using a generation AI. For example, when a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on past support history. This allows the generation unit to generate an appropriate answer based on the user's question. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's question to the generation AI and cause the generation AI to generate an appropriate answer.

[0033] The providing unit can provide the generated answer to the user through a messaging app. Examples of messaging apps include, but are not limited to, LINE, WhatsApp, and Facebook Messenger. For example, the providing unit can provide the generated answer to the user through the messaging app. This allows the providing unit to provide the generated answer to the user through the messaging app. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing the generated answer to the user through the messaging app, the providing unit can optimize the method of providing the answer by using AI.

[0034] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question methods (text, voice, etc.) that the user has used in the past. The reception unit can also predict and suggest a question method to be used in a specific time period based on the user's past question history. In this way, the reception unit can select the optimal reception method by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history into a generation AI and have the generation AI select the optimal reception method.

[0035] When receiving a question, the reception unit can filter the questions based on the user's current situation and areas of interest. For example, the reception unit preferentially receives questions related to topics in which the user is currently interested. The reception unit can also filter questions based on the user's current situation (e.g., immediately after a purchase, during a return period, etc.). The reception unit can also preferentially receive highly relevant questions based on the user's past behavioral history. In this way, the reception unit can preferentially receive highly relevant questions by filtering questions based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and cause the generation AI to filter the questions.

[0036] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the user's home. In this way, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. The geographical location information is obtained using, for example, GPS data, an IP address, Wi-Fi location information, etc. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to determine the priority of questions.

[0037] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can prioritize questions related to topics mentioned by the user on social media. The reception unit can also prioritize questions related to brands or products the user follows on social media. The reception unit can also prioritize questions that are highly relevant based on the user's social media activity history. In this way, the reception unit can prioritize questions that are highly relevant by analyzing the user's social media activity. Social media activity is evaluated by analyzing, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive questions.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a concise analysis on a question with a low level of importance. The analysis unit can also determine the priority of the analysis based on the importance of the question. As a result, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the question. The importance of a question is evaluated based on, for example, the urgency, the scope of impact, the user's priority, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the question category. For example, the analysis unit applies a specialized analysis algorithm to technical questions. The analysis unit can also apply a simple analysis algorithm to general questions. The analysis unit can also select the optimal analysis algorithm depending on the question category. This allows the analysis unit to provide more appropriate analysis results by applying the optimal analysis algorithm depending on the question category. Question categories are classified into, for example, technical questions, business questions, general questions, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input question category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time of question submission. The analysis unit can determine the analysis priority based on, for example, the time zone in which the question was submitted. The analysis unit can also determine the analysis priority based on the date the question was submitted. The analysis unit can also determine the analysis priority based on the timing of the question submission. In this way, the analysis unit can perform analysis in a more appropriate order by determining the analysis priority based on the time of question submission. The time of question submission is evaluated based on, for example, a timestamp, the submission date and time, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input question submission time data into the generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if the relevance of a question is high, the analysis unit can prioritize analysis. Furthermore, if the relevance of a question is low, the analysis unit can postpone analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the questions. In this way, the analysis unit can perform analysis in a more appropriate order by adjusting the order of analysis based on the relevance of the questions. The relevance of questions is evaluated based on, for example, similarity of content, related topics, etc. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0042] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also determine the priority of the answer based on the importance of the question. As a result, the generation unit can provide a more appropriate answer by adjusting the level of detail of the answer based on the importance of the question. The importance of a question is evaluated based on, for example, the urgency, the scope of impact, the user's priority, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0043] When generating an answer, the generation unit can apply different generation algorithms depending on the question category. For example, the generation unit applies a specialized generation algorithm to technical questions. The generation unit can also apply a simple generation algorithm to general questions. The generation unit can also select the optimal generation algorithm depending on the question category. This allows the generation unit to provide a more appropriate answer by applying the optimal generation algorithm depending on the question category. Question categories are classified into, for example, technical questions, business questions, general questions, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0044] When generating answers, the generation unit can determine the priority of answers based on the time of question submission. The generation unit can determine the priority of answers based on, for example, the time period when the question was submitted. The generation unit can also determine the priority of answers based on the date when the question was submitted. The generation unit can also determine the priority of answers based on the timing when the question was submitted. In this way, the generation unit can provide answers in a more appropriate order by determining the priority of answers based on the time when the question was submitted. The time when the question was submitted is evaluated based on, for example, a timestamp, the submission date and time, etc. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0045] When generating answers, the generation unit can adjust the order of answers based on the relevance of the question. For example, if the relevance of the question is high, the generation unit can generate answers preferentially. Furthermore, if the relevance of the question is low, the generation unit can also postpone generating answers. Furthermore, the generation unit can adjust the order of answers based on the relevance of the question. In this way, the generation unit can provide answers in a more appropriate order by adjusting the order of answers based on the relevance of the question. The relevance of a question is evaluated based on, for example, similarity of content, related topics, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0046] When providing an answer, the providing unit can select the optimal delivery method by referring to the user's past question history. For example, the providing unit preferentially suggests delivery methods (text, voice, etc.) that the user has frequently used in the past. The providing unit can also predict and suggest a delivery method to be used during a specific time period based on the user's past question history. The providing unit can also select the optimal delivery method based on the user's past question history. In this way, the providing unit can select the optimal delivery method by referring to the user's past question history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past question history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0047] When providing an answer, the providing unit can customize the means of providing the answer based on the user's current situation. For example, if the user is on the move, the providing unit can prioritize providing the answer via voice. Furthermore, if the user is using a desktop, the providing unit can prioritize providing detailed text. Furthermore, if the user is using a smartphone, the providing unit can prioritize providing a combination of concise text and images. This allows the providing unit to provide an answer in a more appropriate manner by customizing the means of providing the answer based on the user's current situation. The current situation is evaluated based on, for example, the time of day, location, device usage, etc. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current situation data into the generation AI and cause the generation AI to customize the means of providing the answer.

[0048] When providing an answer, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to the user's travel destination. Furthermore, if the user is at home, the providing unit can prioritize providing information related to the user's home. This allows the providing unit to select the optimal providing method by taking into account the user's geographical location information. The geographical location information is obtained using, for example, GPS data, an IP address, Wi-Fi location information, etc. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0049] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means of providing the answer. For example, the providing unit can prioritize providing information related to topics mentioned by the user on social media. The providing unit can also prioritize providing information related to brands or products the user follows on social media. The providing unit can also prioritize providing highly relevant information based on the user's social media activity history. In this way, the providing unit can prioritize providing highly relevant information by analyzing the user's social media activity. Social media activity is evaluated by analyzing, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of providing the answer.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When accepting a user's question, the accepting unit can refer to the user's past purchase history and prioritize accepting related questions. For example, the accepting unit can prioritize accepting questions about products the user has purchased in the past. The accepting unit can also check whether the product the user has purchased in the past is still within the warranty period and prioritize accepting questions about products that are still within the warranty period. Furthermore, the accepting unit can analyze the usage status of products the user has purchased in the past and prioritize accepting questions about products that are used frequently. In this way, the accepting unit can prioritize accepting highly relevant questions by referring to the user's past purchase history.

[0052] When generating an answer based on a user's question, the generator can refer to the user's past feedback and improve the quality of the answer based on the feedback. For example, if the user is not satisfied with a previously provided answer, the generator can take that feedback into consideration and generate a more appropriate answer. Also, if the user has given a high rating to a previously provided answer, the generator can refer to that answer to generate an answer to a similar question. Furthermore, the generator can analyze the user's feedback, identify common problems, and improve the quality of the answer based on that. In this way, the generator can provide higher quality answers by referring to the user's past feedback.

[0053] When providing the generated answer to the user, the providing unit can select the optimal providing method by taking into account the user's current device status. For example, if the user is using a smartphone, an answer that combines concise text and images can be provided. If the user is using a desktop, an answer that includes detailed text and links can be provided. Furthermore, if the user is using a tablet, an answer that includes interactive elements can be provided. This allows the providing unit to provide the answer in the optimal manner by taking into account the user's device status.

[0054] When analyzing a user's question, the analysis unit can improve the accuracy of the analysis by taking into account the user's current geographical location information. For example, if the user is in a specific area, information related to that area can be analyzed preferentially. Also, if the user is traveling, information related to the travel destination can be analyzed preferentially. Furthermore, if the user is at home, information related to the home can be analyzed preferentially. In this way, the analysis unit can provide more appropriate analysis results by taking into account the user's geographical location information.

[0055] When providing the generated answer to the user, the providing unit can refer to the user's past behavioral history and select the optimal timing for providing the answer. For example, if the user previously asked a question during a specific time period, the answer can be provided during that time period. Also, if the user previously asked a question on a specific day of the week, the answer can be provided on that day. Furthermore, the providing unit can analyze the user's past behavioral history and predict the optimal timing for providing the answer. This allows the providing unit to provide the answer at the optimal timing by referring to the user's past behavioral history.

[0056] When analyzing a user's question, the analysis unit can analyze the user's social media activity and prioritize the analysis of related information. For example, it can prioritize the analysis of questions related to topics mentioned by the user on social media. It can also prioritize the analysis of questions related to brands or products that the user follows on social media. It can also prioritize the analysis of highly relevant questions based on the user's social media activity history. This allows the analysis unit to prioritize the analysis of highly relevant information by analyzing the user's social media activity.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives a user's question. The user's question may include, but is not limited to, how to return a product, checking the status of an order, or technical support. The reception unit may receive the user's question via, for example, a messaging app. Step 2: The analysis unit analyzes the question received by the reception unit. For example, the analysis unit uses a generation AI to analyze past customer support history and obtain data for generating appropriate answers to the user's questions. For example, the generation AI extracts frequently asked questions and their answers from past support history and learns from them. Step 3: The generation unit generates an appropriate answer based on the question analyzed by the analysis unit. The generation unit generates an appropriate answer to the user's question, for example, using a generation AI. For example, if a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on past support history. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit can provide the answer to the user through, for example, a messaging app.

[0059] (Example 2) A customer support system according to an embodiment of the present invention provides customer support to end users using a generation AI and a messaging app. In this customer support system, a company loads its past customer support history into the generation AI and uses a messaging app as an interface with the user. Users can receive support using a familiar messaging app. The generation AI analyzes the past history and generates appropriate answers to user questions. This system allows companies to respond with minimal support staff, thereby reducing costs. For example, a company loads its past customer support history into the generation AI. The generation AI then analyzes the past support history to obtain data for generating appropriate answers to user questions. For example, the generation AI extracts frequently asked questions and their answers from the past support history and learns from them. Next, users can receive support using a familiar messaging app. The user enters a question through the messaging app, which is then sent to the generation AI. The generation AI analyzes the user's question and generates an appropriate answer. For example, if a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on the past support history and provides it to the user. This system allows companies to respond with a minimum number of support staff, reducing costs. Generative AI analyzes past support history and generates appropriate answers, reducing the burden on support staff and realizing efficient customer support. This allows the customer support system to provide efficient customer support to end users.

[0060] A customer support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user questions. User questions include, but are not limited to, how to return a product, checking the status of an order, and technical support. The reception unit can receive user questions, for example, through a messaging app. The analysis unit analyzes the questions received by the reception unit. The analysis unit, for example, uses a generation AI to analyze past customer support history and acquire data for generating appropriate answers to the user questions. The generation AI, for example, extracts frequently asked questions and their answers from the past support history and learns from them. The generation unit generates appropriate answers based on the questions analyzed by the analysis unit. The generation unit, for example, uses the generation AI to generate appropriate answers to the user questions. For example, when a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on the past support history. The provision unit provides the answer generated by the generation unit. The provision unit can provide the answer to the user, for example, through a messaging app. This allows the customer support system according to the embodiment to efficiently accept and analyze user questions, and generate and provide answers.

[0061] The reception unit can receive a user's question through a messaging app. Examples of messaging apps include, but are not limited to, LINE, WhatsApp, and Facebook Messenger. The reception unit can receive a question through, for example, a messaging app that the user normally uses. This allows the user to ask a question using a messaging app that the user is familiar with. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's question received through the messaging app to a generation AI and have the generation AI analyze the question.

[0062] The analysis unit can analyze past customer support history and acquire data for generating appropriate answers to user questions. Past customer support history includes, but is not limited to, support tickets, chat logs, and email histories. The analysis unit, for example, uses a generation AI to analyze the past customer support history and acquire data for generating appropriate answers to user questions. The generation AI, for example, extracts frequently asked questions and their answers from the past support history and learns from them. This allows the analysis unit to analyze the past customer support history and acquire data for generating appropriate answers. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the past customer support history into the generation AI and cause the generation AI to acquire data for generating appropriate answers.

[0063] The generation unit can generate an appropriate answer based on the user's question. The generation unit generates an appropriate answer to the user's question, for example, using a generation AI. For example, when a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on past support history. This allows the generation unit to generate an appropriate answer based on the user's question. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's question to the generation AI and cause the generation AI to generate an appropriate answer.

[0064] The providing unit can provide the generated answer to the user through a messaging app. Examples of messaging apps include, but are not limited to, LINE, WhatsApp, and Facebook Messenger. For example, the providing unit can provide the generated answer to the user through the messaging app. This allows the providing unit to provide the generated answer to the user through the messaging app. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing the generated answer to the user through the messaging app, the providing unit can optimize the method of providing the answer by using AI.

[0065] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can immediately accept the question, minimizing waiting time. Furthermore, if the user is relaxed, the reception unit can determine that a short wait is acceptable and process other high-priority questions first. Furthermore, if the user is in a hurry, the reception unit can prioritize and respond to questions quickly. This allows the reception unit to adjust the timing of question reception according to the user's emotions, thereby receiving questions at a more appropriate time. 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 reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of question reception.

[0066] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question methods (text, voice, etc.) that the user has used in the past. The reception unit can also predict and suggest a question method to be used in a specific time period based on the user's past question history. In this way, the reception unit can select the optimal reception method by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history into a generation AI and have the generation AI select the optimal reception method.

[0067] When receiving a question, the reception unit can filter the questions based on the user's current situation and areas of interest. For example, the reception unit preferentially receives questions related to topics in which the user is currently interested. The reception unit can also filter questions based on the user's current situation (e.g., immediately after a purchase, during a return period, etc.). The reception unit can also preferentially receive highly relevant questions based on the user's past behavioral history. In this way, the reception unit can preferentially receive highly relevant questions by filtering questions based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and cause the generation AI to filter the questions.

[0068] The reception unit can estimate the user's emotions and determine the priority of received questions based on the estimated user emotions. For example, if the user is dissatisfied, the reception unit can process that question as the highest priority. Furthermore, if the user is satisfied, the reception unit can also prioritize other questions with a higher urgency. Furthermore, if the user is confused, the reception unit can quickly process that question and resolve the issue. Thus, the reception unit can process questions in a more appropriate order by determining the priority of questions according to the user's emotions. The emotion estimation is realized 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 reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0069] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the user's home. In this way, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. The geographical location information is obtained using, for example, GPS data, an IP address, Wi-Fi location information, etc. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to determine the priority of questions.

[0070] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can prioritize questions related to topics mentioned by the user on social media. The reception unit can also prioritize questions related to brands or products the user follows on social media. The reception unit can also prioritize questions that are highly relevant based on the user's social media activity history. In this way, the reception unit can prioritize questions that are highly relevant by analyzing the user's social media activity. Social media activity is evaluated by analyzing, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive questions.

[0071] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit uses an expression that conveys a sense of security. Furthermore, if the user is excited, the analysis unit can use a calm and collected expression. Furthermore, if the user is relaxed, the analysis unit can use an expression that includes detailed information. By adjusting the way the analysis is presented based on the user's emotions, the analysis unit can provide analysis results in a more appropriate way. The emotion estimation is achieved using an emotion estimation function, for example, 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, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a concise analysis on a question with a low level of importance. The analysis unit can also determine the priority of the analysis based on the importance of the question. As a result, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the question. The importance of a question is evaluated based on, for example, the urgency, the scope of impact, the user's priority, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the question category. For example, the analysis unit applies a specialized analysis algorithm to technical questions. The analysis unit can also apply a simple analysis algorithm to general questions. The analysis unit can also select the optimal analysis algorithm depending on the question category. This allows the analysis unit to provide more appropriate analysis results by applying the optimal analysis algorithm depending on the question category. Question categories are classified into, for example, technical questions, business questions, general questions, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input question category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is excited, the analysis unit can also perform an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide analysis results with a more appropriate length. 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 analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0075] During analysis, the analysis unit can determine the analysis priority based on the time of question submission. The analysis unit can determine the analysis priority based on, for example, the time zone in which the question was submitted. The analysis unit can also determine the analysis priority based on the date the question was submitted. The analysis unit can also determine the analysis priority based on the timing of the question submission. In this way, the analysis unit can perform analysis in a more appropriate order by determining the analysis priority based on the time of question submission. The time of question submission is evaluated based on, for example, a timestamp, the submission date and time, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input question submission time data into the generation AI and have the generation AI determine the analysis priority.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if the relevance of a question is high, the analysis unit can prioritize analysis. Furthermore, if the relevance of a question is low, the analysis unit can postpone analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the questions. In this way, the analysis unit can perform analysis in a more appropriate order by adjusting the order of analysis based on the relevance of the questions. The relevance of questions is evaluated based on, for example, similarity of content, related topics, etc. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0077] The generation unit can estimate the user's emotions and adjust the answer generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an answer that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate an answer that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an answer that adds a visually stimulating effect. This allows the generation unit to adjust the answer generation method according to the user's emotions, thereby generating a more appropriate answer. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the answer generation method.

[0078] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also determine the priority of the answer based on the importance of the question. As a result, the generation unit can provide a more appropriate answer by adjusting the level of detail of the answer based on the importance of the question. The importance of a question is evaluated based on, for example, the urgency, the scope of impact, the user's priority, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0079] When generating an answer, the generation unit can apply different generation algorithms depending on the question category. For example, the generation unit applies a specialized generation algorithm to technical questions. The generation unit can also apply a simple generation algorithm to general questions. The generation unit can also select the optimal generation algorithm depending on the question category. This allows the generation unit to provide a more appropriate answer by applying the optimal generation algorithm depending on the question category. Question categories are classified into, for example, technical questions, business questions, general questions, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0080] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point answer. If the user is relaxed, the generation unit can generate a longer answer with detailed explanations. If the user is excited, the generation unit can generate an answer with visually stimulating effects. This allows the generation unit to provide a more appropriate answer by adjusting the length of the answer according to the user's emotions. The emotion estimation is realized 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 generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0081] When generating answers, the generation unit can determine the priority of answers based on the time of question submission. The generation unit can determine the priority of answers based on, for example, the time period when the question was submitted. The generation unit can also determine the priority of answers based on the date when the question was submitted. The generation unit can also determine the priority of answers based on the timing when the question was submitted. In this way, the generation unit can provide answers in a more appropriate order by determining the priority of answers based on the time when the question was submitted. The time when the question was submitted is evaluated based on, for example, a timestamp, the submission date and time, etc. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0082] When generating answers, the generation unit can adjust the order of answers based on the relevance of the question. For example, if the relevance of the question is high, the generation unit can generate answers preferentially. Furthermore, if the relevance of the question is low, the generation unit can also postpone generating answers. Furthermore, the generation unit can adjust the order of answers based on the relevance of the question. In this way, the generation unit can provide answers in a more appropriate order by adjusting the order of answers based on the relevance of the question. The relevance of a question is evaluated based on, for example, similarity of content, related topics, etc. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0083] The providing unit can estimate the user's emotions and adjust the answer providing method based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit uses a method that provides a sense of security. Furthermore, if the user is excited, the providing unit can use a calm and collected method. Furthermore, if the user is relaxed, the providing unit can use a method that includes detailed information. This allows the providing unit to adjust the answer providing method according to the user's emotions, thereby providing an answer in a more appropriate manner. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the answer providing method.

[0084] When providing an answer, the providing unit can select the optimal delivery method by referring to the user's past question history. For example, the providing unit preferentially suggests delivery methods (text, voice, etc.) that the user has frequently used in the past. The providing unit can also predict and suggest a delivery method to be used during a specific time period based on the user's past question history. The providing unit can also select the optimal delivery method based on the user's past question history. In this way, the providing unit can select the optimal delivery method by referring to the user's past question history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past question history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0085] When providing an answer, the providing unit can customize the means of providing the answer based on the user's current situation. For example, if the user is on the move, the providing unit can prioritize providing the answer via voice. Furthermore, if the user is using a desktop, the providing unit can prioritize providing detailed text. Furthermore, if the user is using a smartphone, the providing unit can prioritize providing a combination of concise text and images. This allows the providing unit to provide an answer in a more appropriate manner by customizing the means of providing the answer based on the user's current situation. The current situation is evaluated based on, for example, the time of day, location, device usage, etc. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current situation data into the generation AI and cause the generation AI to customize the means of providing the answer.

[0086] The providing unit can estimate the user's emotions and determine the priority of answer provision based on the estimated user's emotions. For example, if the user is dissatisfied, the providing unit can provide that answer as the highest priority. Furthermore, if the user is satisfied, the providing unit can also prioritize other answers with a higher urgency. Furthermore, if the user is confused, the providing unit can quickly provide that answer to resolve the problem. Thus, the providing unit can determine the priority of answer provision according to the user's emotions, thereby providing answers in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of answer provision.

[0087] When providing an answer, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to the user's travel destination. Furthermore, if the user is at home, the providing unit can prioritize providing information related to the user's home. This allows the providing unit to select the optimal providing method by taking into account the user's geographical location information. The geographical location information is obtained using, for example, GPS data, an IP address, Wi-Fi location information, etc. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0088] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means of providing the answer. For example, the providing unit can prioritize providing information related to topics mentioned by the user on social media. The providing unit can also prioritize providing information related to brands or products the user follows on social media. The providing unit can also prioritize providing highly relevant information based on the user's social media activity history. In this way, the providing unit can prioritize providing highly relevant information by analyzing the user's social media activity. Social media activity is evaluated by analyzing, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of providing the answer. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's question using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes past customer support history. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer to the user's question. The provision unit provides the answer to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's question using the 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 past customer support history. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer to the user's question. The provision unit provides the answer to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's question using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes past customer support history. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer to the user's question. The provision unit provides the answer to the user using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's question using the 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 past customer support history. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer to the user's question. The provision unit provides the answer to the user using the speaker 240 of the robot 414.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When accepting a user's question, the accepting unit can refer to the user's past purchase history and prioritize accepting related questions. For example, the accepting unit can prioritize accepting questions about products the user has purchased in the past. The accepting unit can also check whether the product the user has purchased in the past is still within the warranty period and prioritize accepting questions about products that are still within the warranty period. Furthermore, the accepting unit can analyze the usage status of products the user has purchased in the past and prioritize accepting questions about products that are used frequently. In this way, the accepting unit can prioritize accepting highly relevant questions by referring to the user's past purchase history.

[0091] When analyzing a user's question, the analysis unit can estimate the user's current emotion and adjust the accuracy of the analysis based on the estimated emotion. For example, if the user is feeling anxious, the analysis unit can perform a more detailed analysis and generate an answer that provides a sense of security. If the user is relaxed, the analysis unit can perform a concise analysis and provide a quick answer. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. This allows the analysis unit to provide a more appropriate analysis result by adjusting the accuracy of the analysis according to the user's emotion.

[0092] When generating an answer based on a user's question, the generator can refer to the user's past feedback and improve the quality of the answer based on the feedback. For example, if the user is not satisfied with a previously provided answer, the generator can take that feedback into consideration and generate a more appropriate answer. Also, if the user has given a high rating to a previously provided answer, the generator can refer to that answer to generate an answer to a similar question. Furthermore, the generator can analyze the user's feedback, identify common problems, and improve the quality of the answer based on that. In this way, the generator can provide higher quality answers by referring to the user's past feedback.

[0093] When providing the generated answer to the user, the providing unit can select the optimal providing method by taking into account the user's current device status. For example, if the user is using a smartphone, an answer that combines concise text and images can be provided. If the user is using a desktop, an answer that includes detailed text and links can be provided. Furthermore, if the user is using a tablet, an answer that includes interactive elements can be provided. This allows the providing unit to provide the answer in the optimal manner by taking into account the user's device status.

[0094] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can immediately receive the question, minimizing waiting time. Alternatively, if the user is relaxed, the reception unit can determine that a short wait is acceptable and process other higher priority questions first. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving questions and respond quickly. In this way, the reception unit can adjust the method of receiving questions according to the user's emotions, allowing questions to be received at a more appropriate time.

[0095] When analyzing a user's question, the analysis unit can improve the accuracy of the analysis by taking into account the user's current geographical location information. For example, if the user is in a specific area, information related to that area can be analyzed preferentially. Also, if the user is traveling, information related to the travel destination can be analyzed preferentially. Furthermore, if the user is at home, information related to the home can be analyzed preferentially. In this way, the analysis unit can provide more appropriate analysis results by taking into account the user's geographical location information.

[0096] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated emotions. For example, if the user is feeling anxious, an expression that gives a sense of security can be used. If the user is excited, a calm and collected expression can be used. Furthermore, if the user is relaxed, an expression that includes detailed information can be used. In this way, the generation unit can provide a more appropriate answer by adjusting the way the answer is expressed depending on the user's emotions.

[0097] When providing the generated answer to the user, the providing unit can refer to the user's past behavioral history and select the optimal timing for providing the answer. For example, if the user previously asked a question during a specific time period, the answer can be provided during that time period. Also, if the user previously asked a question on a specific day of the week, the answer can be provided on that day. Furthermore, the providing unit can analyze the user's past behavioral history and predict the optimal timing for providing the answer. This allows the providing unit to provide the answer at the optimal timing by referring to the user's past behavioral history.

[0098] The reception unit can estimate the user's emotions and determine the priority of questions based on the estimated emotions. For example, if the user is dissatisfied, the question can be processed with the highest priority. Alternatively, if the user is satisfied, other questions with a higher urgency can be prioritized. Furthermore, if the user is confused, the question can be processed quickly to resolve the problem. In this way, the reception unit can process questions in a more appropriate order by determining the priority of questions according to the user's emotions.

[0099] When analyzing a user's question, the analysis unit can analyze the user's social media activity and prioritize the analysis of related information. For example, it can prioritize the analysis of questions related to topics mentioned by the user on social media. It can also prioritize the analysis of questions related to brands or products that the user follows on social media. It can also prioritize the analysis of highly relevant questions based on the user's social media activity history. This allows the analysis unit to prioritize the analysis of highly relevant information by analyzing the user's social media activity.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The reception unit receives a user's question. The user's question may include, but is not limited to, how to return a product, checking the status of an order, or technical support. The reception unit may receive the user's question via, for example, a messaging app. Step 2: The analysis unit analyzes the question received by the reception unit. For example, the analysis unit uses a generation AI to analyze past customer support history and obtain data for generating appropriate answers to the user's questions. For example, the generation AI extracts frequently asked questions and their answers from past support history and learns from them. Step 3: The generation unit generates an appropriate answer based on the question analyzed by the analysis unit. The generation unit generates an appropriate answer to the user's question, for example, using a generation AI. For example, if a user asks, "How do I return a product?", the generation AI generates an appropriate answer based on past support history. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit can provide the answer to the user through, for example, a messaging app.

[0102] 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.

[0103] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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, in order to avoid confusion and to 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; a generation unit that generates an appropriate answer based on the question analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit. A system characterized by:

2. The reception unit Accepting user questions via messaging apps 2. The system of claim 1.

3. The analysis unit Analyze past customer support history to obtain data to generate appropriate answers to user questions 2. The system of claim 1.

4. The generation unit Generate appropriate answers based on user questions 2. The system of claim 1.

5. The providing unit Provide the generated answer to the user through a messaging app 2. The system of claim 1.

6. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past question history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When questions are submitted, they are filtered based on the user's current situation and interests.

2. The system of claim 1.

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