system

Abstract

The system according to the embodiment aims to provide a prompt and appropriate answer to a user's question. [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 answer based on information 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 techniques have had the problem of making it difficult to quickly provide appropriate answers to users' questions.

[0005] The system according to the embodiment aims to provide a prompt and appropriate answer to a user's question. [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 answer based on the information 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 provide a prompt and appropriate answer to a user's question. [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 automatically accepts and analyzes user questions and generates and provides appropriate answers using a generation AI. This customer support system includes a reception unit that accepts user questions, an analysis unit that analyzes the questions accepted by the reception unit, a generation unit that generates answers based on the information analyzed by the analysis unit, and a provision unit that provides the answers generated by the generation unit. For example, when a user contacts a customer center, the user's question is input to the reception unit. The reception unit then transmits the question to the analysis unit, which analyzes the question. The analysis unit, for example, uses natural language processing technology to understand the content of the question and extract appropriate information. The generation unit then generates an answer using a generation AI based on the information received from the analysis unit. The generated answer is transmitted to the provision unit in video or text format, and the provision unit provides the answer to the user. For example, if a user asks, "How do I return a product?", the generation AI analyzes the question and generates detailed instructions on how to return the product. The generated answer is provided to the user in video or text format. For example, a video explaining how to return the product is generated and transmitted to the user. This allows the user to receive the answer in a visually easy-to-understand format. In addition, answers are provided in text format, allowing users to select answers according to their preferences. This service can be outsourced by B2B companies. For example, companies can outsource their customer support operations to this service, allowing for more efficient support. Individual users can also use this service, making it possible to cater to a wider range of users. This allows for faster and more appropriate answers to user questions, improving the quality of customer support. For example, if a user asks about how to use a product, the AI ​​can analyze the question and generate and provide a detailed video on how to use it, thereby resolving the user's concerns. This allows the customer support system to efficiently accept, analyze, generate, and provide answers to user questions.

[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 a user's question. The user's question may be, for example, in text format, audio format, or a question about a specific topic, but is not limited to these examples. The reception unit may receive, for example, a text-format question entered by the user. The reception unit may also receive a user's question using voice input. For example, the user may dictate a question using a microphone, and the reception unit may convert the voice into text and accept the question. The reception unit may also prioritize receiving questions about a specific topic. For example, if a user enters a question about a specific product, the reception unit prioritizes receiving the question. The analysis unit analyzes the question received by the reception unit. The analysis unit may, for example, use natural language processing technology to understand the content of the question and extract appropriate information. For example, the analysis unit may understand the context of the question and extract relevant information. The analysis unit may also analyze the intent of the question using a machine learning algorithm. For example, the analysis unit may determine whether the user's question is about how to return a product or how to use the product. The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate an appropriate answer to the user's question. For example, the generation unit inputs the content of a question to the generation AI, which then generates an answer to the question. The generation unit can also use the generation AI to generate an answer in video format. For example, the generation unit inputs the content of a question to the generation AI, which then generates a video for the question. The provision unit provides the answer generated by the generation unit. For example, the provision unit sends the generated answer to the user. For example, the provision unit sends the generated answer in text format to the user by email. The provision unit can also send the generated answer in video format to the user. For example, the provision unit sends the generated video to the user as a link, allowing the user to click the link to watch the video. This allows the customer support system according to the embodiment to efficiently accept and analyze user questions, generate and provide answers.

[0030] The reception unit can analyze the user's past inquiry history and select an appropriate reception method. For example, the reception unit automatically displays as candidates the content of inquiries frequently made by the user in the past. For example, if the user has frequently inquired about how to return a product in the past, the reception unit automatically displays that content as a candidate, saving the user the trouble of asking the same question again. The reception unit can also suggest the optimal reception method (voice, text, etc.) based on the user's past inquiry history. For example, if the user has preferred to make inquiries by voice in the past, the reception unit suggests a voice reception method. Furthermore, the reception unit can predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. For example, if the user frequently makes inquiries during a specific time period, the reception unit suggests the reception method optimal for that time period. This enables efficient response by selecting the optimal reception method based on the user's past inquiry history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal reception method.

[0031] The reception unit can filter questions based on the user's current situation and areas of interest when receiving a question. For example, when a user inputs their current situation, the reception unit prioritizes questions related to that situation. For example, if a user inputs "traveling" as their current situation, the reception unit prioritizes questions related to travel. The reception unit can also filter and display related questions based on the user's areas of interest. For example, the reception unit can identify the user's areas of interest from the user's past searches or social media activity and prioritize questions related to those areas. Furthermore, the reception unit can suggest optimal questions taking into account the user's current situation and areas of interest. For example, if a user inputs "at work" as their current situation and selects "technical support" as their area of ​​interest, the reception unit can suggest questions related to technical support. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be received. Some or all of the above-described processing by 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 current situation data into the generation AI and have the generation AI perform filtering of related questions.

[0032] When accepting questions, the reception unit can prioritize 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 prioritizes accepting questions related to that area. For example, if the user is traveling and is in a specific tourist destination, the reception unit prioritizes accepting questions related to that tourist destination. The reception unit can also filter and display relevant questions based on the user's geographical location information. For example, if the user is in a specific city, questions related to that city are filtered and displayed. The reception unit can also suggest optimal questions based on the user's current location. For example, if the user enters "office" as their current location, the reception unit suggests questions related to offices. This allows questions to be accepted taking into account the user's geographical location information, thereby prioritizing responses to more relevant questions. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI filter relevant questions.

[0033] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the user's social media activity and prioritizes receiving related questions. For example, if the user frequently posts about a specific topic on social media, the reception unit prioritizes receiving questions related to that topic. The reception unit can also filter and display related questions based on the user's social media interests. For example, if the user has many "likes" for a specific product on social media, the reception unit can filter and display questions related to that product. Furthermore, the reception unit can suggest optimal questions taking the user's social media activity into consideration. For example, if the user is participating in a specific event on social media, the reception unit can suggest questions related to that event. By analyzing the user's social media activity, related questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI filter relevant questions.

[0034] 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 questions with a high level of importance. For example, when a user contacts customer support, the analysis unit evaluates the importance of the question and performs a detailed analysis on questions with a high level of importance. The analysis unit can also perform a concise analysis on questions with a low level of importance. For example, if a question asked by a user is determined to be of low importance, the analysis unit performs a concise analysis and responds quickly. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the question. For example, if the user's question is of high urgency, the analysis unit performs a detailed analysis and responds quickly. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the question. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input question importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the question category during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical questions. For example, when a user asks a technical question to customer support, the analysis unit applies a specialized analysis algorithm to perform a detailed analysis. The analysis unit can also apply a simple analysis algorithm to general questions. For example, when a user asks a general question, the analysis unit applies a simple analysis algorithm to provide a quick response. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the question category. For example, if the user's question belongs to a business category, the analysis unit applies an analysis algorithm suitable for the business category. This allows for applying the optimal analysis algorithm depending on the question category, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question category data into a generation AI and have the generation AI select an optimal analysis algorithm.

[0036] During analysis, the analysis unit can determine the analysis priority based on the time when the question was submitted. The analysis unit determines the analysis priority based on, for example, the time when the question was submitted. For example, when a user contacts customer support, the analysis unit evaluates the time when the question was submitted and prioritizes analysis of questions submitted earlier. The analysis unit can also quickly analyze recently submitted questions. For example, the analysis unit can quickly respond to and provide analysis results for questions recently submitted by the user. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the question was submitted. For example, if the user's question is highly urgent, the analysis unit prioritizes analysis based on the time when the question was submitted. This enables efficient analysis by determining the analysis priority based on the time when the question was submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without 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.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, the analysis unit prioritizes analysis when the questions are highly relevant. For example, when a user contacts customer support, the analysis unit evaluates the relevance of the questions and prioritizes analysis of highly relevant questions. The analysis unit can also postpone analysis when the questions are low in relevance. For example, if a question asked by a user is determined to be low in relevance, the analysis unit postpones analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the questions. For example, if a user's question is related to a specific topic, the analysis unit prioritizes analysis of questions related to that topic. This enables efficient analysis by adjusting the order of analysis based on the relevance of the questions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0038] 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 with a high importance. For example, when a user inquires with customer support, the generation unit evaluates the importance of the question and generates a detailed answer for the question with a high importance. The generation unit can also generate a concise answer for a question with a low importance. For example, if the question asked by the user is determined to be of low importance, the generation unit generates a concise answer and responds quickly. Furthermore, the generation unit can dynamically adjust the level of detail of the answer depending on the importance of the question. For example, if the user's question is of high urgency, the generation unit generates a detailed answer and responds quickly. This enables an efficient answer by adjusting the level of detail of the answer depending on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input question importance data into the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0039] 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. For example, when a user asks a technical question to customer support, the generation unit applies a specialized generation algorithm to generate a detailed answer. The generation unit can also apply a simple generation algorithm to general questions. For example, when a user asks a general question, the generation unit applies a simple generation algorithm to provide a quick response. Furthermore, the generation unit can select an optimal generation algorithm depending on the question category. For example, if the user's question belongs to a business category, the generation unit applies a generation algorithm suitable for the business category. This allows for the application of an optimal generation algorithm depending on the question category, thereby providing a more accurate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select an optimal generation algorithm.

[0040] When generating answers, the generation unit can determine the priority of answers based on the time when the question was submitted. The generation unit determines the priority of answers based on, for example, the time when the question was submitted. For example, when a user contacts customer support, the generation unit evaluates the time when the question was submitted and prioritizes answers to questions submitted earlier. The generation unit can also quickly generate answers when the question was submitted recently. For example, the generation unit quickly responds to and provides answers to questions submitted recently by the user. Furthermore, the generation unit can dynamically adjust the priority of answers based on the time when the question was submitted. For example, if the user's question is highly urgent, the generation unit prioritizes generating answers based on the time when the question was submitted. This enables efficient answers by determining the priority of answers based on the time when the question was submitted. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input question submission time data into a generation AI and have the generation AI determine the priority of answers.

[0041] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, if the question is highly relevant, the generation unit prioritizes generating answers. For example, when a user inquires with customer support, the generation unit evaluates the relevance of the questions and prioritizes answers to highly relevant questions. The generation unit can also postpone generating an answer if the question is low in relevance. For example, if a question asked by a user is determined to be low in relevance, the generation unit postpones generating an answer. Furthermore, the generation unit can dynamically adjust the order of answers based on the relevance of the questions. For example, if a user's question is related to a specific topic, the generation unit prioritizes answers to questions related to that topic. This enables efficient answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input question relevance data into a generation AI and cause the generation AI to adjust the order of answers.

[0042] When providing an answer, the providing unit can select an appropriate delivery method based on the user's past inquiry history. The providing unit selects the optimal delivery method based on, for example, delivery methods used by the user in the past. For example, if the user has previously preferred text-based answers, the providing unit selects the text-based delivery method. The providing unit can also suggest the optimal delivery method (video, text, etc.) based on the user's past inquiry history. For example, if the user has previously preferred video-based answers, the providing unit suggests the video-based delivery method. Furthermore, the providing unit can predict and suggest the delivery method to be used during a specific time period based on the user's past inquiry history. For example, if the user frequently makes inquiries during a specific time period, the providing unit suggests the delivery method optimal for that time period. This enables efficient responses by selecting the optimal delivery method based on the user's past inquiry history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal delivery method.

[0043] The providing unit can customize the means of providing a response based on the user's current situation when providing an answer. For example, when the user inputs their current situation, the providing unit suggests the means of providing a response that is optimal for that situation. For example, if the user inputs "traveling" as their current situation, the providing unit suggests the means of providing a response that is optimal for traveling. The providing unit can also filter and display related means of providing a response based on the user's current situation. For example, if the user inputs "work" as their current situation, the providing unit filters and displays means of providing a response that is optimal for work. The providing unit can also suggest the optimal means of providing a response by taking the user's current situation into consideration. For example, if the user inputs "office" as their current situation, the providing unit suggests a means of providing a response that is optimal for the office. This allows the provision of a more appropriate response by customizing the means of providing a response based on the user's current situation. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current situation data into a generation AI and cause the generation AI to suggest the optimal means of providing a response.

[0044] When providing an answer, the providing unit can select an appropriate providing method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit prioritizes providing answers related to that area. For example, if the user is traveling and is in a specific tourist destination, the providing unit prioritizes providing answers related to that tourist destination. The providing unit can also filter and display relevant answers based on the user's geographical location information. For example, if the user is in a specific city, answers related to that city can be filtered and displayed. The providing unit can also suggest optimal answers based on the user's current location. For example, if the user enters "office" as their current location, the providing unit suggests answers related to offices. This allows for providing answers that take the user's geographical location information into consideration, thereby providing more relevant answers. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter relevant answers.

[0045] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means of providing the answer. The providing unit, for example, analyzes the user's social media activity and prioritizes providing relevant answers. For example, if the user frequently posts about a specific topic on social media, the providing unit prioritizes providing answers related to that topic. The providing unit can also filter and display relevant answers based on the user's social media interests. For example, if the user has many "likes" for a specific product on social media, the providing unit can filter and display answers related to that product. Furthermore, the providing unit can suggest optimal answers taking the user's social media activity into consideration. For example, if the user is participating in a specific event on social media, the providing unit can suggest answers related to that event. In this way, by analyzing the user's social media activity, relevant answers can be prioritized and provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to filter relevant answers.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] When accepting a user's question, the reception unit can acquire the user's device information and provide an optimal interface. For example, if the user is using a smartphone, the reception unit can provide an interface optimized for the smartphone. Also, if the user is using a tablet, the reception unit can provide an interface optimized for the tablet. Furthermore, if the user is using a desktop computer, the reception unit can provide an interface optimized for the desktop computer. This makes it possible to improve the user experience by providing an optimal interface according to the user's device.

[0048] The analysis unit can analyze the user's past inquiry history and select the optimal analysis algorithm. For example, if the user has frequently asked technical questions in the past, the analysis unit can select an analysis algorithm suitable for technical questions. Also, if the user has frequently asked general questions in the past, the analysis unit can select an analysis algorithm suitable for general questions. Furthermore, if the user has frequently asked questions in a specific category in the past, the analysis unit can select an analysis algorithm suitable for that category. In this way, by selecting the optimal analysis algorithm based on the user's past inquiry history, more accurate analysis results can be provided.

[0049] When a question is received, the analysis unit can determine the priority of analysis based on the user's current situation and areas of interest. For example, if the user inputs "traveling" as the current situation, the analysis unit can prioritize analysis of questions related to travel. Also, if the user selects "technical support" as the area of ​​interest, the analysis unit can prioritize analysis of questions related to technical support. Furthermore, if the user inputs "at work" as the current situation, the analysis unit can prioritize analysis of questions related to work. In this way, by determining the priority of analysis based on the user's current situation and areas of interest, it is possible to provide more relevant analysis results.

[0050] The generation unit can analyze the user's past inquiry history and select the optimal answer generation method. For example, if the user has frequently asked technical questions in the past, the generation unit selects an answer generation method suitable for technical questions. Also, if the user has frequently asked general questions in the past, the generation unit can also select an answer generation method suitable for general questions. Furthermore, if the user has frequently asked questions of a specific category in the past, the generation unit can also select an answer generation method suitable for that category. In this way, by selecting the optimal answer generation method based on the user's past inquiry history, more accurate answers can be provided.

[0051] The providing unit can analyze the user's past inquiry history and select the optimal delivery method. For example, if the user has previously preferred text-format answers, the providing unit can select the text-format delivery method. Also, if the user has previously preferred video-format answers, the providing unit can select the video-format delivery method. Furthermore, if the user has previously made an inquiry during a specific time period, the providing unit can select the optimal delivery method for that time period. This allows for efficient responses by selecting the optimal delivery method based on the user's past inquiry history.

[0052] The providing unit can select the optimal delivery method based on the user's current situation and areas of interest. For example, if the user inputs "traveling" as the current situation, the providing unit selects the optimal delivery method for traveling. Also, if the user selects "technical support" as the area of ​​interest, the providing unit can select the optimal delivery method for technical support. Furthermore, if the user inputs "at work" as the current situation, the providing unit can select the optimal delivery method for work. In this way, by selecting the optimal delivery method based on the user's current situation and areas of interest, it is possible to provide more relevant answers.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives a user's question. The user's question may be in text format, voice format, or on a specific topic. For example, a text format question entered by the user may be received. Alternatively, a user's question may be received using voice input. For example, the user may dictate a question using a microphone, and the reception unit converts the voice into text and receives it. Furthermore, questions on a specific topic may be given priority. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis unit uses natural language processing technology to understand the content of the question and extract appropriate information. For example, it understands the context of the question and extracts relevant information. It can also use machine learning algorithms to analyze the intent of the question. For example, it can determine whether the user's question is about how to return a product or how to use the product. Step 3: The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate an appropriate answer to the user's question. For example, the content of a question is input to the generation AI, and the generation AI generates an answer to that question. The generation AI can also be used to generate an answer in the form of a video. For example, the content of a question is input to the generation AI, and the generation AI generates a video in response to that question. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit sends the generated answer to the user. For example, the providing unit may send the generated answer in text format to the user by email. The providing unit may also send the generated answer in video format to the user. For example, the providing unit may send the generated video to the user as a link so that the user can click on the link to watch the video.

[0055] (Example 2) A customer support system according to an embodiment of the present invention automatically accepts and analyzes user questions and generates and provides appropriate answers using a generation AI. This customer support system includes a reception unit that accepts user questions, an analysis unit that analyzes the questions accepted by the reception unit, a generation unit that generates answers based on the information analyzed by the analysis unit, and a provision unit that provides the answers generated by the generation unit. For example, when a user contacts a customer center, the user's question is input to the reception unit. The reception unit then transmits the question to the analysis unit, which analyzes the question. The analysis unit, for example, uses natural language processing technology to understand the content of the question and extract appropriate information. The generation unit then generates an answer using a generation AI based on the information received from the analysis unit. The generated answer is transmitted to the provision unit in video or text format, and the provision unit provides the answer to the user. For example, if a user asks, "How do I return a product?", the generation AI analyzes the question and generates detailed instructions on how to return the product. The generated answer is provided to the user in video or text format. For example, a video explaining how to return the product is generated and transmitted to the user. This allows the user to receive the answer in a visually easy-to-understand format. In addition, answers are provided in text format, allowing users to select answers according to their preferences. This service can be outsourced by B2B companies. For example, companies can outsource their customer support operations to this service, allowing for more efficient support. Individual users can also use this service, making it possible to cater to a wider range of users. This allows for faster and more appropriate answers to user questions, improving the quality of customer support. For example, if a user asks about how to use a product, the AI ​​can analyze the question and generate and provide a detailed video on how to use it, thereby resolving the user's concerns. This allows the customer support system to efficiently accept, analyze, generate, and provide answers to user questions.

[0056] 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 a user's question. The user's question may be, for example, in text format, audio format, or a question about a specific topic, but is not limited to these examples. The reception unit may receive, for example, a text-format question entered by the user. The reception unit may also receive a user's question using voice input. For example, the user may dictate a question using a microphone, and the reception unit may convert the voice into text and accept the question. The reception unit may also prioritize receiving questions about a specific topic. For example, if a user enters a question about a specific product, the reception unit prioritizes receiving the question. The analysis unit analyzes the question received by the reception unit. The analysis unit may, for example, use natural language processing technology to understand the content of the question and extract appropriate information. For example, the analysis unit may understand the context of the question and extract relevant information. The analysis unit may also analyze the intent of the question using a machine learning algorithm. For example, the analysis unit may determine whether the user's question is about how to return a product or how to use the product. The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate an appropriate answer to the user's question. For example, the generation unit inputs the content of a question to the generation AI, which then generates an answer to the question. The generation unit can also use the generation AI to generate an answer in video format. For example, the generation unit inputs the content of a question to the generation AI, which then generates a video for the question. The provision unit provides the answer generated by the generation unit. For example, the provision unit sends the generated answer to the user. For example, the provision unit sends the generated answer in text format to the user by email. The provision unit can also send the generated answer in video format to the user. For example, the provision unit sends the generated video to the user as a link, allowing the user to click the link to watch the video. This allows the customer support system according to the embodiment to efficiently accept and analyze user questions, generate and provide answers.

[0057] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can immediately receive questions and respond quickly. For example, when a user contacts customer support, the reception unit can estimate that the user is feeling stressed from the user's facial expressions and voice and prioritize receiving questions. Furthermore, if the user is relaxed, the reception unit can take the time to receive detailed questions. For example, if it is estimated that the user is relaxed, the reception unit can ask the user detailed questions to collect more specific information. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving concise questions. For example, if it is estimated that the user is in a hurry, the reception unit can prioritize receiving concise questions and respond quickly. This allows for more appropriate responses by adjusting the timing of receiving questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0058] The reception unit can analyze the user's past inquiry history and select an appropriate reception method. For example, the reception unit automatically displays as candidates the content of inquiries frequently made by the user in the past. For example, if the user has frequently inquired about how to return a product in the past, the reception unit automatically displays that content as a candidate, saving the user the trouble of asking the same question again. The reception unit can also suggest the optimal reception method (voice, text, etc.) based on the user's past inquiry history. For example, if the user has preferred to make inquiries by voice in the past, the reception unit suggests a voice reception method. Furthermore, the reception unit can predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. For example, if the user frequently makes inquiries during a specific time period, the reception unit suggests the reception method optimal for that time period. This enables efficient response by selecting the optimal reception method based on the user's past inquiry history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal reception method.

[0059] The reception unit can filter questions based on the user's current situation and areas of interest when receiving a question. For example, when a user inputs their current situation, the reception unit prioritizes questions related to that situation. For example, if a user inputs "traveling" as their current situation, the reception unit prioritizes questions related to travel. The reception unit can also filter and display related questions based on the user's areas of interest. For example, the reception unit can identify the user's areas of interest from the user's past searches or social media activity and prioritize questions related to those areas. Furthermore, the reception unit can suggest optimal questions taking into account the user's current situation and areas of interest. For example, if a user inputs "at work" as their current situation and selects "technical support" as their area of ​​interest, the reception unit can suggest questions related to technical support. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be received. Some or all of the above-described processing by 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 current situation data into the generation AI and have the generation AI perform filtering of related questions.

[0060] The reception unit can estimate the user's emotions and prioritize questions to be received based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes urgent questions. For example, when a user contacts customer support, the reception unit estimates that the user is feeling stressed from the user's facial expressions and voice, and prioritizes urgent questions. Furthermore, if the user is relaxed, the reception unit can prioritize detailed questions. For example, if the user is estimated to be relaxed, the reception unit prioritizes detailed questions to collect more specific information. Furthermore, if the user is in a hurry, the reception unit can prioritize concise questions. For example, if the user is estimated to be in a hurry, the reception unit prioritizes concise questions to respond quickly. This enables more appropriate responses by prioritizing questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0061] When accepting questions, the reception unit can prioritize 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 prioritizes accepting questions related to that area. For example, if the user is traveling and is in a specific tourist destination, the reception unit prioritizes accepting questions related to that tourist destination. The reception unit can also filter and display relevant questions based on the user's geographical location information. For example, if the user is in a specific city, questions related to that city are filtered and displayed. The reception unit can also suggest optimal questions based on the user's current location. For example, if the user enters "office" as their current location, the reception unit suggests questions related to offices. This allows questions to be accepted taking into account the user's geographical location information, thereby prioritizing responses to more relevant questions. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI filter relevant questions.

[0062] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the user's social media activity and prioritizes receiving related questions. For example, if the user frequently posts about a specific topic on social media, the reception unit prioritizes receiving questions related to that topic. The reception unit can also filter and display related questions based on the user's social media interests. For example, if the user has many "likes" for a specific product on social media, the reception unit can filter and display questions related to that product. Furthermore, the reception unit can suggest optimal questions taking the user's social media activity into consideration. For example, if the user is participating in a specific event on social media, the reception unit can suggest questions related to that event. By analyzing the user's social media activity, related questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI filter relevant questions.

[0063] The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, when a user contacts customer support, the analysis unit estimates that the user is nervous from the user's facial expressions and voice and provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is estimated to be relaxed, the analysis unit provides a detailed analysis result and provides more specific information. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. For example, if the user is estimated to be in a hurry, the analysis unit provides a summary analysis result and responds quickly. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0064] 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 questions with a high level of importance. For example, when a user contacts customer support, the analysis unit evaluates the importance of the question and performs a detailed analysis on questions with a high level of importance. The analysis unit can also perform a concise analysis on questions with a low level of importance. For example, if a question asked by a user is determined to be of low importance, the analysis unit performs a concise analysis and responds quickly. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the question. For example, if the user's question is of high urgency, the analysis unit performs a detailed analysis and responds quickly. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the question. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input question importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] The analysis unit can apply different analysis algorithms depending on the question category during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical questions. For example, when a user asks a technical question to customer support, the analysis unit applies a specialized analysis algorithm to perform a detailed analysis. The analysis unit can also apply a simple analysis algorithm to general questions. For example, when a user asks a general question, the analysis unit applies a simple analysis algorithm to provide a quick response. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the question category. For example, if the user's question belongs to a business category, the analysis unit applies an analysis algorithm suitable for the business category. This allows for applying the optimal analysis algorithm depending on the question category, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question category data into a generation AI and have the generation AI select an optimal analysis algorithm.

[0066] 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 provides a short and concise analysis result. For example, when a user contacts customer support, the analysis unit estimates that the user is in a hurry from the user's facial expressions and voice and provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is estimated to be relaxed, the analysis unit provides a detailed analysis result and more specific information. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the user is estimated to be excited, the analysis unit provides an analysis result with a visually stimulating effect. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0067] During analysis, the analysis unit can determine the analysis priority based on the time when the question was submitted. The analysis unit determines the analysis priority based on, for example, the time when the question was submitted. For example, when a user contacts customer support, the analysis unit evaluates the time when the question was submitted and prioritizes analysis of questions submitted earlier. The analysis unit can also quickly analyze recently submitted questions. For example, the analysis unit can quickly respond to and provide analysis results for questions recently submitted by the user. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the question was submitted. For example, if the user's question is highly urgent, the analysis unit prioritizes analysis based on the time when the question was submitted. This enables efficient analysis by determining the analysis priority based on the time when the question was submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without 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.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, the analysis unit prioritizes analysis when the questions are highly relevant. For example, when a user contacts customer support, the analysis unit evaluates the relevance of the questions and prioritizes analysis of highly relevant questions. The analysis unit can also postpone analysis when the questions are low in relevance. For example, if a question asked by a user is determined to be low in relevance, the analysis unit postpones analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the questions. For example, if a user's question is related to a specific topic, the analysis unit prioritizes analysis of questions related to that topic. This enables efficient analysis by adjusting the order of analysis based on the relevance of the questions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0069] The generation unit can estimate the user's emotions and adjust the answer generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates an answer that proceeds at a leisurely pace. For example, when a user inquires with customer support, the generation unit estimates that the user is relaxed from the user's facial expressions and voice and generates an answer that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate an answer that emphasizes the shortest route. For example, if it is estimated that the user is in a hurry, the generation unit generates an answer that emphasizes the shortest route and responds quickly. Furthermore, if the user is excited, the generation unit can generate an answer that adds visually stimulating effects. For example, if it is estimated that the user is excited, the generation unit generates an answer that adds visually stimulating effects. This allows the answer generation method to be adjusted according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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 may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0070] 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 with a high importance. For example, when a user inquires with customer support, the generation unit evaluates the importance of the question and generates a detailed answer for the question with a high importance. The generation unit can also generate a concise answer for a question with a low importance. For example, if the question asked by the user is determined to be of low importance, the generation unit generates a concise answer and responds quickly. Furthermore, the generation unit can dynamically adjust the level of detail of the answer depending on the importance of the question. For example, if the user's question is of high urgency, the generation unit generates a detailed answer and responds quickly. This enables an efficient answer by adjusting the level of detail of the answer depending on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input question importance data into the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0071] 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. For example, when a user asks a technical question to customer support, the generation unit applies a specialized generation algorithm to generate a detailed answer. The generation unit can also apply a simple generation algorithm to general questions. For example, when a user asks a general question, the generation unit applies a simple generation algorithm to provide a quick response. Furthermore, the generation unit can select an optimal generation algorithm depending on the question category. For example, if the user's question belongs to a business category, the generation unit applies a generation algorithm suitable for the business category. This allows for the application of an optimal generation algorithm depending on the question category, thereby providing a more accurate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select an optimal generation algorithm.

[0072] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point answer. For example, when a user contacts customer support, the generation unit estimates that the user is in a hurry from the user's facial expressions and voice and generates a short, to-the-point answer. The generation unit can also generate a longer answer with detailed explanations if the user is relaxed. For example, if the user is estimated to be relaxed, the generation unit generates a longer answer with detailed explanations to provide more specific information. Furthermore, if the user is excited, the generation unit can generate an answer with visually stimulating effects. For example, if the user is estimated to be excited, the generation unit generates an answer with visually stimulating effects. This allows the length of the answer to be adjusted according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0073] When generating answers, the generation unit can determine the priority of answers based on the time when the question was submitted. The generation unit determines the priority of answers based on, for example, the time when the question was submitted. For example, when a user contacts customer support, the generation unit evaluates the time when the question was submitted and prioritizes answers to questions submitted earlier. The generation unit can also quickly generate answers when the question was submitted recently. For example, the generation unit quickly responds to and provides answers to questions submitted recently by the user. Furthermore, the generation unit can dynamically adjust the priority of answers based on the time when the question was submitted. For example, if the user's question is highly urgent, the generation unit prioritizes generating answers based on the time when the question was submitted. This enables efficient answers by determining the priority of answers based on the time when the question was submitted. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input question submission time data into a generation AI and have the generation AI determine the priority of answers.

[0074] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, if the question is highly relevant, the generation unit prioritizes generating answers. For example, when a user inquires with customer support, the generation unit evaluates the relevance of the questions and prioritizes answers to highly relevant questions. The generation unit can also postpone generating an answer if the question is low in relevance. For example, if a question asked by a user is determined to be low in relevance, the generation unit postpones generating an answer. Furthermore, the generation unit can dynamically adjust the order of answers based on the relevance of the questions. For example, if a user's question is related to a specific topic, the generation unit prioritizes answers to questions related to that topic. This enables efficient answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input question relevance data into a generation AI and cause the generation AI to adjust the order of answers.

[0075] The providing unit can estimate the user's emotions and adjust the answer providing method based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible answer providing method. For example, when a user inquires with customer support, the providing unit estimates that the user is nervous from the user's facial expressions and voice, and provides a simple, highly visible answer providing method. The providing unit can also provide a method including detailed information if the user is relaxed. For example, if it is estimated that the user is relaxed, the providing unit provides a method including detailed information, providing more specific information. Furthermore, if the user is in a hurry, the providing unit can also provide a method that focuses on the main points. For example, if it is estimated that the user is in a hurry, the providing unit provides a method that focuses on the main points, responding quickly. This allows the answer providing method to be adjusted according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0076] When providing an answer, the providing unit can select an appropriate delivery method based on the user's past inquiry history. The providing unit selects the optimal delivery method based on, for example, delivery methods used by the user in the past. For example, if the user has previously preferred text-based answers, the providing unit selects the text-based delivery method. The providing unit can also suggest the optimal delivery method (video, text, etc.) based on the user's past inquiry history. For example, if the user has previously preferred video-based answers, the providing unit suggests the video-based delivery method. Furthermore, the providing unit can predict and suggest the delivery method to be used during a specific time period based on the user's past inquiry history. For example, if the user frequently makes inquiries during a specific time period, the providing unit suggests the delivery method optimal for that time period. This enables efficient responses by selecting the optimal delivery method based on the user's past inquiry history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal delivery method.

[0077] The providing unit can customize the means of providing a response based on the user's current situation when providing an answer. For example, when the user inputs their current situation, the providing unit suggests the means of providing a response that is optimal for that situation. For example, if the user inputs "traveling" as their current situation, the providing unit suggests the means of providing a response that is optimal for traveling. The providing unit can also filter and display related means of providing a response based on the user's current situation. For example, if the user inputs "work" as their current situation, the providing unit filters and displays means of providing a response that is optimal for work. The providing unit can also suggest the optimal means of providing a response by taking the user's current situation into consideration. For example, if the user inputs "office" as their current situation, the providing unit suggests a means of providing a response that is optimal for the office. This allows the provision of a more appropriate response by customizing the means of providing a response based on the user's current situation. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current situation data into a generation AI and cause the generation AI to suggest the optimal means of providing a response.

[0078] The providing unit can estimate the user's emotions and prioritize answers based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit prioritizes providing answers with a high level of urgency. For example, when a user contacts customer support, the providing unit estimates that the user is feeling stressed from the user's facial expressions and voice, and prioritizes answers with a high level of urgency. The providing unit can also prioritize providing detailed answers if the user is relaxed. For example, if it is estimated that the user is relaxed, the providing unit prioritizes providing detailed answers and more specific information. Furthermore, the providing unit can also prioritize providing concise answers if the user is in a hurry. For example, if it is estimated that the user is in a hurry, the providing unit prioritizes providing concise answers and responds quickly. This enables more appropriate responses by prioritizing answers according to the user's emotions. Emotion estimation is realized 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0079] When providing an answer, the providing unit can select an appropriate providing method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit prioritizes providing answers related to that area. For example, if the user is traveling and is in a specific tourist destination, the providing unit prioritizes providing answers related to that tourist destination. The providing unit can also filter and display relevant answers based on the user's geographical location information. For example, if the user is in a specific city, answers related to that city can be filtered and displayed. The providing unit can also suggest optimal answers based on the user's current location. For example, if the user enters "office" as their current location, the providing unit suggests answers related to offices. This allows for providing answers that take the user's geographical location information into consideration, thereby providing more relevant answers. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to filter relevant answers.

[0080] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means of providing the answer. The providing unit, for example, analyzes the user's social media activity and prioritizes providing relevant answers. For example, if the user frequently posts about a specific topic on social media, the providing unit prioritizes providing answers related to that topic. The providing unit can also filter and display relevant answers based on the user's social media interests. For example, if the user has many "likes" for a specific product on social media, the providing unit can filter and display answers related to that product. Furthermore, the providing unit can suggest optimal answers taking the user's social media activity into consideration. For example, if the user is participating in a specific event on social media, the providing unit can suggest answers related to that event. In this way, by analyzing the user's social media activity, relevant answers can be prioritized and provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to filter relevant answers. === 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 is realized by the control unit 46A of the smart device 14 and receives a user's question. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated answer to the user. For example, the reception unit can estimate the user's emotion and adjust the timing of receiving the question based on the estimated emotion. === Hard Collateral 1-2 === Each of the multiple elements including the above-described 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 is realized by the control unit 46A of the smart glasses 214 and receives a user's question. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated answer to the user. For example, the reception unit can estimate the user's emotion and adjust the timing of receiving the question based on the estimated emotion. === 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 is realized by the control unit 46A of the headset type terminal 314 and receives a question from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated answer to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of receiving the question based on the estimated emotions. === 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 is realized by the control unit 46A of the robot 414 and receives a question from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated answer to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of receiving the question based on the estimated emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When accepting a user's question, the reception unit can acquire the user's device information and provide an optimal interface. For example, if the user is using a smartphone, the reception unit can provide an interface optimized for the smartphone. Also, if the user is using a tablet, the reception unit can provide an interface optimized for the tablet. Furthermore, if the user is using a desktop computer, the reception unit can provide an interface optimized for the desktop computer. This makes it possible to improve the user experience by providing an optimal interface according to the user's device.

[0083] The reception unit can estimate the user's emotions and customize the way questions are received based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface. If the user is feeling relaxed, the reception unit can provide an interface that allows the user to input detailed information. Furthermore, if the user is excited, the reception unit can provide a visually appealing interface. This makes it possible to improve the user experience by providing an optimal interface according to the user's emotions.

[0084] The analysis unit can analyze the user's past inquiry history and select the optimal analysis algorithm. For example, if the user has frequently asked technical questions in the past, the analysis unit can select an analysis algorithm suitable for technical questions. Also, if the user has frequently asked general questions in the past, the analysis unit can select an analysis algorithm suitable for general questions. Furthermore, if the user has frequently asked questions in a specific category in the past, the analysis unit can select an analysis algorithm suitable for that category. In this way, by selecting the optimal analysis algorithm based on the user's past inquiry history, more accurate analysis results can be provided.

[0085] When a question is received, the analysis unit can determine the priority of analysis based on the user's current situation and areas of interest. For example, if the user inputs "traveling" as the current situation, the analysis unit can prioritize analysis of questions related to travel. Also, if the user selects "technical support" as the area of ​​interest, the analysis unit can prioritize analysis of questions related to technical support. Furthermore, if the user inputs "at work" as the current situation, the analysis unit can prioritize analysis of questions related to work. In this way, by determining the priority of analysis based on the user's current situation and areas of interest, it is possible to provide more relevant analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a concise and to-the-point analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. This makes it possible to improve the user experience by providing optimal analysis results according to the user's emotions.

[0087] The generation unit can analyze the user's past inquiry history and select the optimal answer generation method. For example, if the user has frequently asked technical questions in the past, the generation unit selects an answer generation method suitable for technical questions. Also, if the user has frequently asked general questions in the past, the generation unit can also select an answer generation method suitable for general questions. Furthermore, if the user has frequently asked questions of a specific category in the past, the generation unit can also select an answer generation method suitable for that category. In this way, by selecting the optimal answer generation method based on the user's past inquiry history, more accurate answers can be provided.

[0088] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an answer that includes detailed explanations. If the user is in a hurry, the generation unit can generate a concise and to-the-point answer. Furthermore, if the user is excited, the generation unit can generate an answer that adds visually appealing effects. This can improve the user experience by providing the optimal answer according to the user's emotions.

[0089] The providing unit can analyze the user's past inquiry history and select the optimal delivery method. For example, if the user has previously preferred text-format answers, the providing unit can select the text-format delivery method. Also, if the user has previously preferred video-format answers, the providing unit can select the video-format delivery method. Furthermore, if the user has previously made an inquiry during a specific time period, the providing unit can select the optimal delivery method for that time period. This allows for efficient responses by selecting the optimal delivery method based on the user's past inquiry history.

[0090] 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 nervous, a simple and highly visible providing method can be provided. If the user is relaxed, a providing method including detailed information can be provided. Furthermore, if the user is in a hurry, a providing method that focuses on the main points can be provided. This makes it possible to improve the user experience by providing the optimal providing method according to the user's emotions.

[0091] The providing unit can select the optimal delivery method based on the user's current situation and areas of interest. For example, if the user inputs "traveling" as the current situation, the providing unit selects the optimal delivery method for traveling. Also, if the user selects "technical support" as the area of ​​interest, the providing unit can select the optimal delivery method for technical support. Furthermore, if the user inputs "at work" as the current situation, the providing unit can select the optimal delivery method for work. In this way, by selecting the optimal delivery method based on the user's current situation and areas of interest, it is possible to provide more relevant answers.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives a user's question. The user's question may be in text format, voice format, or on a specific topic. For example, a text format question entered by the user may be received. Alternatively, a user's question may be received using voice input. For example, the user may dictate a question using a microphone, and the reception unit converts the voice into text and receives it. Furthermore, questions on a specific topic may be given priority. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis unit uses natural language processing technology to understand the content of the question and extract appropriate information. For example, it understands the context of the question and extracts relevant information. It can also use machine learning algorithms to analyze the intent of the question. For example, it can determine whether the user's question is about how to return a product or how to use the product. Step 3: The generation unit generates an answer based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate an appropriate answer to the user's question. For example, the content of a question is input to the generation AI, and the generation AI generates an answer to that question. The generation AI can also be used to generate an answer in the form of a video. For example, the content of a question is input to the generation AI, and the generation AI generates a video in response to that question. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit sends the generated answer to the user. For example, the providing unit may send the generated answer in text format to the user by email. The providing unit may also send the generated answer in video format to the user. For example, the providing unit may send the generated video to the user as a link so that the user can click on the link to watch the video.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0101] The 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.

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

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

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

[0105] Fig. 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.

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

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

[0108] In the 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.

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

[0110] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

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

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0121] 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.

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

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

[0124] In the 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.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 answer based on the information analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit; Equipped with A system characterized by:

2. 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.

3. The reception unit Analyze the user's past inquiry history and select the appropriate reception method 2. The system of claim 1.

4. 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.

5. The reception unit Estimate the user's emotions and prioritize the questions to be accepted based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When accepting questions, prioritize relevant questions based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When a question is received, the social media activity of the user is analyzed and related questions are received.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

Images