system

Abstract

The system according to the embodiment aims to enable users to solve questions and problems about home appliances by voice. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an answering unit, and a questioning unit. The reception unit receives voice input from a user. The analysis unit analyzes the voice input received by the reception unit and generates an answer. The answering unit provides the answer generated by the analysis unit to the user. The questioning unit automatically queries manufacturer support if the answering unit is unable to return an answer.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to quickly and appropriately respond to questions or problems that arise while using home appliances.

[0005] The system according to the embodiment aims to enable users to solve questions and problems about home appliances by voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an answering unit, and a questioning unit. The reception unit receives voice input from a user. The analysis unit analyzes the voice input received by the reception unit and generates an answer. The answering unit provides the answer generated by the analysis unit to the user. The questioning unit automatically queries manufacturer support if the answering unit cannot return an answer. [Effects of the Invention]

[0007] A system according to an embodiment may enable a user to resolve questions or problems related to home appliances by voice. [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) The talking home appliance platform according to an embodiment of the present invention is a system that uses a generative AI to tune the instruction manuals and FAQs of home appliances, allowing users to receive support through voice conversations. The talking home appliance platform can receive requests from each product via an API and return appropriate answers. If it is unable to return an appropriate answer, it also has a function that automatically calls the manufacturer's support center. For example, when a user asks, "How do I use this washing machine?", the generative AI returns an appropriate answer based on the instruction manual and FAQs. If the generative AI is unable to return an appropriate answer, it automatically calls the manufacturer's support center and receives an answer. This allows users to easily use and troubleshoot their home appliances. By providing the "talking home appliance platform," home appliance manufacturers can earn revenue through licenses and API usage fees. This allows the talking home appliance platform to easily help users use and troubleshoot their home appliances. For example, when a user asks how to use a washing machine, the generative AI can return an appropriate answer, saving the user the trouble of reading the instruction manual. By providing the "talking home appliance platform," home appliance manufacturers can earn revenue through licenses and API usage fees.

[0029] A talking home appliance platform according to an embodiment includes a reception unit, an analysis unit, a response unit, and a question unit. The reception unit receives voice input from a user. For example, the reception unit can receive voice input using a microphone. The reception unit can also receive voice input using a smartphone. Furthermore, the reception unit can estimate the user's emotions when receiving the voice input and adjust the timing of receiving the voice input based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can delay the timing of receiving the voice input until the user is relaxed. The analysis unit uses the generation AI to analyze the voice input received by the reception unit and generate an appropriate response. For example, the analysis unit can convert the voice input into text data using speech recognition technology and analyze it using natural language processing technology. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the generation AI can provide a detailed analysis result. The response unit provides the response generated by the analysis unit to the user. For example, the response unit can provide the response in text format. The answering unit can also provide an answer in voice format. Furthermore, the answering 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 in a hurry, the generation AI provides a concise answer. The questioning unit automatically asks manufacturer support if the answering unit cannot return an appropriate answer. For example, if the generation AI cannot return an appropriate answer to a user's question, the questioning unit automatically asks manufacturer support by phone and receives an answer. In this way, the talking home appliance platform according to the embodiment can provide a system in which users can receive support through voice conversation.

[0030] The reception unit analyzes the user's past voice input history and selects a reception method. For example, the reception unit can prioritize reception of phrases that the user has frequently used in the past. The reception unit can also predict phrases that will be used in a specific time period from the user's past voice input history and adjust the reception method. The reception unit can also select an optimal voice recognition algorithm based on the user's past voice input history. This makes it possible to provide an optimal reception method based on the user's past 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 using AI. For example, the reception unit can input the user's past voice input history into a generation AI and have the generation AI select an optimal reception method.

[0031] When receiving a voice input, the reception unit filters the voice input based on the user's current situation and areas of interest. For example, the reception unit can receive only questions related to home appliances currently used by the user. The reception unit can also preferentially receive related questions based on the user's areas of interest. The reception unit can also filter appropriate questions based on the user's current situation (time of day and location). This makes it possible to receive appropriate questions according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and cause the generation AI to filter appropriate questions.

[0032] When receiving voice input, the reception unit selects a reception means according to the user's input method. For example, if the user selects voice input, the reception unit can cause the generation AI to prioritize voice recognition. Furthermore, if the user selects text input, the reception unit can cause the generation AI to perform text analysis. Furthermore, if the user selects gesture input, the reception unit can cause the generation AI to perform gesture recognition. This makes it possible to provide the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's input method to the generation AI and cause the generation AI to select the optimal reception means.

[0033] When receiving a voice input, the reception unit prioritizes receiving highly relevant input in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to the user's home. This makes it possible to prioritize receiving highly relevant questions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant questions.

[0034] The reception unit analyzes the user's social media activity when receiving a voice input and receives related input. For example, the reception unit can prioritize receiving questions related to topics the user is discussing on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. This makes it possible to receive related questions based on the user's social media activity. 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 data on the user's social media activity into the generation AI and cause the generation AI to receive related questions.

[0035] The reception unit customizes the reception method by reflecting the user's past feedback when receiving voice input. The reception unit can, for example, select the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback into a generation AI and have the generation AI customize the reception method.

[0036] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the voice input. For example, the analysis unit allows the generation AI to perform a detailed analysis for important questions. The analysis unit also allows the generation AI to perform a concise analysis for general questions. The analysis unit also allows the generation AI to perform a quick analysis for urgent questions. This allows analysis to be performed with an appropriate level of detail according to the importance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input data on the importance of the voice input to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit applies different analysis algorithms depending on the category of the voice input. For example, the analysis unit allows the generation AI to apply a specialized analysis algorithm to technical questions. The analysis unit also allows the generation AI to apply a simple analysis algorithm to general questions. The analysis unit also allows the generation AI to apply a quick analysis algorithm to urgent questions. This makes it possible to apply an appropriate analysis algorithm depending on the category of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input data of the voice input category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, cause the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also preferentially select a specific analysis method from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit determines the priority of analysis based on the time of submission of the voice input. For example, the analysis unit can cause the generation AI to perform analysis preferentially for urgent questions. The analysis unit can also cause the generation AI to perform analysis with normal priority for general questions. The analysis unit can also cause the generation AI to perform analysis with normal priority for important questions. This allows analysis to be performed with appropriate priority according to the time of submission of the voice input. 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 data on the time of submission of the voice input to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the voice input. For example, the analysis unit can have the generation AI perform analysis preferentially for highly relevant questions. The analysis unit can also have the generation AI perform analysis later for questions with low relevance. The analysis unit can also have the generation AI perform analysis in the normal order for questions with medium relevance. This allows analysis to be performed in an appropriate order according to the relevance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the relevance of the voice input to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can cause the generation AI to use simple terminology. Furthermore, if the user is a beginner, the analysis unit can cause the generation AI to use easy-to-understand terminology. This allows the analysis results to be provided using appropriate technical terminology according to the user's level of expertise. 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 data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] When answering, the answering unit adjusts the level of detail of the answer based on the importance of the voice input. For example, the answering unit can have the generation AI provide a detailed answer to an important question. The answering unit can also have the generation AI provide a concise answer to a general question. The answering unit can also have the generation AI provide a quick answer to an urgent question. This makes it possible to provide an answer with an appropriate level of detail according to the importance of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input data on the importance of the voice input to the generation AI and have the generation AI adjust the level of detail of the answer.

[0043] When answering, the answering unit applies different answering algorithms depending on the category of the voice input. For example, the answering unit can have the generation AI apply a specialized answering algorithm to technical questions. The answering unit can also have the generation AI apply a simple answering algorithm to general questions. The answering unit can also have the generation AI apply a quick answering algorithm to urgent questions. This makes it possible to apply an appropriate answering algorithm depending on the category of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data of the voice input category to the generation AI and have the generation AI apply different answering algorithms.

[0044] When answering a question, the answering unit improves the accuracy of the answer by referring to the user's past answer results. For example, the answering unit can cause the generation AI to improve the accuracy of the answer based on the user's past answer results. The answering unit can also preferentially select a specific answering method from the user's past answer results. The answering unit can also analyze the user's past answer results and improve the accuracy of the answer. This makes it possible to improve the accuracy of the answer based on the user's past answer results. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data on the user's past answer results into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0045] When answering, the answering unit determines the priority of the answers based on the time of submission of the voice input. For example, the answering unit can have the generation AI give priority to answers to urgent questions. The answering unit can also have the generation AI give priority to answers to general questions. The answering unit can also have the generation AI give priority to answers to important questions. This makes it possible to provide answers with appropriate priority according to the time of submission of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data on the time of submission of the voice input to the generation AI and have the generation AI determine the priority of the answers.

[0046] When answering, the answering unit adjusts the order of answers based on the relevance of the voice input. For example, the answering unit can have the generation AI give priority to answers to questions with high relevance. The answering unit can also have the generation AI give answers later to questions with low relevance. The answering unit can also have the generation AI answer in the normal order to questions with medium relevance. This makes it possible to provide answers in an appropriate order according to the relevance of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input data on the relevance of the voice input to the generation AI and have the generation AI adjust the order of the answers.

[0047] When answering, the answering unit adjusts the use of technical terminology in the answer depending on the user's level of expertise. For example, if the user has technical expertise, the answering unit can cause the generation AI to use a lot of technical terminology. Also, if the user has general knowledge, the answering unit can cause the generation AI to use simple terminology. Also, if the user is a beginner, the answering unit can cause the generation AI to use terminology that is easy to understand. This allows the answer to be provided using appropriate technical terminology depending on the user's level of expertise. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] The questioning unit adjusts the level of detail of the question based on the importance of the voice input when asking a question. For example, the questioning unit can have the generation AI ask a detailed question for an important question. The questioning unit can also have the generation AI ask a concise question for a general question. The questioning unit can also have the generation AI ask a quick question for an urgent question. This allows questions to be asked with an appropriate level of detail according to the importance of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the importance of the voice input to the generation AI and cause the generation AI to adjust the level of detail of the question.

[0049] The questioning unit applies different questioning algorithms depending on the category of the voice input when asking a question. For example, the questioning unit can have the generation AI apply a specialized questioning algorithm to a technical question. The questioning unit can also have the generation AI apply a simple questioning algorithm to a general question. The questioning unit can also have the generation AI apply a quick questioning algorithm to an urgent question. This makes it possible to apply an appropriate questioning algorithm depending on the category of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data of the category of the voice input to the generation AI and cause the generation AI to apply different questioning algorithms.

[0050] When asking a question, the questioning unit improves the accuracy of the question by referring to the user's past question results. For example, the questioning unit can cause the generation AI to improve the accuracy of the question based on the user's past question results. The questioning unit can also preferentially select a specific questioning method from the user's past question results. The questioning unit can also analyze the user's past question results and improve the accuracy of the question. This makes it possible to improve the accuracy of the question based on the user's past question results. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the user's past question results into the generation AI and cause the generation AI to improve the accuracy of the question.

[0051] The questioning unit determines the priority of the question based on the time of submission of the voice input when a question is asked. For example, the questioning unit can have the generation AI ask urgent questions with priority. For general questions, the questioning unit can also have the generation AI ask questions with normal priority. For important questions, the questioning unit can also have the generation AI ask questions with priority. This allows questions to be asked with appropriate priority according to the time of submission of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the time of submission of the voice input to the generation AI and have the generation AI determine the priority of the questions.

[0052] The questioning unit adjusts the order of questions based on the relevance of the voice input when asking a question. For example, the questioning unit can have the generation AI ask questions with priority for highly relevant questions. The questioning unit can also have the generation AI ask questions later for questions with low relevance. The questioning unit can also have the generation AI ask questions in the normal order for questions with medium relevance. This allows questions to be asked in an appropriate order according to the relevance of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the relevance of the voice input to the generation AI and have the generation AI adjust the order of questions.

[0053] When asking a question, the questioning unit adjusts the use of technical terminology in the question according to the user's level of expertise. For example, if the user has technical expertise, the questioning unit can cause the generation AI to use a lot of technical terminology. Also, if the user has general knowledge, the questioning unit can cause the generation AI to use simple terminology. Also, if the user is a beginner, the questioning unit can cause the generation AI to use terminology that is easy to understand. This allows questions to be asked using appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI or may be performed without using AI. For example, the questioning unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can analyze background sounds of the user's voice input and improve the accuracy of the analysis based on the background sounds. For example, if the user inputs voice in a noisy environment, the analysis unit can filter the background sounds and remove noise to improve the accuracy of the analysis. Alternatively, if the user inputs voice in a quiet environment, the analysis unit can perform analysis without taking background sounds into account. Furthermore, if the user is listening to specific music, the analysis unit can adjust the timing of the analysis based on the rhythm and tempo of the music. This makes it possible to provide an optimal analysis based on the background sounds of the user's voice input.

[0056] The analysis unit can analyze the context of the user's voice input and improve the accuracy of the analysis based on the context. For example, it can take into account the content of questions the user has asked in the past to understand the context of the current question. Also, if the user is talking about a specific topic, it can prioritize analysis of information related to that topic. Furthermore, if the user asks multiple questions in succession, it can also perform analysis taking into account the relevance of those questions. This makes it possible to provide highly accurate analysis based on the context of the user's voice input.

[0057] The reception unit can analyze the speed of the user's voice input and adjust the reception method based on the speed. For example, if the user speaks quickly, the reception unit can quickly analyze the voice input and respond immediately. On the other hand, if the user speaks slowly, the reception unit can perform a detailed analysis and provide a courteous response. Furthermore, if the user's speaking speed changes, the reception method can be adjusted in real time based on the change. This makes it possible to provide the optimal reception method according to the user's speaking speed.

[0058] The analysis unit can automatically detect the language of the user's voice input and perform analysis according to the language. For example, if the user is speaking English, the analysis unit can perform analysis using English natural language processing technology. Also, if the user is speaking Japanese, the analysis unit can perform analysis using Japanese natural language processing technology. Furthermore, if the user is speaking a mixture of multiple languages, the analysis can be performed according to each language. This makes it possible to provide an appropriate analysis according to the language of the user's voice input.

[0059] The analysis unit can analyze the accent of the user's voice input and improve the accuracy of the analysis based on the accent. For example, if the user has an accent from a specific region, the analysis can be performed based on that accent. Also, if the user has a foreign accent, the analysis can be performed based on that accent. Furthermore, if the user's accent changes, the analysis method can be adjusted based on the change. This makes it possible to provide a highly accurate analysis based on the accent of the user's voice input.

[0060] The reception unit can analyze the background music of the user's voice input and adjust the reception method based on the background music. For example, if the user is listening to relaxing music, the reception unit can provide a relaxing response. If the user is listening to up-tempo music, the reception unit can also provide a quick response. Furthermore, if the user is listening to a specific genre of music, the reception unit can also provide a response according to the genre. This makes it possible to provide an optimal reception method according to the background music of the user's voice input.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives voice input from the user. For example, the reception unit can receive voice input using a microphone or a smartphone. The reception unit can also estimate the user's emotions when receiving the voice input and adjust the timing of receiving the voice input based on the estimated emotions. For example, if the user is feeling stressed, the generation AI can delay the timing of receiving the voice input and wait until the user is relaxed. Step 2: The analysis unit uses the generation AI to analyze the voice input received by the reception unit and generate an appropriate response. For example, the analysis unit converts the voice input into text data using voice recognition technology and performs analysis using natural language processing technology. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, the generation AI will provide a detailed analysis result. Step 3: The answering unit provides the answer generated by the analysis unit to the user. For example, the answering unit can provide the answer in text or audio format. The answering unit can also estimate the user's emotions and adjust the way the answer is expressed based on the estimated emotions. For example, if the user is in a hurry, the generation AI will provide a concise answer. Step 4: If the answering section cannot return an appropriate answer, the questioning section automatically asks the manufacturer's support. For example, if the generation AI cannot return an appropriate answer to a user's question, the questioning section can automatically call the manufacturer's support and obtain an answer.

[0063] (Example 2) The talking home appliance platform according to an embodiment of the present invention is a system that uses a generative AI to tune the instruction manuals and FAQs of home appliances, allowing users to receive support through voice conversations. The talking home appliance platform can receive requests from each product via an API and return appropriate answers. If it is unable to return an appropriate answer, it also has a function that automatically calls the manufacturer's support center. For example, when a user asks, "How do I use this washing machine?", the generative AI returns an appropriate answer based on the instruction manual and FAQs. If the generative AI is unable to return an appropriate answer, it automatically calls the manufacturer's support center and receives an answer. This allows users to easily use and troubleshoot their home appliances. By providing the "talking home appliance platform," home appliance manufacturers can earn revenue through licenses and API usage fees. This allows the talking home appliance platform to easily help users use and troubleshoot their home appliances. For example, when a user asks how to use a washing machine, the generative AI can return an appropriate answer, saving the user the trouble of reading the instruction manual. By providing the "talking home appliance platform," home appliance manufacturers can earn revenue through licenses and API usage fees.

[0064] A talking home appliance platform according to an embodiment includes a reception unit, an analysis unit, a response unit, and a question unit. The reception unit receives voice input from a user. For example, the reception unit can receive voice input using a microphone. The reception unit can also receive voice input using a smartphone. Furthermore, the reception unit can estimate the user's emotions when receiving the voice input and adjust the timing of receiving the voice input based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can delay the timing of receiving the voice input until the user is relaxed. The analysis unit uses the generation AI to analyze the voice input received by the reception unit and generate an appropriate response. For example, the analysis unit can convert the voice input into text data using speech recognition technology and analyze it using natural language processing technology. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the generation AI can provide a detailed analysis result. The response unit provides the response generated by the analysis unit to the user. For example, the response unit can provide the response in text format. The answering unit can also provide an answer in voice format. Furthermore, the answering 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 in a hurry, the generation AI provides a concise answer. The questioning unit automatically asks manufacturer support if the answering unit cannot return an appropriate answer. For example, if the generation AI cannot return an appropriate answer to a user's question, the questioning unit automatically asks manufacturer support by phone and receives an answer. In this way, the talking home appliance platform according to the embodiment can provide a system in which users can receive support through voice conversation.

[0065] The reception unit estimates the user's emotion and adjusts the timing of receiving the voice input based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can cause the generation AI to delay the timing of receiving the voice input and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can cause the generation AI to advance the timing of receiving the voice input, allowing the conversation to proceed smoothly. Furthermore, if the user is in a hurry, the reception unit can cause the generation AI to immediately accept the voice input and respond quickly. This allows the voice input to be accepted at an appropriate timing according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0066] The reception unit analyzes the user's past voice input history and selects a reception method. For example, the reception unit can prioritize reception of phrases that the user has frequently used in the past. The reception unit can also predict phrases that will be used in a specific time period from the user's past voice input history and adjust the reception method. The reception unit can also select an optimal voice recognition algorithm based on the user's past voice input history. This makes it possible to provide an optimal reception method based on the user's past 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 using AI. For example, the reception unit can input the user's past voice input history into a generation AI and have the generation AI select an optimal reception method.

[0067] When receiving a voice input, the reception unit filters the voice input based on the user's current situation and areas of interest. For example, the reception unit can receive only questions related to home appliances currently used by the user. The reception unit can also preferentially receive related questions based on the user's areas of interest. The reception unit can also filter appropriate questions based on the user's current situation (time of day and location). This makes it possible to receive appropriate questions according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and cause the generation AI to filter appropriate questions.

[0068] When receiving voice input, the reception unit selects a reception means according to the user's input method. For example, if the user selects voice input, the reception unit can cause the generation AI to prioritize voice recognition. Furthermore, if the user selects text input, the reception unit can cause the generation AI to perform text analysis. Furthermore, if the user selects gesture input, the reception unit can cause the generation AI to perform gesture recognition. This makes it possible to provide the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's input method to the generation AI and cause the generation AI to select the optimal reception means.

[0069] The reception unit estimates the user's emotions and determines the priority of the voice input to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit allows the generation AI to prioritize receiving important questions. Furthermore, when the user is relaxed, the reception unit allows the generation AI to prioritize receiving general questions. Furthermore, when the user is in a hurry, the reception unit allows the generation AI to prioritize receiving urgent questions. This allows the voice input to be received in a priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0070] When receiving a voice input, the reception unit prioritizes receiving highly relevant input in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to the user's home. This makes it possible to prioritize receiving highly relevant questions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant questions.

[0071] The reception unit analyzes the user's social media activity when receiving a voice input and receives related input. For example, the reception unit can prioritize receiving questions related to topics the user is discussing on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. This makes it possible to receive related questions based on the user's social media activity. 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 data on the user's social media activity into the generation AI and cause the generation AI to receive related questions.

[0072] The reception unit customizes the reception method by reflecting the user's past feedback when receiving voice input. The reception unit can, for example, select the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback into a generation AI and have the generation AI customize the reception method.

[0073] The analysis unit estimates the user's emotions and adjusts the way the analysis is expressed based on the estimated user emotions. For example, if the user is relaxed, the analysis unit allows the generation AI to provide detailed analysis results. If the user is in a hurry, the analysis unit allows the generation AI to provide concise analysis results. If the user is stressed, the analysis unit allows the generation AI to provide easy-to-understand analysis results. This makes it possible to provide analysis results in an appropriate expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the voice input. For example, the analysis unit allows the generation AI to perform a detailed analysis for important questions. The analysis unit also allows the generation AI to perform a concise analysis for general questions. The analysis unit also allows the generation AI to perform a quick analysis for urgent questions. This allows analysis to be performed with an appropriate level of detail according to the importance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input data on the importance of the voice input to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit applies different analysis algorithms depending on the category of the voice input. For example, the analysis unit allows the generation AI to apply a specialized analysis algorithm to technical questions. The analysis unit also allows the generation AI to apply a simple analysis algorithm to general questions. The analysis unit also allows the generation AI to apply a quick analysis algorithm to urgent questions. This makes it possible to apply an appropriate analysis algorithm depending on the category of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input data of the voice input category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0076] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, cause the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also preferentially select a specific analysis method from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0077] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit allows the generation AI to provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit allows the generation AI to provide concise analysis results. Furthermore, if the user is stressed, the analysis unit allows the generation AI to provide easy-to-understand analysis results. This makes it possible to provide analysis results of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] During analysis, the analysis unit determines the priority of analysis based on the time of submission of the voice input. For example, the analysis unit can cause the generation AI to perform analysis preferentially for urgent questions. The analysis unit can also cause the generation AI to perform analysis with normal priority for general questions. The analysis unit can also cause the generation AI to perform analysis with normal priority for important questions. This allows analysis to be performed with appropriate priority according to the time of submission of the voice input. 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 data on the time of submission of the voice input to the generation AI and have the generation AI determine the analysis priority.

[0079] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the voice input. For example, the analysis unit can have the generation AI perform analysis preferentially for highly relevant questions. The analysis unit can also have the generation AI perform analysis later for questions with low relevance. The analysis unit can also have the generation AI perform analysis in the normal order for questions with medium relevance. This allows analysis to be performed in an appropriate order according to the relevance of the voice input. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the relevance of the voice input to the generation AI and have the generation AI adjust the order of analysis.

[0080] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can cause the generation AI to use simple terminology. Furthermore, if the user is a beginner, the analysis unit can cause the generation AI to use easy-to-understand terminology. This allows the analysis results to be provided using appropriate technical terminology according to the user's level of expertise. 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 data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0081] The answering unit estimates the user's emotions and adjusts the way the answer is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide a detailed answer. If the user is in a hurry, the answering unit can also provide a concise answer. If the user is stressed, the generation AI can also provide an easy-to-understand answer. This makes it possible to provide an answer in an appropriate way of expression according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] When answering, the answering unit adjusts the level of detail of the answer based on the importance of the voice input. For example, the answering unit can have the generation AI provide a detailed answer to an important question. The answering unit can also have the generation AI provide a concise answer to a general question. The answering unit can also have the generation AI provide a quick answer to an urgent question. This makes it possible to provide an answer with an appropriate level of detail according to the importance of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input data on the importance of the voice input to the generation AI and have the generation AI adjust the level of detail of the answer.

[0083] When answering, the answering unit applies different answering algorithms depending on the category of the voice input. For example, the answering unit can have the generation AI apply a specialized answering algorithm to technical questions. The answering unit can also have the generation AI apply a simple answering algorithm to general questions. The answering unit can also have the generation AI apply a quick answering algorithm to urgent questions. This makes it possible to apply an appropriate answering algorithm depending on the category of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data of the voice input category to the generation AI and have the generation AI apply different answering algorithms.

[0084] When answering a question, the answering unit improves the accuracy of the answer by referring to the user's past answer results. For example, the answering unit can cause the generation AI to improve the accuracy of the answer based on the user's past answer results. The answering unit can also preferentially select a specific answering method from the user's past answer results. The answering unit can also analyze the user's past answer results and improve the accuracy of the answer. This makes it possible to improve the accuracy of the answer based on the user's past answer results. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data on the user's past answer results into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0085] The answering unit estimates the user's emotions and adjusts the length of the answer based on the estimated user emotions. For example, if the user is relaxed, the generation AI can provide a detailed answer. If the user is in a hurry, the answering unit can also provide a concise answer. If the user is stressed, the generation AI can also provide an easy-to-understand answer. This allows answers to be provided at an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] When answering, the answering unit determines the priority of the answers based on the time of submission of the voice input. For example, the answering unit can have the generation AI give priority to answers to urgent questions. The answering unit can also have the generation AI give priority to answers to general questions. The answering unit can also have the generation AI give priority to answers to important questions. This makes it possible to provide answers with appropriate priority according to the time of submission of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data on the time of submission of the voice input to the generation AI and have the generation AI determine the priority of the answers.

[0087] When answering, the answering unit adjusts the order of answers based on the relevance of the voice input. For example, the answering unit can have the generation AI give priority to answers to questions with high relevance. The answering unit can also have the generation AI give answers later to questions with low relevance. The answering unit can also have the generation AI answer in the normal order to questions with medium relevance. This makes it possible to provide answers in an appropriate order according to the relevance of the voice input. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input data on the relevance of the voice input to the generation AI and have the generation AI adjust the order of the answers.

[0088] When answering, the answering unit adjusts the use of technical terminology in the answer depending on the user's level of expertise. For example, if the user has technical expertise, the answering unit can cause the generation AI to use a lot of technical terminology. Also, if the user has general knowledge, the answering unit can cause the generation AI to use simple terminology. Also, if the user is a beginner, the answering unit can cause the generation AI to use terminology that is easy to understand. This allows the answer to be provided using appropriate technical terminology depending on the user's level of expertise. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0089] The questioning unit estimates the user's emotions and adjusts the way the question is phrased based on the estimated user emotions. For example, if the user is relaxed, the questioning unit can cause the generation AI to ask detailed questions. If the user is in a hurry, the questioning unit can also cause the generation AI to ask concise questions. If the user is feeling stressed, the questioning unit can also cause the generation AI to ask easy-to-understand questions. This allows the question to be posed in an appropriate way according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The questioning unit adjusts the level of detail of the question based on the importance of the voice input when asking a question. For example, the questioning unit can have the generation AI ask a detailed question for an important question. The questioning unit can also have the generation AI ask a concise question for a general question. The questioning unit can also have the generation AI ask a quick question for an urgent question. This allows questions to be asked with an appropriate level of detail according to the importance of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the importance of the voice input to the generation AI and cause the generation AI to adjust the level of detail of the question.

[0091] The questioning unit applies different questioning algorithms depending on the category of the voice input when asking a question. For example, the questioning unit can have the generation AI apply a specialized questioning algorithm to a technical question. The questioning unit can also have the generation AI apply a simple questioning algorithm to a general question. The questioning unit can also have the generation AI apply a quick questioning algorithm to an urgent question. This makes it possible to apply an appropriate questioning algorithm depending on the category of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data of the category of the voice input to the generation AI and cause the generation AI to apply different questioning algorithms.

[0092] When asking a question, the questioning unit improves the accuracy of the question by referring to the user's past question results. For example, the questioning unit can cause the generation AI to improve the accuracy of the question based on the user's past question results. The questioning unit can also preferentially select a specific questioning method from the user's past question results. The questioning unit can also analyze the user's past question results and improve the accuracy of the question. This makes it possible to improve the accuracy of the question based on the user's past question results. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the user's past question results into the generation AI and cause the generation AI to improve the accuracy of the question.

[0093] The questioning unit estimates the user's emotions and adjusts the length of the questions based on the estimated user emotions. For example, if the user is relaxed, the questioning unit can cause the generation AI to ask detailed questions. If the user is in a hurry, the questioning unit can also cause the generation AI to ask concise questions. If the user is feeling stressed, the questioning unit can also cause the generation AI to ask easy-to-understand questions. This allows questions to be asked at an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The questioning unit determines the priority of the question based on the time of submission of the voice input when a question is asked. For example, the questioning unit can have the generation AI ask urgent questions with priority. For general questions, the questioning unit can also have the generation AI ask questions with normal priority. For important questions, the questioning unit can also have the generation AI ask questions with priority. This allows questions to be asked with appropriate priority according to the time of submission of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the time of submission of the voice input to the generation AI and have the generation AI determine the priority of the questions.

[0095] The questioning unit adjusts the order of questions based on the relevance of the voice input when asking a question. For example, the questioning unit can have the generation AI ask questions with priority for highly relevant questions. The questioning unit can also have the generation AI ask questions later for questions with low relevance. The questioning unit can also have the generation AI ask questions in the normal order for questions with medium relevance. This allows questions to be asked in an appropriate order according to the relevance of the voice input. Some or all of the above-mentioned processing in the questioning unit may be performed using AI, for example, or may be performed without using AI. For example, the questioning unit can input data on the relevance of the voice input to the generation AI and have the generation AI adjust the order of questions.

[0096] When asking a question, the questioning unit adjusts the use of technical terminology in the question according to the user's level of expertise. For example, if the user has technical expertise, the questioning unit can cause the generation AI to use a lot of technical terminology. Also, if the user has general knowledge, the questioning unit can cause the generation AI to use simple terminology. Also, if the user is a beginner, the questioning unit can cause the generation AI to use terminology that is easy to understand. This allows questions to be asked using appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI or may be performed without using AI. For example, the questioning unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, response unit, and question unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14, estimates the user's emotions using the control unit 46A, and adjusts the timing of receiving the voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the voice input using a generation AI, and generates an appropriate response. The response unit provides an answer to the user using the output device 40 of the smart device 14. The question unit is realized by the specific processing unit 290 of the data processing device 12, and automatically queries manufacturer support if an appropriate response cannot be returned. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, answering unit, and questioning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214, estimates the user's emotions using the control unit 46A, and adjusts the timing of receiving the voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the voice input using a generation AI, and generates an appropriate answer. The answering unit provides an answer to the user using the speaker 240 of the smart glasses 214. The questioning unit is realized by the specific processing unit 290 of the data processing device 12, and automatically queries manufacturer support if an appropriate answer cannot be returned. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, answering unit, and questioning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the headset-type terminal 314, estimates the user's emotions using the control unit 46A, and adjusts the timing of receiving the voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the voice input using a generation AI, and generates an appropriate answer. The answering unit provides an answer to the user using the speaker 240 of the headset-type terminal 314. The questioning unit is realized by the specific processing unit 290 of the data processing device 12, and automatically queries manufacturer support if an appropriate answer cannot be returned. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, answering unit, and questioning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414, estimates the user's emotions using the control unit 46A, and adjusts the timing of receiving the voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the voice input using a generation AI, and generates an appropriate answer. The answering unit provides an answer to the user using the speaker 240 of the robot 414. The questioning unit is realized by the specific processing unit 290 of the data processing device 12, and automatically queries manufacturer support if an appropriate answer cannot be returned.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The analysis unit can analyze background sounds of the user's voice input and improve the accuracy of the analysis based on the background sounds. For example, if the user inputs voice in a noisy environment, the analysis unit can filter the background sounds and remove noise to improve the accuracy of the analysis. Alternatively, if the user inputs voice in a quiet environment, the analysis unit can perform analysis without taking background sounds into account. Furthermore, if the user is listening to specific music, the analysis unit can adjust the timing of the analysis based on the rhythm and tempo of the music. This makes it possible to provide an optimal analysis based on the background sounds of the user's voice input.

[0099] The reception unit analyzes the tone and pitch of the user's voice input and can more accurately estimate the user's emotions. For example, if the user's voice is high-pitched, it can be estimated that the user is feeling stressed or nervous, and the reception timing can be adjusted accordingly. Alternatively, if the user's voice is low-pitched, it can be estimated that the user is relaxed, and the conversation can proceed smoothly. Furthermore, if the user's voice tone changes, it can estimate the change in emotion in real time based on that change and take appropriate action. This makes it possible to estimate the user's emotions based on the tone and pitch of their voice, and provide more personalized responses.

[0100] The analysis unit can analyze the context of the user's voice input and improve the accuracy of the analysis based on the context. For example, it can take into account the content of questions the user has asked in the past to understand the context of the current question. Also, if the user is talking about a specific topic, it can prioritize analysis of information related to that topic. Furthermore, if the user asks multiple questions in succession, it can also perform analysis taking into account the relevance of those questions. This makes it possible to provide highly accurate analysis based on the context of the user's voice input.

[0101] The reception unit can analyze the speed of the user's voice input and adjust the reception method based on the speed. For example, if the user speaks quickly, the reception unit can quickly analyze the voice input and respond immediately. On the other hand, if the user speaks slowly, the reception unit can perform a detailed analysis and provide a courteous response. Furthermore, if the user's speaking speed changes, the reception method can be adjusted in real time based on the change. This makes it possible to provide the optimal reception method according to the user's speaking speed.

[0102] The analysis unit can automatically detect the language of the user's voice input and perform analysis according to the language. For example, if the user is speaking English, the analysis unit can perform analysis using English natural language processing technology. Also, if the user is speaking Japanese, the analysis unit can perform analysis using Japanese natural language processing technology. Furthermore, if the user is speaking a mixture of multiple languages, the analysis can be performed according to each language. This makes it possible to provide an appropriate analysis according to the language of the user's voice input.

[0103] The answering unit can estimate the user's emotions and adjust the tone of the answer based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide an answer in a gentle tone. If the user is relaxed, the generation AI can also provide an answer in a friendly tone. Furthermore, if the user is in a hurry, the generation AI can provide an answer in a quick and concise tone. This makes it possible to provide an answer in an appropriate tone according to the user's emotions.

[0104] The analysis unit can analyze the accent of the user's voice input and improve the accuracy of the analysis based on the accent. For example, if the user has an accent from a specific region, the analysis can be performed based on that accent. Also, if the user has a foreign accent, the analysis can be performed based on that accent. Furthermore, if the user's accent changes, the analysis method can be adjusted based on the change. This makes it possible to provide a highly accurate analysis based on the accent of the user's voice input.

[0105] The reception unit can analyze the background music of the user's voice input and adjust the reception method based on the background music. For example, if the user is listening to relaxing music, the reception unit can provide a relaxing response. If the user is listening to up-tempo music, the reception unit can also provide a quick response. Furthermore, if the user is listening to a specific genre of music, the reception unit can also provide a response according to the genre. This makes it possible to provide an optimal reception method according to the background music of the user's voice input.

[0106] The analysis unit can estimate the emotion of the user's voice input and determine the priority of analysis based on the estimated emotion. For example, if the user is feeling stressed, the generation AI can prioritize analyzing important questions. Also, if the user is relaxed, the generation AI can prioritize analyzing general questions. Furthermore, if the user is in a hurry, the generation AI can prioritize analyzing urgent questions. This allows analysis to be prioritized according to the user's emotion.

[0107] The answering unit can estimate the user's emotions and adjust the level of detail in the answer based on the estimated emotions. For example, if the user is relaxed, the generation AI can provide a detailed answer. If the user is in a hurry, the generation AI can provide a concise answer. Furthermore, if the user is stressed, the generation AI can provide an easy-to-understand answer. This makes it possible to provide answers with an appropriate level of detail according to the user's emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit receives voice input from the user. For example, the reception unit can receive voice input using a microphone or a smartphone. The reception unit can also estimate the user's emotions when receiving the voice input and adjust the timing of receiving the voice input based on the estimated emotions. For example, if the user is feeling stressed, the generation AI can delay the timing of receiving the voice input and wait until the user is relaxed. Step 2: The analysis unit uses the generation AI to analyze the voice input received by the reception unit and generate an appropriate response. For example, the analysis unit converts the voice input into text data using voice recognition technology and performs analysis using natural language processing technology. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, the generation AI will provide a detailed analysis result. Step 3: The answering unit provides the answer generated by the analysis unit to the user. For example, the answering unit can provide the answer in text or audio format. The answering unit can also estimate the user's emotions and adjust the way the answer is expressed based on the estimated emotions. For example, if the user is in a hurry, the generation AI will provide a concise answer. Step 4: If the answering section cannot return an appropriate answer, the questioning section automatically asks the manufacturer's support. For example, if the generation AI cannot return an appropriate answer to a user's question, the questioning section can automatically call the manufacturer's support and obtain an answer.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

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

[0115] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

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

[0131] 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.

[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 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.

[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 (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).

[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] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 voice input from a user; an analysis unit that analyzes the voice input received by the reception unit and generates a response; a response unit that provides a user with the response generated by the analysis unit; a questioning unit that automatically asks a question to manufacturer support when the answering unit cannot return an answer; A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions.

2. The system of claim 1.

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

4. The reception unit When accepting voice input, it filters it based on the user's current situation and interests.

2. The system of claim 1.

5. The reception unit When accepting voice input, select the acceptance method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the voice inputs to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting voice input, the system takes into account the user's geographic location information and prioritizes relevant input.

2. The system of claim 1.

8. The reception unit When receiving voice input, analyze the user's social media activity and receive related input.

2. The system of claim 1.

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