System

Abstract

An object of the system according to the embodiment is to quickly and appropriately answer a question from a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a search unit. The reception unit receives a question from a user. The analyzer analyzes the question received by the receiver. The providing unit provides an appropriate answer based on the question analyzed by the analyzing unit. The retrieval part retrieves information on the network based on the question analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, it is difficult to quickly and appropriately respond to questions from users, which may result in a decrease in work efficiency.

[0005] The system according to the embodiment aims to provide prompt and appropriate answers to questions from users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a search unit. The reception unit receives questions from users. The analysis unit analyzes the questions received by the reception unit. The provision unit provides appropriate answers based on the questions analyzed by the analysis unit. The search unit searches for information online based on the questions analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and appropriately answer questions from users. [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 universal chatbot system according to an embodiment of the present invention is a system that can answer all internal and external inquiries and response requests. This system significantly reduces work time by searching a database of past FAQs and online information to provide the most appropriate answer. For example, when a user inputs a question, the system searches a database of past FAQs and online information to provide the most appropriate answer. This mechanism allows users to obtain the necessary information in a short amount of time, allowing them to focus on sales activities such as idea generation and negotiation. This system can also be customized for use with agents and business partners. This allows the universal chatbot system to quickly provide the most appropriate answer to user questions. For example, information gathering that would previously have taken several hours can now be completed in just a few minutes. This allows users to focus on sales activities such as idea generation and negotiation. Furthermore, the system can provide product sales and promotion information to agents and contract and delivery information to business partners. This allows information to be provided tailored to each user's needs.

[0029] The universal chatbot system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a search unit. The reception unit receives questions from users. The users input specific questions, such as, for example, "Please tell me about the specifications of a new product." The analysis unit analyzes the questions received by the reception unit. The analysis unit analyzes the intent of the questions using, for example, natural language processing technology and extracts information for finding appropriate answers. The provision unit provides optimal answers based on the questions analyzed by the analysis unit. The provision unit searches for relevant information from, for example, a past FAQ database and provides the answers. The provision unit can also provide product sales information and promotion information to distributors. The provision unit can also provide contract information and delivery date information to business partners. The search unit searches for information online based on the questions analyzed by the analysis unit. The search unit searches, for example, the latest technical information and market trends, and provides optimal answers. This enables the universal chatbot system according to the embodiment to quickly provide optimal answers to users' questions.

[0030] The providing unit can search for relevant information from a past FAQ database and provide an answer. The past FAQ database includes information such as product specifications, usage methods, and troubleshooting. For example, when a user asks, "Please tell me about the specifications of a new product," the providing unit searches for relevant information from the past FAQ database and provides an answer. In addition, by increasing the frequency of updating the FAQ database, the providing unit can always provide the latest information. In this way, by utilizing the past FAQ database, it is possible to provide quick and accurate answers.

[0031] The search unit can search for information on the Internet and provide the latest information. Information on the Internet includes, for example, the latest technical information, industry news, product reviews, etc. For example, when a user asks, "Tell me about the latest technology trends," the search unit searches for information on the Internet and provides the latest information. In addition, the search unit can provide accurate information by prioritizing searches of reliable information sources. This allows the latest information on the Internet to always provide the most up-to-date answers.

[0032] The provision unit can provide product sales information or promotion information to agents. Information for agents includes, for example, product price information, inventory information, sales promotion campaigns, etc. For example, when an agent asks, "Tell me about current sales campaigns," the provision unit provides the relevant information. The provision unit can also customize the information to meet the needs of the agent. This can support the agent's business by providing information tailored to the agent.

[0033] The provision unit can provide contract information and delivery date information to business partners. Information for business partners includes, for example, contract terms, contract period, expected delivery date, and delivery progress. For example, when a business partner asks, "Please tell me about the current contract terms," ​​the provision unit provides the relevant information. The provision unit can also customize information to meet the needs of business partners. This allows the provision of information specialized for business partners to support their business operations.

[0034] The providing unit can be customized to provide information tailored to the needs of each user. The providing unit customizes the information based on, for example, the user's profile information or past usage history. For example, the providing unit preferentially provides information related to questions the user has frequently asked in the past. The providing unit can also customize the information based on the user's individual settings. This allows the provision of information tailored to the user's needs, thereby improving user satisfaction.

[0035] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. In this way, by analyzing the past question history, the optimal reception method can be provided to the user.

[0036] When receiving a question, the reception unit can filter the questions based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives questions related to the project the user is currently working on. The reception unit can also filter and display related questions based on the user's area of ​​interest. Furthermore, the reception unit can suggest appropriate questions depending on the progress of the user's project. This makes it possible to provide highly relevant information by filtering questions based on the user's project or area of ​​interest.

[0037] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question using text, the acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the acceptance unit can also accept the question using image recognition technology. This allows for selecting the optimal acceptance means depending on the user's input method, thereby improving user convenience.

[0038] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the acceptance unit can also prioritize accepting questions related to the user's travel destination. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting questions related to the user's home. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0039] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit preferentially receives questions related to content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related questions. Furthermore, the reception unit can also suggest related questions by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and select the optimal reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the question. For example, the analysis unit performs a detailed analysis on a question of high importance. The analysis unit can also perform a concise analysis on a question of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis results can be provided.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. The analysis unit can also apply a business-oriented analysis algorithm to business-related questions. Furthermore, the analysis unit can apply a general-purpose analysis algorithm to general questions. In this way, by applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the date and time of question submission. The analysis unit determines the priority of analysis based on, for example, when the question was submitted. The analysis unit can also prioritize analysis if the question was submitted earlier. Furthermore, the analysis unit can also quickly analyze if the question was submitted more recently. In this way, by determining the priority of analysis based on the date and time the question was submitted, a faster response is possible.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if the relevance of a question is high, the analysis unit performs the analysis with priority. Also, if the relevance of a question is low, the analysis unit can postpone the analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the questions. In this way, by adjusting the order of analysis based on the relevance of the questions, more appropriate analysis results can be provided.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of knowledge. For example, if the user has a high level of expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Also, if the user has a low level of expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0047] The providing unit can adjust the level of detail to be provided based on the priority of the answer when providing the answer. For example, the providing unit provides detailed information for an answer with a high level of importance. The providing unit can also provide concise information for an answer with a low level of importance. Furthermore, the providing unit can also determine the priority of the information to be provided based on the importance of the answer. In this way, by adjusting the level of detail to be provided based on the importance of the answer, it is possible to provide a more appropriate answer.

[0048] The providing unit can apply different providing algorithms depending on the category of the answer when providing the answer. For example, the providing unit can apply a specialized providing algorithm to a technical answer. The providing unit can also apply a business-oriented providing algorithm to a business-related answer. Furthermore, the providing unit can also apply a general-purpose providing algorithm to a general answer. In this way, by applying different providing algorithms depending on the category of the answer, more appropriate answers can be provided.

[0049] The providing unit can improve the accuracy of the provision based on the user's past provision results at the time of provision. For example, the providing unit performs current provision based on provision results received by the user in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the accuracy of the provision. Furthermore, the providing unit can analyze the user's past provision results and select an optimal provision algorithm. In this way, the accuracy of the provision can be improved by referring to the user's past provision results.

[0050] The providing unit can determine the priority of providing answers based on the submission date and time of the answers when providing the answers. The providing unit determines the priority of providing answers based on, for example, the time when the answers were submitted. Furthermore, the providing unit can also provide answers preferentially if the answers were submitted earlier. Furthermore, the providing unit can also provide answers quickly if the answers were submitted more recently. In this way, by determining the priority of providing answers based on the time when the answers were submitted, a faster response is possible.

[0051] The providing unit can adjust the order of providing answers based on the relevance of the answers when providing them. For example, if the relevance of the answers is high, the providing unit provides them preferentially. Also, if the relevance of the answers is low, the providing unit can also provide them later. Furthermore, the providing unit can adjust the order of providing answers based on the relevance of the answers. In this way, by adjusting the order of providing answers based on the relevance of the answers, more appropriate answers can be provided.

[0052] The providing unit can adjust the use of technical terms provided in accordance with the user's level of knowledge when providing the answer. For example, if the user's level of expertise is high, the providing unit can provide an answer that uses a lot of technical terms. Also, if the user's level of expertise is low, the providing unit can provide a concise and easy-to-understand answer. Furthermore, the providing unit can adjust the use of technical terms provided in accordance with the user's level of expertise. In this way, by adjusting the use of technical terms provided in accordance with the user's level of expertise, a more appropriate answer can be provided.

[0053] The search unit can improve the accuracy of a search based on the interrelationships between questions during a search. For example, the search unit analyzes the interrelationships between questions and preferentially searches for related information. The search unit can also improve the relevance of search results based on the interrelationships between questions. Furthermore, the search unit can provide optimal search results by taking into account the interrelationships between questions. This makes it possible to provide more relevant search results by taking into account the interrelationships between questions.

[0054] The search unit can conduct a search while taking into account the attribute information of the question submitter. For example, the search unit can search for related information with priority based on the job or position of the question submitter. The search unit can also provide optimal search results based on the industry or specialty of the question submitter. Furthermore, the search unit can search for related information by referring to the question submitter's past question history. In this way, by taking into account the attribute information of the question submitter, more appropriate search results can be provided.

[0055] During a search, the search unit can weight the search based on the number of times a question is submitted. For example, if a question is submitted frequently, the search unit prioritizes searching for related information. Also, if a question is submitted infrequently, the search unit can postpone the search. Furthermore, the search unit can adjust the weighting of search results based on the frequency of question submission. Thus, by weighting the search based on the frequency of question submission, more appropriate search results can be provided.

[0056] The search unit can perform a search while taking into account the geographic distribution of questions. For example, the search unit analyzes the geographic distribution of questions and prioritizes searching for related information. The search unit can also provide optimal search results based on the geographic distribution of questions. Furthermore, the search unit can improve the relevance of search results by taking into account the geographic distribution of questions. In this way, more appropriate search results can be provided by taking into account the geographic distribution of questions.

[0057] The search unit can improve the accuracy of the search by referring to literature related to the question during the search. For example, the search unit refers to literature related to the question and searches for related information preferentially. The search unit can also provide optimal search results based on literature related to the question. Furthermore, the search unit can improve the accuracy of the search results by taking into account literature related to the question. In this way, more appropriate search results can be provided by referring to literature related to the question.

[0058] The search unit can perform a search while taking into account the market value of the question. For example, the search unit analyzes the market value of the question and searches for related information preferentially. The search unit can also provide optimal search results based on the market value of the question. Furthermore, the search unit can adjust the weighting of the search results by taking into account the market value of the question. This allows for more appropriate search results to be provided by taking into account the market value of the question.

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

[0060] The reception unit can analyze the user's past question history and select the optimal reception method. For example, questions that the user has frequently asked in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, the optimal reception method can be provided to the user.

[0061] The providing unit can adjust the level of detail to be provided based on the priority of the answer when providing the answer. For example, detailed information can be provided for an answer with a high level of importance. Also, brief information can be provided for an answer with a low level of importance. Furthermore, the providing unit can determine the priority of the information to be provided according to the importance of the answer. In this way, by adjusting the level of detail to be provided based on the importance of the answer, it is possible to provide a more appropriate answer.

[0062] During a search, the search unit can improve the accuracy of the search based on the interrelationships between questions. For example, the search unit analyzes the interrelationships between questions and prioritizes searching for related information. The search unit can also improve the relevance of search results based on the interrelationships between questions. Furthermore, the search unit can provide optimal search results by taking into account the interrelationships between questions. This allows for providing more relevant search results by taking into account the interrelationships between questions.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the question. For example, a detailed analysis can be performed for a question with a high level of importance. On the other hand, a brief analysis can be performed for a question with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis results can be provided.

[0064] The providing unit can improve the accuracy of the provision based on the user's past provision results when providing information. For example, the current provision is made based on the provision results the user received in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the accuracy of the provision. Furthermore, the providing unit can analyze the user's past provision results and select the optimal provision algorithm. This makes it possible to improve the accuracy of the provision by referring to the user's past provision results.

[0065] The search unit can take into account the attribute information of the question submitter when conducting a search. For example, it can prioritize searching for related information based on the job title or position of the question submitter. The search unit can also provide optimal search results based on the industry or specialty of the question submitter. Furthermore, the search unit can also search for related information by referring to the question submitter's past question history. In this way, by taking into account the attribute information of the question submitter, more appropriate search results can be provided.

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

[0067] Step 1: The reception unit receives a question from the user. The user inputs a specific question, such as, "Please tell me the specifications of the new product." Step 2: The analysis unit analyzes the question received by the reception unit. The analysis unit analyzes the intent of the question using, for example, natural language processing technology, and extracts information to find an appropriate answer. Step 3: The providing unit provides the optimal answer based on the question analyzed by the analyzing unit. For example, the providing unit searches for relevant information from a database of past FAQs and provides the answer. The providing unit can also provide product sales information and promotion information to agents. Furthermore, the providing unit can also provide contract information and delivery date information to business partners. Step 4: The search unit searches for information on the Internet based on the question analyzed by the analysis unit. The search unit searches for the latest technical information or market trends, for example, and provides the most appropriate answer.

[0068] (Example 2) The universal chatbot system according to an embodiment of the present invention is a system that can answer all internal and external inquiries and response requests. This system significantly reduces work time by searching a database of past FAQs and online information to provide the most appropriate answer. For example, when a user inputs a question, the system searches a database of past FAQs and online information to provide the most appropriate answer. This mechanism allows users to obtain the necessary information in a short amount of time, allowing them to focus on sales activities such as idea generation and negotiation. This system can also be customized for use with agents and business partners. This allows the universal chatbot system to quickly provide the most appropriate answer to user questions. For example, information gathering that would previously have taken several hours can now be completed in just a few minutes. This allows users to focus on sales activities such as idea generation and negotiation. Furthermore, the system can provide product sales and promotion information to agents and contract and delivery information to business partners. This allows information to be provided tailored to each user's needs.

[0069] The universal chatbot system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a search unit. The reception unit receives questions from users. The users input specific questions, such as, for example, "Please tell me about the specifications of a new product." The analysis unit analyzes the questions received by the reception unit. The analysis unit analyzes the intent of the questions using, for example, natural language processing technology and extracts information for finding appropriate answers. The provision unit provides optimal answers based on the questions analyzed by the analysis unit. The provision unit searches for relevant information from, for example, a past FAQ database and provides the answers. The provision unit can also provide product sales information and promotion information to distributors. The provision unit can also provide contract information and delivery date information to business partners. The search unit searches for information online based on the questions analyzed by the analysis unit. The search unit searches, for example, the latest technical information and market trends, and provides optimal answers. This enables the universal chatbot system according to the embodiment to quickly provide optimal answers to users' questions.

[0070] The providing unit can search for relevant information from a past FAQ database and provide an answer. The past FAQ database includes information such as product specifications, usage methods, and troubleshooting. For example, when a user asks, "Please tell me about the specifications of a new product," the providing unit searches for relevant information from the past FAQ database and provides an answer. In addition, by increasing the frequency of updating the FAQ database, the providing unit can always provide the latest information. In this way, by utilizing the past FAQ database, it is possible to provide quick and accurate answers.

[0071] The search unit can search for information on the Internet and provide the latest information. Information on the Internet includes, for example, the latest technical information, industry news, product reviews, etc. For example, when a user asks, "Tell me about the latest technology trends," the search unit searches for information on the Internet and provides the latest information. In addition, the search unit can provide accurate information by prioritizing searches of reliable information sources. This allows the latest information on the Internet to always provide the most up-to-date answers.

[0072] The provision unit can provide product sales information or promotion information to agents. Information for agents includes, for example, product price information, inventory information, sales promotion campaigns, etc. For example, when an agent asks, "Tell me about current sales campaigns," the provision unit provides the relevant information. The provision unit can also customize the information to meet the needs of the agent. This can support the agent's business by providing information tailored to the agent.

[0073] The provision unit can provide contract information and delivery date information to business partners. Information for business partners includes, for example, contract terms, contract period, expected delivery date, and delivery progress. For example, when a business partner asks, "Please tell me about the current contract terms," ​​the provision unit provides the relevant information. The provision unit can also customize information to meet the needs of business partners. This allows the provision of information specialized for business partners to support their business operations.

[0074] The providing unit can be customized to provide information tailored to the needs of each user. The providing unit customizes the information based on, for example, the user's profile information or past usage history. For example, the providing unit preferentially provides information related to questions the user has frequently asked in the past. The providing unit can also customize the information based on the user's individual settings. This allows the provision of information tailored to the user's needs, thereby improving user satisfaction.

[0075] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive questions and provide answers immediately. Furthermore, if the user is relaxed, the reception unit can also receive detailed questions and provide more in-depth information. Furthermore, if the user is in a hurry, the reception unit can prioritize brief questions and provide answers quickly. This allows for more appropriate responses by adjusting the timing of question reception 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. In this way, by analyzing the past question history, the optimal reception method can be provided to the user.

[0077] When receiving a question, the reception unit can filter the questions based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives questions related to the project the user is currently working on. The reception unit can also filter and display related questions based on the user's area of ​​interest. Furthermore, the reception unit can suggest appropriate questions depending on the progress of the user's project. This makes it possible to provide highly relevant information by filtering questions based on the user's project or area of ​​interest.

[0078] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question using text, the acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the acceptance unit can also accept the question using image recognition technology. This allows for selecting the optimal acceptance means depending on the user's input method, thereby improving user convenience.

[0079] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize urgent questions. Furthermore, when the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, when the user is in a hurry, the reception unit can prioritize concise questions. This allows for more appropriate responses by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the acceptance unit can also prioritize accepting questions related to the user's travel destination. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting questions related to the user's home. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0081] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit preferentially receives questions related to content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related questions. Furthermore, the reception unit can also suggest related questions by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0082] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and select the optimal reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0083] The analysis unit can 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 nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. 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.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the question. For example, the analysis unit performs a detailed analysis on a question of high importance. The analysis unit can also perform a concise analysis on a question of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis results can be provided.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. The analysis unit can also apply a business-oriented analysis algorithm to business-related questions. Furthermore, the analysis unit can apply a general-purpose analysis algorithm to general questions. In this way, by applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result if the user is excited. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. 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.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the date and time of question submission. The analysis unit determines the priority of analysis based on, for example, when the question was submitted. The analysis unit can also prioritize analysis if the question was submitted earlier. Furthermore, the analysis unit can also quickly analyze if the question was submitted more recently. In this way, by determining the priority of analysis based on the date and time the question was submitted, a faster response is possible.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if the relevance of a question is high, the analysis unit performs the analysis with priority. Also, if the relevance of a question is low, the analysis unit can postpone the analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the questions. In this way, by adjusting the order of analysis based on the relevance of the questions, more appropriate analysis results can be provided.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of knowledge. For example, if the user has a high level of expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Also, if the user has a low level of expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0091] The providing unit can estimate the user's emotions and adjust the way the answer is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible answer. Furthermore, if the user is relaxed, the providing unit can also provide a detailed answer. Furthermore, if the user is in a hurry, the providing unit can also provide an answer that focuses on the main points. This allows for adjusting the way the answer is presented according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The providing unit can adjust the level of detail to be provided based on the priority of the answer when providing the answer. For example, the providing unit provides detailed information for an answer with a high level of importance. The providing unit can also provide concise information for an answer with a low level of importance. Furthermore, the providing unit can also determine the priority of the information to be provided based on the importance of the answer. In this way, by adjusting the level of detail to be provided based on the importance of the answer, it is possible to provide a more appropriate answer.

[0093] The providing unit can apply different providing algorithms depending on the category of the answer when providing the answer. For example, the providing unit can apply a specialized providing algorithm to a technical answer. The providing unit can also apply a business-oriented providing algorithm to a business-related answer. Furthermore, the providing unit can also apply a general-purpose providing algorithm to a general answer. In this way, by applying different providing algorithms depending on the category of the answer, more appropriate answers can be provided.

[0094] The providing unit can improve the accuracy of the provision based on the user's past provision results at the time of provision. For example, the providing unit performs current provision based on provision results received by the user in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the accuracy of the provision. Furthermore, the providing unit can analyze the user's past provision results and select an optimal provision algorithm. In this way, the accuracy of the provision can be improved by referring to the user's past provision results.

[0095] The providing unit can estimate the user's emotions and adjust the length of the answer to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a short and to-the-point answer. The providing unit can also provide a detailed answer if the user is relaxed. Furthermore, if the user is excited, the providing unit can also provide a visually stimulating answer. This allows for adjusting the length of the answer according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The providing unit can determine the priority of providing answers based on the submission date and time of the answers when providing the answers. The providing unit determines the priority of providing answers based on, for example, the time when the answers were submitted. Furthermore, the providing unit can also provide answers preferentially if the answers were submitted earlier. Furthermore, the providing unit can also provide answers quickly if the answers were submitted more recently. In this way, by determining the priority of providing answers based on the time when the answers were submitted, a faster response is possible.

[0097] The providing unit can adjust the order of providing answers based on the relevance of the answers when providing them. For example, if the relevance of the answers is high, the providing unit provides them preferentially. Also, if the relevance of the answers is low, the providing unit can also provide them later. Furthermore, the providing unit can adjust the order of providing answers based on the relevance of the answers. In this way, by adjusting the order of providing answers based on the relevance of the answers, more appropriate answers can be provided.

[0098] The providing unit can adjust the use of technical terms provided in accordance with the user's level of knowledge when providing the answer. For example, if the user's level of expertise is high, the providing unit can provide an answer that uses a lot of technical terms. Also, if the user's level of expertise is low, the providing unit can provide a concise and easy-to-understand answer. Furthermore, the providing unit can adjust the use of technical terms provided in accordance with the user's level of expertise. In this way, by adjusting the use of technical terms provided in accordance with the user's level of expertise, a more appropriate answer can be provided.

[0099] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. For example, if the user is nervous, the search unit can provide simple, highly visible search results. The search unit can also provide detailed search results if the user is relaxed. Furthermore, if the user is in a hurry, the search unit can provide search results that focus on the main points. This allows for more appropriate search results to be provided by adjusting the search criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The search unit can improve the accuracy of a search based on the interrelationships between questions during a search. For example, the search unit analyzes the interrelationships between questions and preferentially searches for related information. The search unit can also improve the relevance of search results based on the interrelationships between questions. Furthermore, the search unit can provide optimal search results by taking into account the interrelationships between questions. This makes it possible to provide more relevant search results by taking into account the interrelationships between questions.

[0101] The search unit can conduct a search while taking into account the attribute information of the question submitter. For example, the search unit can search for related information with priority based on the job or position of the question submitter. The search unit can also provide optimal search results based on the industry or specialty of the question submitter. Furthermore, the search unit can search for related information by referring to the question submitter's past question history. In this way, by taking into account the attribute information of the question submitter, more appropriate search results can be provided.

[0102] During a search, the search unit can weight the search based on the number of times a question is submitted. For example, if a question is submitted frequently, the search unit prioritizes searching for related information. Also, if a question is submitted infrequently, the search unit can postpone the search. Furthermore, the search unit can adjust the weighting of search results based on the frequency of question submission. Thus, by weighting the search based on the frequency of question submission, more appropriate search results can be provided.

[0103] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. For example, if the user is nervous, the search unit can prioritize displaying simple, highly visible search results. The search unit can also prioritize displaying detailed search results if the user is relaxed. Furthermore, if the user is in a hurry, the search unit can prioritize displaying search results that focus on the main points. This allows for adjusting the display order of search results according to the user's emotions, thereby providing more appropriate search results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The search unit can perform a search while taking into account the geographic distribution of questions. For example, the search unit analyzes the geographic distribution of questions and prioritizes searching for related information. The search unit can also provide optimal search results based on the geographic distribution of questions. Furthermore, the search unit can improve the relevance of search results by taking into account the geographic distribution of questions. In this way, more appropriate search results can be provided by taking into account the geographic distribution of questions.

[0105] The search unit can improve the accuracy of the search by referring to literature related to the question during the search. For example, the search unit refers to literature related to the question and searches for related information preferentially. The search unit can also provide optimal search results based on literature related to the question. Furthermore, the search unit can improve the accuracy of the search results by taking into account literature related to the question. In this way, more appropriate search results can be provided by referring to literature related to the question.

[0106] The search unit can perform a search while taking into account the market value of the question. For example, the search unit analyzes the market value of the question and searches for related information preferentially. The search unit can also provide optimal search results based on the market value of the question. Furthermore, the search unit can adjust the weighting of the search results by taking into account the market value of the question. This allows for more appropriate search results to be provided by taking into account the market value of the question. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and search unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives questions from users. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the intent of the question using natural language processing technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for relevant information from a past FAQ database and provides an answer. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information on the Internet. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and search unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a question from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the intent of the question using natural language processing technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for relevant information from a past FAQ database and provides an answer. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information on the Internet. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and search unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives questions from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the intent of the question using natural language processing technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for relevant information from a past FAQ database and provides an answer. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information on the Internet. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and search unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the intent of the question using natural language processing technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for relevant information from a past FAQ database and provides an answer. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information on the Internet.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's past question history and select the optimal reception method. For example, questions that the user has frequently asked in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be asked during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, the optimal reception method can be provided to the user.

[0109] The analysis unit can 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 nervous, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, an analysis result that focuses on the main points can be provided. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] The providing unit can estimate the user's emotions and adjust the way in which the answer is presented based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible answer can be provided. If the user is relaxed, a detailed answer can be provided. Furthermore, if the user is in a hurry, an answer that focuses on the main points can be provided. This allows for adjusting the way in which the answer is presented according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated user emotions. For example, if the user is nervous, simple, highly visible search results can be provided. If the user is relaxed, detailed search results can be provided. Furthermore, if the user is in a hurry, search results that focus on the main points can be provided. By adjusting the search criteria according to the user's emotions, more appropriate search results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] The providing unit can adjust the level of detail to be provided based on the priority of the answer when providing the answer. For example, detailed information can be provided for an answer with a high level of importance. Also, brief information can be provided for an answer with a low level of importance. Furthermore, the providing unit can determine the priority of the information to be provided according to the importance of the answer. In this way, by adjusting the level of detail to be provided based on the importance of the answer, it is possible to provide a more appropriate answer.

[0113] During a search, the search unit can improve the accuracy of the search based on the interrelationships between questions. For example, the search unit analyzes the interrelationships between questions and prioritizes searching for related information. The search unit can also improve the relevance of search results based on the interrelationships between questions. Furthermore, the search unit can provide optimal search results by taking into account the interrelationships between questions. This allows for providing more relevant search results by taking into account the interrelationships between questions.

[0114] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the question. For example, a detailed analysis can be performed for a question with a high level of importance. On the other hand, a brief analysis can be performed for a question with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis results can be provided.

[0115] The providing unit can improve the accuracy of the provision based on the user's past provision results when providing information. For example, the current provision is made based on the provision results the user received in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the accuracy of the provision. Furthermore, the providing unit can analyze the user's past provision results and select the optimal provision algorithm. This makes it possible to improve the accuracy of the provision by referring to the user's past provision results.

[0116] The search unit can take into account the attribute information of the question submitter when conducting a search. For example, it can prioritize searching for related information based on the job title or position of the question submitter. The search unit can also provide optimal search results based on the industry or specialty of the question submitter. Furthermore, the search unit can also search for related information by referring to the question submitter's past question history. In this way, by taking into account the attribute information of the question submitter, more appropriate search results can be provided.

[0117] The providing unit can estimate the user's emotions and adjust the length of the answer to be provided based on the estimated user's emotions. For example, if the user is in a hurry, a short and to-the-point answer can be provided. If the user is relaxed, a detailed answer can be provided. Furthermore, if the user is excited, a visually stimulating answer can be provided. This allows for adjusting the length of the answer according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives a question from the user. The user inputs a specific question, such as, "Please tell me the specifications of the new product." Step 2: The analysis unit analyzes the question received by the reception unit. The analysis unit analyzes the intent of the question using, for example, natural language processing technology, and extracts information to find an appropriate answer. Step 3: The providing unit provides the optimal answer based on the question analyzed by the analyzing unit. For example, the providing unit searches for relevant information from a database of past FAQs and provides the answer. The providing unit can also provide product sales information and promotion information to agents. Furthermore, the providing unit can also provide contract information and delivery date information to business partners. Step 4: The search unit searches for information on the Internet based on the question analyzed by the analysis unit. The search unit searches for the latest technical information or market trends, for example, and provides the most appropriate answer.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; a providing unit that provides an appropriate answer based on the question analyzed by the analyzing unit; a search unit that searches for information on the Internet based on the question analyzed by the analysis unit. A system characterized by:

2. The providing unit Search for relevant information in a database of past FAQs and provide answers 2. The system of claim 1.

3. The search unit Searching for information on the Internet and providing the latest information 2. The system of claim 1.

4. The providing unit Providing product sales or promotion information for distributors 2. The system of claim 1.

5. The providing unit Providing contract and delivery information to business partners 2. The system of claim 1.

6. The providing unit Customized to provide information tailored to each user's needs 2. The system of claim 1.

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

2. The system of claim 1.

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

Images