system

Abstract

The system according to the embodiment aims to improve the efficiency of librarians' reference work and provide information that utilizes a wealth of data. [Solution] A system according to an embodiment includes a question receiving unit, an information collecting unit, an analysis unit, an answer generating unit, and a providing unit. The question receiving unit receives a question from a user. The information collecting unit searches related books and archives based on the question received by the question receiving unit. The analysis unit analyzes the information collected by the information collecting unit. The answer generating unit generates an answer based on the information analyzed by the analysis unit. The providing unit provides the answer generated by the answer generating unit to the user.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the reference work of librarians was highly dependent on the individual, making it difficult to provide information efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of librarians' reference work and provide information that utilizes a wealth of data. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, an information collecting unit, an analysis unit, an answer generating unit, and a providing unit. The question receiving unit receives a question from a user. The information collecting unit searches related books and archives based on the question received by the question receiving unit. The analysis unit analyzes the information collected by the information collecting unit. The answer generating unit generates an answer based on the information analyzed by the analysis unit. The providing unit provides the answer generated by the answer generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of librarians' reference work and provide information that utilizes a wealth of data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to improve the efficiency of librarians' reference work. This system uses AI glasses and voice recognition to support searching a library's extensive book database (DB) and archive DB. Next, it generates interactive question and answer content, reducing the workload of librarians. Furthermore, it summarizes papers and extracts keywords to provide useful information to users. This allows librarians to focus on providing specialized support and assisting researchers, while enabling the provision of information utilizing abundant data. For example, it uses AI glasses and voice recognition to support searching a library's book database and archive DB. When a user asks a question such as "I'm looking for a book on a specific topic," the AI ​​analyzes the question and searches related books and archives. This allows users to quickly obtain the information they need. Next, it generates interactive question and answer content. When a user asks a question using AI glasses or voice recognition, the AI ​​analyzes the question and generates an appropriate answer. For example, in response to a question such as "Please tell me the summary of this book," the AI ​​generates a summary of the book and provides it to the user. This reduces the workload of librarians. Furthermore, it summarizes papers and extracts keywords. When a user inquires about a specific paper, the AI ​​analyzes the paper and extracts a summary and keywords. For example, in response to a question such as, "What are the main points of this paper?", the AI ​​generates a summary and keywords for the paper and provides them to the user. This allows users to quickly obtain useful information. This system allows librarians to focus on providing specialized support and assisting researchers, and enables them to provide information using abundant data. For example, librarians can provide deeper knowledge in response to users' specialized questions. Researchers can also use AI to efficiently collect information and advance their research activities. This allows the system, which aims to improve the efficiency of librarians' reference work, to provide quick and appropriate answers to users' questions.

[0029] A reference work support system according to an embodiment includes a question receiving unit, an information collecting unit, an analysis unit, an answer generating unit, and a providing unit. The question receiving unit receives a user's question. The question can be received in, for example, text format or voice format. For example, when a user inputs a question by voice, the question receiving unit converts the question into text using voice recognition technology. Furthermore, when a user inputs a question in text format, the question receiving unit can also directly accept the text. The information collecting unit searches related books and archives based on the question received by the question receiving unit. For example, the information collecting unit searches a library's book collection database or archive database to collect related information. For example, when a user asks a question such as "I'm looking for a book on a specific topic," the information collecting unit analyzes the question and searches related books and archives. The analysis unit analyzes the information collected by the information collecting unit and extracts related information. For example, the analysis unit analyzes the collected information using text mining technology to extract related keywords and phrases. For example, the analysis unit extracts information related to the user's question from the collected information. The answer generation unit generates an appropriate answer to the user's question based on the information analyzed by the analysis unit. The answer generation unit generates an answer to the user's question, for example, using a generation AI. For example, the answer generation unit generates a summary of a book in response to a user's question such as, "Please tell me a summary of this book." The providing unit provides the answer generated by the answer generation unit to the user. The providing unit can provide the generated answer in, for example, a text format or an audio format. For example, when a user inputs a question by voice, the providing unit can provide the generated answer by voice. Furthermore, when a user inputs a question by text, the providing unit can also provide the generated answer by text. This allows the reference work support system according to the embodiment to provide quick and appropriate answers to user questions.

[0030] The information collection unit can search a library's book collection database or archive database. For example, the information collection unit searches a library's book collection database to collect related books and papers. For example, when a user asks a question such as "I'm looking for a book on a specific topic," the information collection unit analyzes the question and searches for related books. The information collection unit can also search an archive database to collect past newspaper articles and historical documents. For example, when a user asks a question such as "I'm looking for information on a specific historical event," the information collection unit analyzes the question and searches for related archives. This allows the information collection unit to efficiently search a library's book collection database or archive database.

[0031] The analysis unit can analyze the collected information and extract related information. For example, the analysis unit analyzes the collected information using text mining technology and extracts related keywords and phrases. For example, the analysis unit extracts information related to the user's question from the collected information. The analysis unit can also analyze the collected information using natural language processing technology and extract related information. For example, the analysis unit extracts information related to the user's question from the collected information. Furthermore, the analysis unit can analyze the collected information using machine learning technology and extract related information. For example, the analysis unit extracts information related to the user's question from the collected information. In this way, the analysis unit can efficiently analyze the collected information and extract related information.

[0032] The answer generation unit can generate answers to user questions. The answer generation unit generates answers to user questions using, for example, a generation AI. For example, the answer generation unit generates a summary of a book in response to a user question such as, "Please tell me a summary of this book." The answer generation unit can also generate a summary of a paper in response to a user question such as, "Please tell me the main points of this paper." Furthermore, the answer generation unit can generate related information in response to a user question such as, "Please tell me information about this topic." For example, the answer generation unit generates related information in response to a user question and provides it to the user. This allows the answer generation unit to generate an appropriate answer to the user's question.

[0033] The providing unit can provide the generated answer to the user. The providing unit can provide the generated answer in, for example, a text format or an audio format. For example, when the user inputs a question by voice, the providing unit can provide the generated answer by audio. Furthermore, when the user inputs a question by text, the providing unit can also provide the generated answer by text. Furthermore, the providing unit can provide the generated answer in real time. For example, when the user inputs a question, the providing unit can instantly provide the generated answer. This allows the providing unit to quickly provide the generated answer to the user.

[0034] The analysis unit can extract a paper's abstract or keywords. The analysis unit, for example, uses generative AI to generate a paper's abstract. For example, the analysis unit generates a paper's abstract in response to a user's question such as, "Please tell me the summary of this paper." The analysis unit can also use text mining technology to extract keywords from a paper. For example, the analysis unit extracts important keywords from a paper and provides them to the user. Furthermore, the analysis unit can simultaneously extract a paper's abstract and keywords. For example, the analysis unit generates a paper's abstract and extracts important keywords from the abstract. This allows the analysis unit to efficiently extract paper summaries and keywords.

[0035] The answer generation unit can provide information to a user based on the extracted summary or keywords. The answer generation unit provides information to a user based on, for example, the extracted summary. For example, in response to a user's question such as "Please tell me the summary of this paper," the answer generation unit provides a summary of the paper. The answer generation unit can also provide information to a user based on the extracted keywords. For example, in response to a user's question such as "Please tell me keywords related to this topic," the answer generation unit provides related keywords. The answer generation unit can also provide useful information to a user based on the extracted summary and keywords. For example, in response to a user's question such as "Please tell me the summary and keywords of this paper," the answer generation unit provides the summary and keywords of the paper. This allows the answer generation unit to quickly provide useful information to a user.

[0036] The question reception unit can analyze the user's past question history and select a question reception method. For example, the question reception unit can automatically display questions that the user has frequently asked in the past as candidates. The question reception unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. Furthermore, the question reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the question reception unit can suggest questions related to a specific time period based on questions that the user has asked in the past. This allows the question reception unit to select the optimal question reception method based on the user's past question history, thereby enabling efficient question reception.

[0037] When accepting a question, the question accepting unit can select questions based on the user's current areas of interest. For example, the question accepting unit preferentially accepts questions related to topics in which the user is currently interested. The question accepting unit can also suggest related questions based on the user's recent search history. Furthermore, the question accepting unit can filter related questions based on events or seminars in which the user is participating. For example, the question accepting unit preferentially accepts questions related to events in which the user is participating. In this way, the question accepting unit can preferentially accept highly relevant questions by filtering questions based on the user's current areas of interest.

[0038] When accepting a question, the question acceptance unit can prioritize accepting relevant questions by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the question acceptance unit can prioritize accepting questions related to that area. Furthermore, when the user is traveling, the question acceptance unit can also prioritize accepting questions related to the travel destination. Furthermore, when the user is in a specific facility, the question acceptance unit can also prioritize accepting questions related to the facility. For example, when the user is in a specific facility, the question acceptance unit prioritizes accepting questions related to the facility. This allows the question acceptance unit to prioritize accepting highly relevant questions by taking into consideration the user's geographical location information, thereby enabling more appropriate question acceptance.

[0039] The question accepting unit can analyze the user's social media activity and accept the question when accepting the question. For example, the question accepting unit can preferentially accept questions related to topics in which the user is interested on social media. The question accepting unit can also suggest related questions based on the content of posts from accounts the user follows. Furthermore, the question accepting unit can filter related questions based on the activity of online communities in which the user participates. For example, the question accepting unit can preferentially accept questions related to online communities in which the user participates. In this way, the question accepting unit can preferentially accept highly relevant questions by analyzing the user's social media activity.

[0040] When collecting information, the information collecting unit can select an information collection method by referring to the user's past search history. For example, the information collecting unit preferentially collects information related to themes that the user has frequently searched for in the past. The information collecting unit can also preferentially suggest search methods (keywords, categories, etc.) that the user has used in the past. Furthermore, the information collecting unit can predict and suggest searches that will be performed in a specific time period based on the user's past search history. For example, the information collecting unit can suggest information related to a specific time period based on searches that the user has performed in the past. This allows the information collecting unit to select the optimal information collection method based on the user's past search history, enabling efficient information collection.

[0041] When collecting information, the information collecting unit can select information based on the user's current research theme. For example, the information collecting unit preferentially collects information related to the theme the user is currently researching. The information collecting unit can also suggest related information based on the user's recent research results. Furthermore, the information collecting unit can filter related information based on research projects in which the user is participating. For example, the information collecting unit preferentially collects information related to research projects in which the user is participating. In this way, the information collecting unit can preferentially collect highly relevant information by filtering information based on the user's current research theme.

[0042] When collecting information, the information collecting unit can prioritize collecting relevant information by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the information collecting unit prioritizes collecting information related to that area. Furthermore, when the user is traveling, the information collecting unit can also prioritize collecting information related to the travel destination. Furthermore, when the user is in a specific facility, the information collecting unit can also prioritize collecting information related to the facility. For example, when the user is in a specific facility, the information collecting unit prioritizes collecting information related to the facility. This allows the information collecting unit to prioritize collecting highly relevant information by taking into consideration the user's geographical location information, thereby enabling more appropriate information collection.

[0043] When collecting information, the information collection unit can analyze the user's social media activities and collect information. For example, the information collection unit can preferentially collect information related to topics in which the user is interested on social media. The information collection unit can also suggest related information based on the content posted by accounts the user follows. Furthermore, the information collection unit can filter related information based on the activities of online communities in which the user participates. For example, the information collection unit preferentially collects information related to online communities in which the user participates. This allows the information collection unit to preferentially collect highly relevant information by analyzing the user's social media activities.

[0044] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information during analysis. For example, the analysis unit analyzes the interrelationships of the collected information and prioritizes the analysis of highly related information. The analysis unit can also improve the accuracy of the analysis based on the interrelationships of the collected information. Furthermore, the analysis unit can provide analysis results by taking into account the interrelationships of the collected information. For example, the analysis unit extracts mutually related information from the collected information and performs analysis based on that information. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of the collected information.

[0045] During the analysis, the analysis unit can perform the analysis by referring to the user's past research results. For example, the analysis unit analyzes related information based on the user's past research results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past research results. Furthermore, the analysis unit can provide analysis results taking the user's past research results into consideration. For example, the analysis unit extracts related information based on the user's past research results and performs analysis based on that information. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past research results.

[0046] The analysis unit can perform the analysis while taking into account the geographical distribution of the information. For example, the analysis unit prioritizes analysis of highly relevant information based on the geographical distribution of the information. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of the information. Furthermore, the analysis unit can provide analysis results based on the geographical distribution of the information. For example, the analysis unit extracts relevant information based on the geographical distribution of the information and performs analysis based on that information. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the geographical distribution of the information.

[0047] The analysis unit can improve the accuracy of the analysis by referring to related literature during analysis. For example, the analysis unit improves the accuracy of the analysis based on related literature. The analysis unit can also provide analysis results by referring to related literature. Furthermore, the analysis unit can improve the accuracy of the analysis by taking related literature into consideration. For example, the analysis unit extracts related information based on related literature and performs analysis based on that information. In this way, the analysis unit improves the accuracy of the analysis by referring to related literature.

[0048] The answer generation unit can change the level of detail of the answer based on the importance of the question when generating an answer. For example, the answer generation unit provides a detailed answer for a question of high importance. The answer generation unit can also provide a concise answer for a question of low importance. Furthermore, the answer generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the answer generation unit provides a detailed answer based on the importance of the question. In this way, the answer generation unit can adjust the level of detail of the answer based on the importance of the question, thereby enabling a more appropriate answer.

[0049] When generating an answer, the answer generation unit can use different answer algorithms depending on the question category. For example, the answer generation unit applies a specialized answer algorithm to a technical question. The answer generation unit can also apply a simple answer algorithm to a general question. Furthermore, the answer generation unit can select the optimal answer algorithm depending on the question category. For example, the answer generation unit applies the optimal answer algorithm based on the question category. In this way, the answer generation unit can provide a more appropriate answer by applying the optimal answer algorithm depending on the question category.

[0050] When generating answers, the answer generation unit can set the priority of answers based on the time of submission of the question. The answer generation unit determines the priority of answers based on, for example, the time period when the question was submitted. The answer generation unit can also prioritize questions that require a quick answer depending on the time of submission of the question. Furthermore, the answer generation unit can determine the optimal order of answers taking into account the time of submission of the question. For example, the answer generation unit prioritizes questions that require a quick answer based on the time of submission of the question. In this way, the answer generation unit can provide more appropriate answers by determining the priority of answers based on the time of submission of the question.

[0051] The answer generation unit can change the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit prioritizes answering the most relevant question based on the relevance of the questions. The answer generation unit can also adjust the order of answers taking into account the relevance of the questions. Furthermore, the answer generation unit can determine the optimal order of answers depending on the relevance of the questions. For example, the answer generation unit prioritizes answering the most relevant question based on the relevance of the questions. In this way, the answer generation unit can adjust the order of answers based on the relevance of the questions, thereby enabling more appropriate answers.

[0052] When providing information, the providing unit can select a delivery method by referring to the user's past usage history. For example, the providing unit preferentially suggests delivery methods that the user has frequently used in the past. The providing unit can also select the optimal delivery method from the user's past usage history. Furthermore, the providing unit can also provide related information based on the user's past usage history. For example, if the user has received information using a specific method in the past, the providing unit preferentially suggests that method. This allows the providing unit to select the optimal delivery method based on the user's past usage history, thereby enabling efficient information provision.

[0053] The providing unit can select a providing method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. For example, if the user is using a smartwatch, the providing unit can prioritize providing highly visible information. This allows the providing unit to select the optimal providing method by taking into consideration the user's device information, thereby enabling efficient information provision.

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

[0055] The question reception unit can analyze the user's past question history and select a question reception method. For example, questions that the user has frequently asked in the past can be automatically displayed as candidates. The question reception unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. Furthermore, the question reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the question reception unit can suggest questions related to a specific time period based on questions that the user has asked in the past. This allows the question reception unit to select the optimal question reception method based on the user's past question history, enabling efficient question reception.

[0056] The information collecting unit can select questions based on the user's current areas of interest. For example, it preferentially accepts questions related to topics in which the user is currently interested. The information collecting unit can also suggest related questions based on the user's recent search history. Furthermore, the information collecting unit can filter related questions based on events or seminars in which the user is participating. For example, the information collecting unit preferentially accepts questions related to events in which the user is participating. In this way, the information collecting unit can preferentially accept highly relevant questions by filtering questions based on the user's current areas of interest.

[0057] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information. For example, the analysis unit analyzes the interrelationships of the collected information and prioritizes the analysis of highly related information. The analysis unit can also improve the accuracy of the analysis based on the interrelationships of the collected information. Furthermore, the analysis unit can provide analysis results by taking into account the interrelationships of the collected information. For example, the analysis unit extracts mutually related information from the collected information and performs analysis based on that information. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of the collected information.

[0058] The answer generation unit can use different answering algorithms depending on the category of the question. For example, a specialized answering algorithm is applied to a technical question. The answer generation unit can also apply a simple answering algorithm to a general question. Furthermore, the answer generation unit can select the optimal answering algorithm depending on the category of the question. For example, the answer generation unit applies the optimal answering algorithm based on the category of the question. In this way, the answer generation unit can provide a more appropriate answer by applying the optimal answering algorithm depending on the category of the question.

[0059] The providing unit can select a providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. For example, if the user is using a smartwatch, the providing unit can provide information with high visibility preferentially. This allows the providing unit to select the optimal providing method by taking into consideration the user's device information, thereby enabling efficient information provision.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The question receiving unit receives a question from the user. The question can be received in text format or voice format. For example, if the user inputs a question by voice, it is converted into text using voice recognition technology. Also, if the user inputs a question in text format, the text can be received directly. Step 2: The information collection unit searches for related books and archives based on the question received by the question reception unit. For example, it searches the library's book collection database and archive database to collect related information. When a user asks a question such as "I'm looking for books on a specific topic," the unit analyzes the question and searches for related books and archives. Step 3: The analysis unit analyzes the information collected by the information collection unit and extracts related information. For example, the collected information is analyzed using text mining technology to extract related keywords and phrases. Information related to the user's question is extracted. Step 4: The answer generation unit generates an appropriate answer to the user's question based on the information analyzed by the analysis unit. For example, a generation AI is used to generate an answer to the user's question. If the user asks, "Please tell me the summary of this book," a summary of the book is generated. Step 5: The providing unit provides the answer generated by the answer generating unit to the user. For example, the generated answer can be provided in text format or audio format. If the user inputs a question by voice, the generated answer can be provided by audio. Also, if the user inputs a question by text, the generated answer can be provided by text.

[0062] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to improve the efficiency of librarians' reference work. This system uses AI glasses and voice recognition to support searching a library's extensive book database (DB) and archive DB. Next, it generates interactive question and answer content, reducing the workload of librarians. Furthermore, it summarizes papers and extracts keywords to provide useful information to users. This allows librarians to focus on providing specialized support and assisting researchers, while enabling the provision of information utilizing abundant data. For example, it uses AI glasses and voice recognition to support searching a library's book database and archive DB. When a user asks a question such as "I'm looking for a book on a specific topic," the AI ​​analyzes the question and searches related books and archives. This allows users to quickly obtain the information they need. Next, it generates interactive question and answer content. When a user asks a question using AI glasses or voice recognition, the AI ​​analyzes the question and generates an appropriate answer. For example, in response to a question such as "Please tell me the summary of this book," the AI ​​generates a summary of the book and provides it to the user. This reduces the workload of librarians. Furthermore, it summarizes papers and extracts keywords. When a user inquires about a specific paper, the AI ​​analyzes the paper and extracts a summary and keywords. For example, in response to a question such as, "What are the main points of this paper?", the AI ​​generates a summary and keywords for the paper and provides them to the user. This allows users to quickly obtain useful information. This system allows librarians to focus on providing specialized support and assisting researchers, and enables them to provide information using abundant data. For example, librarians can provide deeper knowledge in response to users' specialized questions. Researchers can also use AI to efficiently collect information and advance their research activities. This allows the system, which aims to improve the efficiency of librarians' reference work, to provide quick and appropriate answers to users' questions.

[0063] A reference work support system according to an embodiment includes a question receiving unit, an information collecting unit, an analysis unit, an answer generating unit, and a providing unit. The question receiving unit receives a user's question. The question can be received in, for example, text format or voice format. For example, when a user inputs a question by voice, the question receiving unit converts the question into text using voice recognition technology. Furthermore, when a user inputs a question in text format, the question receiving unit can also directly accept the text. The information collecting unit searches related books and archives based on the question received by the question receiving unit. For example, the information collecting unit searches a library's book collection database or archive database to collect related information. For example, when a user asks a question such as "I'm looking for a book on a specific topic," the information collecting unit analyzes the question and searches related books and archives. The analysis unit analyzes the information collected by the information collecting unit and extracts related information. For example, the analysis unit analyzes the collected information using text mining technology to extract related keywords and phrases. For example, the analysis unit extracts information related to the user's question from the collected information. The answer generation unit generates an appropriate answer to the user's question based on the information analyzed by the analysis unit. The answer generation unit generates an answer to the user's question, for example, using a generation AI. For example, the answer generation unit generates a summary of a book in response to a user's question such as, "Please tell me a summary of this book." The providing unit provides the answer generated by the answer generation unit to the user. The providing unit can provide the generated answer in, for example, a text format or an audio format. For example, when a user inputs a question by voice, the providing unit can provide the generated answer by voice. Furthermore, when a user inputs a question by text, the providing unit can also provide the generated answer by text. This allows the reference work support system according to the embodiment to provide quick and appropriate answers to user questions.

[0064] The information collection unit can search a library's book collection database or archive database. For example, the information collection unit searches a library's book collection database to collect related books and papers. For example, when a user asks a question such as "I'm looking for a book on a specific topic," the information collection unit analyzes the question and searches for related books. The information collection unit can also search an archive database to collect past newspaper articles and historical documents. For example, when a user asks a question such as "I'm looking for information on a specific historical event," the information collection unit analyzes the question and searches for related archives. This allows the information collection unit to efficiently search a library's book collection database or archive database.

[0065] The analysis unit can analyze the collected information and extract related information. For example, the analysis unit analyzes the collected information using text mining technology and extracts related keywords and phrases. For example, the analysis unit extracts information related to the user's question from the collected information. The analysis unit can also analyze the collected information using natural language processing technology and extract related information. For example, the analysis unit extracts information related to the user's question from the collected information. Furthermore, the analysis unit can analyze the collected information using machine learning technology and extract related information. For example, the analysis unit extracts information related to the user's question from the collected information. In this way, the analysis unit can efficiently analyze the collected information and extract related information.

[0066] The answer generation unit can generate answers to user questions. The answer generation unit generates answers to user questions using, for example, a generation AI. For example, the answer generation unit generates a summary of a book in response to a user question such as, "Please tell me a summary of this book." The answer generation unit can also generate a summary of a paper in response to a user question such as, "Please tell me the main points of this paper." Furthermore, the answer generation unit can generate related information in response to a user question such as, "Please tell me information about this topic." For example, the answer generation unit generates related information in response to a user question and provides it to the user. This allows the answer generation unit to generate an appropriate answer to the user's question.

[0067] The providing unit can provide the generated answer to the user. The providing unit can provide the generated answer in, for example, a text format or an audio format. For example, when the user inputs a question by voice, the providing unit can provide the generated answer by audio. Furthermore, when the user inputs a question by text, the providing unit can also provide the generated answer by text. Furthermore, the providing unit can provide the generated answer in real time. For example, when the user inputs a question, the providing unit can instantly provide the generated answer. This allows the providing unit to quickly provide the generated answer to the user.

[0068] The analysis unit can extract a paper's abstract or keywords. The analysis unit, for example, uses generative AI to generate a paper's abstract. For example, the analysis unit generates a paper's abstract in response to a user's question such as, "Please tell me the summary of this paper." The analysis unit can also use text mining technology to extract keywords from a paper. For example, the analysis unit extracts important keywords from a paper and provides them to the user. Furthermore, the analysis unit can simultaneously extract a paper's abstract and keywords. For example, the analysis unit generates a paper's abstract and extracts important keywords from the abstract. This allows the analysis unit to efficiently extract paper summaries and keywords.

[0069] The answer generation unit can provide information to a user based on the extracted summary or keywords. The answer generation unit provides information to a user based on, for example, the extracted summary. For example, in response to a user's question such as "Please tell me the summary of this paper," the answer generation unit provides a summary of the paper. The answer generation unit can also provide information to a user based on the extracted keywords. For example, in response to a user's question such as "Please tell me keywords related to this topic," the answer generation unit provides related keywords. The answer generation unit can also provide useful information to a user based on the extracted summary and keywords. For example, in response to a user's question such as "Please tell me the summary and keywords of this paper," the answer generation unit provides the summary and keywords of the paper. This allows the answer generation unit to quickly provide useful information to a user.

[0070] The reference work support system further includes a question acceptance unit that estimates a user's emotions and changes the method of accepting questions based on the estimated user emotions. For example, when a user is stressed, the question acceptance unit provides a simple interface and minimizes input steps. Furthermore, when a user is relaxed, the question acceptance unit can provide detailed input options and suggest customizable input methods. Furthermore, when a user is in a hurry, the question acceptance unit can prioritize voice input and quickly accept questions. For example, when a user inputs a question by voice, the question acceptance unit converts it into text using speech recognition technology and quickly accepts the question. This allows the question acceptance unit to adjust the method of accepting questions according to the user's emotions, thereby enabling more appropriate question acceptance. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0071] The question reception unit can analyze the user's past question history and select a question reception method. For example, the question reception unit can automatically display questions that the user has frequently asked in the past as candidates. The question reception unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. Furthermore, the question reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the question reception unit can suggest questions related to a specific time period based on questions that the user has asked in the past. This allows the question reception unit to select the optimal question reception method based on the user's past question history, thereby enabling efficient question reception.

[0072] When accepting a question, the question accepting unit can select questions based on the user's current areas of interest. For example, the question accepting unit preferentially accepts questions related to topics in which the user is currently interested. The question accepting unit can also suggest related questions based on the user's recent search history. Furthermore, the question accepting unit can filter related questions based on events or seminars in which the user is participating. For example, the question accepting unit preferentially accepts questions related to events in which the user is participating. In this way, the question accepting unit can preferentially accept highly relevant questions by filtering questions based on the user's current areas of interest.

[0073] The question reception unit can estimate the user's emotions and set the priority of questions based on the estimated user emotions. For example, when the user is nervous, the question reception unit can prioritize urgent questions. Furthermore, when the user is relaxed, the question reception unit can prioritize detailed questions. Furthermore, when the user is in a hurry, the question reception unit can prioritize questions that require a quick answer. For example, when the user is in a hurry, the question reception unit prioritizes voice input and quickly accepts questions. This allows the question reception unit to prioritize questions according to the user's emotions, thereby enabling more appropriate question reception. Emotion estimation is realized 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.

[0074] When accepting a question, the question acceptance unit can prioritize accepting relevant questions by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the question acceptance unit can prioritize accepting questions related to that area. Furthermore, when the user is traveling, the question acceptance unit can also prioritize accepting questions related to the travel destination. Furthermore, when the user is in a specific facility, the question acceptance unit can also prioritize accepting questions related to the facility. For example, when the user is in a specific facility, the question acceptance unit prioritizes accepting questions related to the facility. This allows the question acceptance unit to prioritize accepting highly relevant questions by taking into consideration the user's geographical location information, thereby enabling more appropriate question acceptance.

[0075] The question accepting unit can analyze the user's social media activity and accept the question when accepting the question. For example, the question accepting unit can preferentially accept questions related to topics in which the user is interested on social media. The question accepting unit can also suggest related questions based on the content of posts from accounts the user follows. Furthermore, the question accepting unit can filter related questions based on the activity of online communities in which the user participates. For example, the question accepting unit can preferentially accept questions related to online communities in which the user participates. In this way, the question accepting unit can preferentially accept highly relevant questions by analyzing the user's social media activity.

[0076] The information collection unit can estimate the user's emotions and change the information collection method based on the estimated user's emotions. For example, when the user is relaxed, the information collection unit collects detailed information. Furthermore, when the user is in a hurry, the information collection unit can prioritize collecting information that covers the main points. Furthermore, when the user is excited, the information collection unit can collect visually stimulating information. For example, when the user is excited, the information collection unit prioritizes collecting visually stimulating information. This allows the information collection unit to adjust the information collection method according to the user's emotions, thereby enabling more appropriate information collection. Emotion estimation is realized 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.

[0077] When collecting information, the information collecting unit can select an information collection method by referring to the user's past search history. For example, the information collecting unit preferentially collects information related to themes that the user has frequently searched for in the past. The information collecting unit can also preferentially suggest search methods (keywords, categories, etc.) that the user has used in the past. Furthermore, the information collecting unit can predict and suggest searches that will be performed in a specific time period based on the user's past search history. For example, the information collecting unit can suggest information related to a specific time period based on searches that the user has performed in the past. This allows the information collecting unit to select the optimal information collection method based on the user's past search history, enabling efficient information collection.

[0078] When collecting information, the information collecting unit can select information based on the user's current research theme. For example, the information collecting unit preferentially collects information related to the theme the user is currently researching. The information collecting unit can also suggest related information based on the user's recent research results. Furthermore, the information collecting unit can filter related information based on research projects in which the user is participating. For example, the information collecting unit preferentially collects information related to research projects in which the user is participating. In this way, the information collecting unit can preferentially collect highly relevant information by filtering information based on the user's current research theme.

[0079] The information collection unit can estimate the user's emotions and set a priority order for the information to be collected based on the estimated user's emotions. For example, when the user is nervous, the information collection unit prioritizes collecting information with a high degree of urgency. Furthermore, when the user is relaxed, the information collection unit can also prioritize collecting detailed information. Furthermore, when the user is in a hurry, the information collection unit can prioritize collecting information that can be collected quickly. For example, when the user is in a hurry, the information collection unit prioritizes collecting information that covers the main points. This allows the information collection unit to determine the priority order of information to be collected according to the user's emotions, thereby enabling more appropriate information collection. Emotion estimation is realized 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 collecting information, the information collecting unit can prioritize collecting relevant information by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the information collecting unit prioritizes collecting information related to that area. Furthermore, when the user is traveling, the information collecting unit can also prioritize collecting information related to the travel destination. Furthermore, when the user is in a specific facility, the information collecting unit can also prioritize collecting information related to the facility. For example, when the user is in a specific facility, the information collecting unit prioritizes collecting information related to the facility. This allows the information collecting unit to prioritize collecting highly relevant information by taking into consideration the user's geographical location information, thereby enabling more appropriate information collection.

[0081] When collecting information, the information collection unit can analyze the user's social media activities and collect information. For example, the information collection unit can preferentially collect information related to topics in which the user is interested on social media. The information collection unit can also suggest related information based on the content posted by accounts the user follows. Furthermore, the information collection unit can filter related information based on the activities of online communities in which the user participates. For example, the information collection unit preferentially collects information related to online communities in which the user participates. This allows the information collection unit to preferentially collect highly relevant information by analyzing the user's social media activities.

[0082] The analysis unit can estimate the user's emotions and change the analysis method based on the estimated user's emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. Furthermore, the analysis unit can perform a brief analysis when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. For example, the analysis unit can prioritize providing visually stimulating analysis results when the user is excited. This allows the analysis unit to adjust the analysis method according to the user's emotions, thereby enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information during analysis. For example, the analysis unit analyzes the interrelationships of the collected information and prioritizes the analysis of highly related information. The analysis unit can also improve the accuracy of the analysis based on the interrelationships of the collected information. Furthermore, the analysis unit can provide analysis results by taking into account the interrelationships of the collected information. For example, the analysis unit extracts mutually related information from the collected information and performs analysis based on that information. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of the collected information.

[0084] During the analysis, the analysis unit can perform the analysis by referring to the user's past research results. For example, the analysis unit analyzes related information based on the user's past research results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past research results. Furthermore, the analysis unit can provide analysis results taking the user's past research results into consideration. For example, the analysis unit extracts related information based on the user's past research results and performs analysis based on that information. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past research results.

[0085] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit can prioritize a visually concise display method. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, thereby enabling a more appropriate display. 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.

[0086] The analysis unit can perform the analysis while taking into account the geographical distribution of the information. For example, the analysis unit prioritizes analysis of highly relevant information based on the geographical distribution of the information. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of the information. Furthermore, the analysis unit can provide analysis results based on the geographical distribution of the information. For example, the analysis unit extracts relevant information based on the geographical distribution of the information and performs analysis based on that information. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the geographical distribution of the information.

[0087] The analysis unit can improve the accuracy of the analysis by referring to related literature during analysis. For example, the analysis unit improves the accuracy of the analysis based on related literature. The analysis unit can also provide analysis results by referring to related literature. Furthermore, the analysis unit can improve the accuracy of the analysis by taking related literature into consideration. For example, the analysis unit extracts related information based on related literature and performs analysis based on that information. In this way, the analysis unit improves the accuracy of the analysis by referring to related literature.

[0088] The answer generation unit can estimate the user's emotions and change the way the answer is expressed based on the estimated user's emotions. For example, the answer generation unit provides a detailed answer when the user is relaxed. Furthermore, the answer generation unit can provide a concise answer that focuses on the main points when the user is in a hurry. Furthermore, the answer generation unit can provide a visually stimulating answer when the user is excited. For example, when the user is excited, the answer generation unit preferentially provides a visually stimulating answer. This allows the answer generation unit to adjust the way the answer is expressed depending on the user's emotions, thereby enabling 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.

[0089] The answer generation unit can change the level of detail of the answer based on the importance of the question when generating an answer. For example, the answer generation unit provides a detailed answer for a question of high importance. The answer generation unit can also provide a concise answer for a question of low importance. Furthermore, the answer generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the answer generation unit provides a detailed answer based on the importance of the question. In this way, the answer generation unit can adjust the level of detail of the answer based on the importance of the question, thereby enabling a more appropriate answer.

[0090] When generating an answer, the answer generation unit can use different answer algorithms depending on the question category. For example, the answer generation unit applies a specialized answer algorithm to a technical question. The answer generation unit can also apply a simple answer algorithm to a general question. Furthermore, the answer generation unit can select the optimal answer algorithm depending on the question category. For example, the answer generation unit applies the optimal answer algorithm based on the question category. In this way, the answer generation unit can provide a more appropriate answer by applying the optimal answer algorithm depending on the question category.

[0091] The answer generation unit can estimate the user's emotions and change the length of the answer based on the estimated user's emotions. For example, the answer generation unit provides a detailed answer when the user is relaxed. The answer generation unit can also provide a concise answer when the user is in a hurry. Furthermore, the answer generation unit can provide a visually stimulating answer when the user is excited. For example, the answer generation unit preferentially provides a visually stimulating answer when the user is excited. This allows the answer generation unit to adjust the length of the answer according to the user's emotions, thereby enabling 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 such examples.

[0092] When generating answers, the answer generation unit can set the priority of answers based on the time of submission of the question. The answer generation unit determines the priority of answers based on, for example, the time period when the question was submitted. The answer generation unit can also prioritize questions that require a quick answer depending on the time of submission of the question. Furthermore, the answer generation unit can determine the optimal order of answers taking into account the time of submission of the question. For example, the answer generation unit prioritizes questions that require a quick answer based on the time of submission of the question. In this way, the answer generation unit can provide more appropriate answers by determining the priority of answers based on the time of submission of the question.

[0093] The answer generation unit can change the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit prioritizes answering the most relevant question based on the relevance of the questions. The answer generation unit can also adjust the order of answers taking into account the relevance of the questions. Furthermore, the answer generation unit can determine the optimal order of answers depending on the relevance of the questions. For example, the answer generation unit prioritizes answering the most relevant question based on the relevance of the questions. In this way, the answer generation unit can adjust the order of answers based on the relevance of the questions, thereby enabling more appropriate answers.

[0094] The providing unit can estimate the user's emotion and change the presentation method based on the estimated user's emotion. For example, the providing unit can provide detailed information when the user is relaxed. The providing unit can also provide concise information when the user is in a hurry. Furthermore, the providing unit can provide visually stimulating information when the user is excited. For example, when the user is excited, the providing unit can provide visually stimulating information preferentially. This allows the providing unit to adjust the presentation method according to the user's emotion, thereby enabling more appropriate information to be provided. 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 such examples.

[0095] When providing information, the providing unit can select a delivery method by referring to the user's past usage history. For example, the providing unit preferentially suggests delivery methods that the user has frequently used in the past. The providing unit can also select the optimal delivery method from the user's past usage history. Furthermore, the providing unit can also provide related information based on the user's past usage history. For example, if the user has received information using a specific method in the past, the providing unit preferentially suggests that method. This allows the providing unit to select the optimal delivery method based on the user's past usage history, thereby enabling efficient information provision.

[0096] The providing unit can estimate the user's emotions and set a priority order for information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can prioritize providing information with high urgency. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information that can be provided quickly. For example, if the user is in a hurry, the providing unit prioritizes providing information that covers the main points. This allows the providing unit to determine the priority order of information to be provided according to the user's emotions, thereby enabling more appropriate information to be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The providing unit can select a providing method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. For example, if the user is using a smartwatch, the providing unit can prioritize providing highly visible information. This allows the providing unit to select the optimal providing method by taking into consideration the user's device information, thereby enabling efficient information provision. === Hard Collateral 1-1 === Each of the multiple elements, including the question receiving unit, information collecting unit, analysis unit, answer generating unit, and providing unit, described above, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the question receiving unit is realized by the control unit 46A of the smart device 14 and converts a user's question into text using voice recognition technology. The information collecting unit is realized by the specific processing unit 290 of the data processing device 12 and searches a library's book collection database or archive database. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and extracts related keywords and phrases. The answer generating unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer using a generation AI. The providing unit is realized by the control unit 46A of the smart device 14 and provides the generated answer to the user in voice or text format. Furthermore, the question receiving unit estimates the user's emotion using an emotion engine and adjusts the method of receiving the question depending on the emotion. === Hard Collateral 1-2 === Each of the multiple elements, including the question receiving unit, information collecting unit, analysis unit, answer generating unit, and providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the question receiving unit is realized by the control unit 46A of the smart glasses 214 and converts a user's question into text using voice recognition technology. The information collecting unit is realized by the specific processing unit 290 of the data processing device 12 and searches a library's book collection database or archive database. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and extracts related keywords and phrases. The answer generating unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer using a generation AI. The providing unit is realized by the control unit 46A of the smart glasses 214 and provides the generated answer to the user in voice or text format. Furthermore, the question receiving unit estimates the user's emotion using an emotion engine and adjusts the method of receiving the question depending on the emotion. === Hard Collateral 1-3 === Each of the multiple elements, including the question receiving unit, information collecting unit, analysis unit, answer generating unit, and providing unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the question receiving unit is realized by the control unit 46A of the headset-type terminal 314 and converts a user's question into text using voice recognition technology. The information collecting unit is realized by the specific processing unit 290 of the data processing device 12 and searches a library's book collection database or archive database. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and extracts related keywords and phrases. The answer generating unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer using a generation AI. The providing unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated answer to the user in voice or text format. Furthermore, the question receiving unit estimates the user's emotion using an emotion engine and adjusts the method of receiving the question depending on the emotion. === Hard Collateral 1-4 === Each of the multiple elements, including the question receiving unit, information collecting unit, analysis unit, answer generating unit, and providing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the question receiving unit is realized by the control unit 46A of the robot 414 and converts a user's question into text using voice recognition technology. The information collecting unit is realized by the specific processing unit 290 of the data processing device 12 and searches a library's book collection database or archive database. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and extracts related keywords and phrases. The answer generating unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer using a generation AI. The providing unit is realized by the control unit 46A of the robot 414 and provides the generated answer to the user in voice or text format. Furthermore, the question receiving unit estimates the user's emotion using an emotion engine and adjusts the method of receiving the question depending on the emotion.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The question reception unit can analyze the user's past question history and select a question reception method. For example, questions that the user has frequently asked in the past can be automatically displayed as candidates. The question reception unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. Furthermore, the question reception unit can predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the question reception unit can suggest questions related to a specific time period based on questions that the user has asked in the past. This allows the question reception unit to select the optimal question reception method based on the user's past question history, enabling efficient question reception.

[0100] The information collecting unit can select questions based on the user's current areas of interest. For example, it preferentially accepts questions related to topics in which the user is currently interested. The information collecting unit can also suggest related questions based on the user's recent search history. Furthermore, the information collecting unit can filter related questions based on events or seminars in which the user is participating. For example, the information collecting unit preferentially accepts questions related to events in which the user is participating. In this way, the information collecting unit can preferentially accept highly relevant questions by filtering questions based on the user's current areas of interest.

[0101] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information. For example, the analysis unit analyzes the interrelationships of the collected information and prioritizes the analysis of highly related information. The analysis unit can also improve the accuracy of the analysis based on the interrelationships of the collected information. Furthermore, the analysis unit can provide analysis results by taking into account the interrelationships of the collected information. For example, the analysis unit extracts mutually related information from the collected information and performs analysis based on that information. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships of the collected information.

[0102] The answer generation unit can use different answering algorithms depending on the category of the question. For example, a specialized answering algorithm is applied to a technical question. The answer generation unit can also apply a simple answering algorithm to a general question. Furthermore, the answer generation unit can select the optimal answering algorithm depending on the category of the question. For example, the answer generation unit applies the optimal answering algorithm based on the category of the question. In this way, the answer generation unit can provide a more appropriate answer by applying the optimal answering algorithm depending on the category of the question.

[0103] The providing unit can select a providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. For example, if the user is using a smartwatch, the providing unit can provide information with high visibility preferentially. This allows the providing unit to select the optimal providing method by taking into consideration the user's device information, thereby enabling efficient information provision.

[0104] The question acceptance unit can estimate the user's emotions and change the method of accepting questions based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly accept questions. For example, when a user inputs a question by voice, the question acceptance unit converts it into text using voice recognition technology and quickly accepts the question. In this way, the question acceptance unit can adjust the method of accepting questions according to the user's emotions, thereby enabling more appropriate question acceptance.

[0105] The information collecting unit can estimate the user's emotions and change the method of collecting information based on the estimated user's emotions. For example, when the user is relaxed, detailed information can be collected. Also, when the user is in a hurry, information that focuses on the main points can be collected preferentially. Furthermore, when the user is excited, the information collecting unit can collect visually stimulating information. For example, when the user is excited, the information collecting unit collects visually stimulating information preferentially. In this way, the information collecting unit can adjust the method of collecting information according to the user's emotions, thereby enabling more appropriate information collection.

[0106] The analysis unit can estimate the user's emotions and change the analysis method based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, an analysis that focuses on the main points can also be performed. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, if the user is excited, the analysis unit preferentially provides visually stimulating analysis results. This allows the analysis unit to adjust the analysis method according to the user's emotions, enabling more appropriate analysis.

[0107] The answer generation unit can estimate the user's emotions and change the way the answer is expressed based on the estimated user's emotions. For example, if the user is relaxed, a detailed answer can be provided. Also, if the user is in a hurry, a concise answer that gets to the point can be provided. Furthermore, the answer generation unit can provide a visually stimulating answer if the user is excited. For example, if the user is excited, the answer generation unit preferentially provides a visually stimulating answer. In this way, the answer generation unit can adjust the way the answer is expressed depending on the user's emotions, thereby enabling a more appropriate answer.

[0108] The providing unit can estimate the user's emotions and change the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, detailed information is provided. Furthermore, if the user is in a hurry, the providing unit can provide concise information. Furthermore, if the user is excited, the providing unit can provide visually stimulating information. For example, if the user is excited, the providing unit provides visually stimulating information preferentially. In this way, the providing unit can adjust the method of providing information according to the user's emotions, thereby enabling more appropriate information to be provided.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The question receiving unit receives a question from the user. The question can be received in text format or voice format. For example, if the user inputs a question by voice, it is converted into text using voice recognition technology. Also, if the user inputs a question in text format, the text can be received directly. Step 2: The information collection unit searches for related books and archives based on the question received by the question reception unit. For example, it searches the library's book collection database and archive database to collect related information. When a user asks a question such as "I'm looking for books on a specific topic," the unit analyzes the question and searches for related books and archives. Step 3: The analysis unit analyzes the information collected by the information collection unit and extracts related information. For example, the collected information is analyzed using text mining technology to extract related keywords and phrases. Information related to the user's question is extracted. Step 4: The answer generation unit generates an appropriate answer to the user's question based on the information analyzed by the analysis unit. For example, a generation AI is used to generate an answer to the user's question. If the user asks, "Please tell me the summary of this book," a summary of the book is generated. Step 5: The providing unit provides the answer generated by the answer generating unit to the user. For example, the generated answer can be provided in text format or audio format. If the user inputs a question by voice, the generated answer can be provided by audio. Also, if the user inputs a question by text, the generated answer can be provided by text.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the 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.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 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.

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

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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 question receiving unit that receives questions from users; an information collection unit that searches for related books and archives based on the question received by the question receiving unit; an analysis unit that analyzes the information collected by the information collection unit; a response generation unit that generates a response based on the information analyzed by the analysis unit; a providing unit that provides the answer generated by the answer generating unit to the user. A system characterized by:

2. The information collecting unit Search the library's book database or archive database 2. The system of claim 1.

3. The analysis unit Analyze the collected information and extract relevant information 2. The system of claim 1.

4. The answer generation unit Generate answers to user questions 2. The system of claim 1.

5. The providing unit Providing the generated answer to the user 2. The system of claim 1.

6. The analysis unit Extracting abstracts or keywords from papers 2. The system of claim 1.

7. The answer generation unit Provide information to users based on the extracted summary or keywords 2. The system of claim 1.

8. The question receiving unit Estimate the user's emotions and change the way questions are accepted based on the estimated user emotions.

2. The system of claim 1.

Images