system

Abstract

The system according to the embodiment aims to provide an appropriate answer to a survey subject input by a user in natural language. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives natural language input from a user. The analysis unit analyzes the natural language input received by the reception unit and extracts keywords. The identification unit identifies related information based on the keywords extracted by the analysis unit. The search unit searches for the related information identified by the identification unit. The provision unit generates an answer based on the information searched by the search unit and provides it 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] Conventional techniques have had the problem that it is difficult for users to obtain appropriate answers even when they input survey subjects in natural language.

[0005] The system according to the embodiment aims to provide an appropriate answer to a survey subject input by a user in natural language. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives natural language input from a user. The analysis unit analyzes the natural language input received by the reception unit and extracts keywords. The identification unit identifies related information based on the keywords extracted by the analysis unit. The search unit searches for the related information identified by the identification unit. The provision unit generates an answer based on the information searched by the search unit and provides it to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate answers to survey questions entered by a user in natural language. [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) An information retrieval system according to an embodiment of the present invention is a system in which, when a user inputs a subject of research in natural language, a generation AI understands the content, extracts relevant information from big data, generates a language, and provides an answer to the user. In this information retrieval system, a user inputs a subject of research in natural language, and a generation AI analyzes the input and extracts keywords and phrases related to the subject of research. The generation AI then searches for relevant information in big data and generates an appropriate answer. Finally, the generated answer is provided to the user. For example, a reception unit is required in which a user inputs the subject of research in natural language. Next, an analysis unit is required in which the generation AI analyzes the input and extracts keywords. Then, a search unit is required to search for relevant information from big data. Finally, a provision unit is required to provide the generated answer to the user. This enables the information retrieval system to efficiently collect information related to the subject of research by the user and provide an appropriate answer.

[0029] An information retrieval system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives natural language input from a user. When a user inputs a search target in natural language, the reception unit receives the input. For example, the reception unit can receive natural language input in the form of text input or voice input. The analysis unit analyzes the natural language input received by the reception unit and extracts keywords. The analysis unit uses a generation AI to analyze the natural language input using methods such as morphological analysis, grammatical analysis, and semantic analysis to extract keywords. For example, the analysis unit can extract frequently occurring words and words with high importance. The identification unit identifies related information based on the keywords extracted by the analysis unit. The identification unit uses the generation AI to identify related information based on the extracted keywords. For example, the identification unit can identify information from a database or information from the web. The search unit searches for the related information identified by the identification unit. The search unit uses the generation AI to search for the identified related information. For example, the search unit can search for related information using a search algorithm or a search scope. The providing unit generates an answer based on the information searched by the searching unit and provides it to the user. The providing unit uses a generation AI to generate an answer based on the searched information and provides it to the user. For example, the providing unit can generate an answer using a template or machine learning. This allows the information retrieval system according to the embodiment to efficiently search for related information for a search subject entered by a user in natural language and provide an appropriate answer.

[0030] The information retrieval system further includes a reception unit that analyzes the user's past input history and suggests an optimal input method. The reception unit analyzes the user's past input history and suggests the optimal input method. For example, the reception unit prioritizes suggesting input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can also suggest similar input methods based on the content the user has previously input. This improves user convenience by suggesting an optimal input method based on the user's past input history. The input history is analyzed, for example, using analysis of past input data and input patterns. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input data to a generation AI and have the generation AI suggest an optimal input method.

[0031] The information retrieval system further includes a reception unit that filters input content based on the user's current areas of interest when natural language input is performed. The reception unit filters the input content based on the user's current areas of interest when natural language input is performed. For example, the reception unit preferentially receives input content related to keywords recently searched by the user. The reception unit can also filter the input content based on topics in which the user has previously shown interest. The reception unit can also analyze the user's social media activities and preferentially receive related input content. In this way, by filtering the input content based on the user's areas of interest, highly relevant information can be preferentially received. The areas of interest are identified using, for example, past search history or social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to identify the user's areas of interest.

[0032] The information retrieval system further includes a reception unit that, when inputting natural language, prioritizes accepting highly relevant inputs in consideration of the user's geographical location information. The reception unit, when inputting natural language, prioritizes accepting highly relevant inputs in consideration of the user's geographical location information. For example, when a user inputs information related to their current location, the reception unit prioritizes accepting the information. Furthermore, when a user inputs information related to a specific region, the reception unit can also prioritize accepting information related to the region. Furthermore, when a user is traveling, the reception unit can also prioritize accepting information related to the user's travel destination. This allows highly relevant information to be prioritized by considering the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's GPS data to a generation AI and cause the generation AI to identify highly relevant information.

[0033] The information retrieval system further includes a reception unit that analyzes the user's social media activity and accepts related inputs when natural language input is received. The reception unit analyzes the user's social media activity and accepts related inputs when natural language input is received. For example, the reception unit may preferentially accept related inputs based on content recently posted by the user. The reception unit may also preferentially accept related inputs based on the activity of accounts the user follows. The reception unit may also preferentially accept related inputs based on the activity of groups the user participates in. This allows the user's social media activity to be analyzed and highly relevant information to be preferentially accepted. The analysis of social media activity is performed using, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's social media data to a generation AI and cause the generation AI to identify related inputs.

[0034] The information retrieval system further includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the input content during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the input content during analysis. For example, the analysis unit performs a detailed analysis of important input content. The analysis unit can also perform a concise analysis of general input content. Furthermore, the analysis unit can also perform a quick analysis of input content that is highly urgent. In this way, by adjusting the level of detail of the analysis based on the importance of the input content, detailed analysis of important information can be performed. The importance evaluation is performed, for example, using a user specification or an automatic evaluation by the system. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI evaluate the importance.

[0035] Furthermore, the information retrieval system includes an analysis unit that applies different analysis algorithms depending on the category of the input content during analysis. The analysis unit applies different analysis algorithms depending on the category of the input content during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical content. The analysis unit can also apply a standard analysis algorithm to general content. The analysis unit can also apply a rapid analysis algorithm to content with high urgency. This allows for applying an appropriate analysis algorithm depending on the category of the input content, thereby improving the accuracy of the analysis. Categories are classified using, for example, technical categories or business categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input input data to a generation AI and have the generation AI classify the categories and apply the analysis algorithm.

[0036] The information retrieval system further includes an analysis unit that, during analysis, determines the analysis priority based on the submission time of the input content. The analysis unit, during analysis, determines the analysis priority based on the submission time of the input content. For example, the analysis unit prioritizes analysis of recently submitted input content. The analysis unit can also prioritize analysis of input content with high urgency. The analysis unit can also determine the analysis priority based on a deadline specified by the user. In this way, by determining the analysis priority based on the submission time of the input content, it is possible to prioritize analysis of information with high urgency. The submission time is evaluated using, for example, the submission date and time or the submission frequency. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the input data to a generation AI and have the generation AI evaluate the submission time and determine the priority.

[0037] The information retrieval system further includes an analysis unit that adjusts the order of analysis based on the relevance of the input content during analysis. The analysis unit adjusts the order of analysis based on the relevance of the input content during analysis. For example, the analysis unit prioritizes analysis of highly relevant input content. The analysis unit can also postpone analysis of less relevant input content. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. This allows highly relevant information to be analyzed preferentially by adjusting the order of analysis based on the relevance of the input content. The relevance is evaluated using, for example, common keywords or related topics. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input input data to a generation AI and have the generation AI evaluate the relevance and adjust the analysis order.

[0038] The information retrieval system further includes an identification unit that, during identification, improves the accuracy of identification by taking into account the interrelationships of input contents. The identification unit, during identification, improves the accuracy of identification by taking into account the interrelationships of input contents. For example, the identification unit analyzes the interrelationships of input contents to identify highly relevant information. The identification unit can also improve the accuracy of identification by taking into account the interrelationships of input contents. Furthermore, the identification unit can provide optimal identification results based on the interrelationships of input contents. In this way, the accuracy of identification can be improved by taking into account the interrelationships of input contents. The evaluation of the interrelationships is performed using, for example, common keywords or related topics. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input input data to a generation AI and cause the generation AI to evaluate the interrelationships and improve the accuracy of identification.

[0039] Furthermore, the information retrieval system includes an identification unit that performs identification by taking into account attribute information of the person who submitted the input content. The identification unit performs identification by taking into account attribute information of the person who submitted the input content. For example, the identification unit improves the accuracy of identification by taking into account the submitter's occupation and field of expertise. The identification unit can also improve the accuracy of identification by referring to the submitter's past input history. Furthermore, the identification unit can provide optimal identification results based on the submitter's attribute information. This allows the accuracy of identification to be improved by taking into account the submitter's attribute information. The attribute information is acquired using, for example, age, gender, occupation, etc. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can input the submitter's attribute data into a generation AI and have the generation AI evaluate and identify the attribute information.

[0040] The information search system further includes an identification unit that performs identification taking into account the geographical distribution of the input content during identification. The identification unit performs identification taking into account the geographical distribution of the input content during identification. For example, the identification unit analyzes the geographical distribution of the input content to identify highly relevant information. The identification unit can also improve the accuracy of identification by taking the geographical distribution into account. Furthermore, the identification unit can provide optimal identification results based on the geographical distribution. This makes it possible to identify highly relevant information by taking the geographical distribution of the input content into account. The evaluation of the geographical distribution is performed using, for example, regional data or geographical trends. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input input data to a generation AI and have the generation AI evaluate and identify the geographical distribution.

[0041] Furthermore, the information retrieval system includes an identification unit that, during identification, refers to related literature of the input content to improve the accuracy of identification. The identification unit, during identification, refers to related literature of the input content to improve the accuracy of identification. For example, the identification unit refers to related literature to improve the accuracy of identification. The identification unit can also provide optimal identification results based on the related literature. Furthermore, the identification unit can also improve the accuracy of identification by taking related literature into consideration. In this way, the accuracy of identification can be improved by referring to related literature. The reference to related literature is performed, for example, by searching for literature from a database or analyzing cited literature. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input input data to a generation AI and cause the generation AI to refer to related literature and improve the accuracy of identification.

[0042] Furthermore, the information retrieval system includes a search unit that improves search accuracy by taking into account interrelationships between input contents during a search. The search unit improves search accuracy by taking into account interrelationships between input contents during a search. For example, the search unit analyzes interrelationships between input contents and searches for highly relevant information. The search unit can also improve search accuracy by taking into account interrelationships between input contents. Furthermore, the search unit can provide optimal search results based on interrelationships between input contents. In this way, by taking interrelationships between input contents into account, search accuracy can be improved. Interrelationships are evaluated using, for example, common keywords or related topics. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input input data to a generation AI and cause the generation AI to evaluate interrelationships and improve search accuracy.

[0043] The information retrieval system further includes a search unit that performs a search taking into account the geographical distribution of the input content. The search unit performs a search taking into account the geographical distribution of the input content. For example, the search unit analyzes the geographical distribution of the input content and searches for highly relevant information. The search unit can also improve search accuracy by taking the geographical distribution into account. Furthermore, the search unit can provide optimal search results based on the geographical distribution. This makes it possible to search for highly relevant information by taking the geographical distribution of the input content into account. The evaluation of the geographical distribution is performed, for example, using regional data or geographical trends. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input input data to a generation AI and cause the generation AI to evaluate the geographical distribution and perform a search.

[0044] Furthermore, the information retrieval system includes a search unit that, during a search, refers to literature related to the input content to improve search accuracy. The search unit, during a search, refers to literature related to the input content to improve search accuracy. For example, the search unit refers to related literature to improve search accuracy. The search unit can also provide optimal search results based on the related literature. Furthermore, the search unit can also improve search accuracy by taking related literature into consideration. In this way, the search accuracy can be improved by referring to related literature. The reference to related literature is performed, for example, by searching for literature from a database or analyzing cited literature. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input input data to a generation AI and cause the generation AI to refer to related literature and improve search accuracy.

[0045] The information search system further includes a providing unit that adjusts the level of detail of the provided answer based on the importance of the answer when the answer is provided. The providing unit adjusts the level of detail of the provided answer based on the importance of the answer when the answer is provided. For example, the providing unit provides detailed information for an important answer. The providing unit can also provide concise information for a general answer. The providing unit can also quickly provide information for an answer with high urgency. In this way, by adjusting the level of detail of the provided answer based on the importance of the answer, a detailed answer can be provided for important information. The importance is evaluated, for example, using a user specification or an automatic evaluation by the system. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the importance and adjust the level of detail.

[0046] The information search system further includes a providing unit that applies different providing algorithms depending on the category of the answer when providing the answer. The providing unit applies different providing algorithms depending on the category of the answer when providing the answer. For example, the providing unit applies a specialized providing algorithm to technical answers. The providing unit can also apply a standard providing algorithm to general answers. The providing unit can also apply a rapid providing algorithm to highly urgent answers. This allows for applying an appropriate providing algorithm depending on the category of the answer, thereby improving the accuracy of the answer provided. The categories are classified using, for example, technical categories or business categories. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input answer data to a generation AI and cause the generation AI to classify the categories and apply the providing algorithm.

[0047] The information search system further includes a providing unit that, at the time of providing, determines the priority of providing based on the time of submission of the answer. The providing unit, at the time of providing, determines the priority of providing based on the time of submission of the answer. For example, the providing unit prioritizes providing the most recently submitted answer. The providing unit can also prioritize providing answers with high urgency. Furthermore, the providing unit can also determine the priority of providing based on a deadline specified by the user. In this way, by determining the priority of providing based on the time of submission of the answer, it is possible to prioritize the provision of information with high urgency. The evaluation of the submission time is performed using, for example, the submission date and time or the submission frequency. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the submission time and determine the priority.

[0048] The information search system further includes a providing unit that adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. The providing unit adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. For example, the providing unit prioritizes providing highly relevant answers. The providing unit can also postpone less relevant answers. The providing unit can also adjust the order of answers to be provided based on the relevance specified by the user. In this way, by adjusting the order of answers to be provided based on the relevance of the answers, highly relevant information can be provided preferentially. The relevance is evaluated using, for example, common keywords or related topics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the relevance and adjust the order of answers to be provided.

[0049] The information search system further includes a providing unit that adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. The providing unit adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. For example, the providing unit prioritizes providing highly relevant answers. The providing unit can also postpone less relevant answers. The providing unit can also adjust the order of answers to be provided based on the relevance specified by the user. In this way, by adjusting the order of answers to be provided based on the relevance of the answers, highly relevant information can be provided preferentially. The relevance is evaluated using, for example, common keywords or related topics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the relevance and adjust the order of answers to be provided.

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

[0051] The information retrieval system can analyze a user's past search history and prioritize providing relevant information based on the user's previous searches. For example, if a user has frequently searched for a particular topic in the past, new information related to that topic will be displayed preferentially. It can also suggest new related keywords based on the keywords the user has previously searched for. Furthermore, it can automatically add news and articles on related topics to the feed based on the user's past searches. This makes it possible to provide more relevant information by utilizing the user's past search history.

[0052] The information search system can prioritize providing relevant local information by taking into account the user's current geographic location information. For example, if the user is in a specific area, news and event information related to that area can be displayed preferentially. If the user is traveling, tourist information and restaurant reviews related to the user's destination can be provided. Furthermore, if the user is searching for information about a specific area, detailed maps and traffic information related to that area can be provided. In this way, the information search system can utilize the user's geographic location information to provide more relevant information.

[0053] The information retrieval system can analyze a user's social media activity and provide relevant information preferentially. For example, it can prioritize information related to topics that the user has recently shown interest in on social media. It can also provide relevant news and articles based on the content posted by accounts the user follows. It can also add relevant information to the feed based on the activity of groups the user participates in. This makes it possible to utilize the user's social media activity to provide more relevant information.

[0054] Information retrieval systems can automatically notify users of new related information based on their past search history. For example, if a user has frequently searched for a particular topic in the past, they can automatically notify them of new articles or news related to that topic. They can also suggest new related keywords based on keywords the user has searched for in the past. Furthermore, they can notify users of forums and discussions on related topics based on their past searches. This makes it possible to provide highly relevant information by utilizing the user's past search history.

[0055] Information retrieval systems can analyze a user's current areas of interest and prioritize providing relevant information. For example, they can prioritize displaying new related information based on keywords recently searched by the user. They can also provide relevant news and articles based on topics in which the user has shown interest in the past. They can also analyze a user's social media activity and add related information to the feed. This allows them to utilize the user's areas of interest to provide more relevant information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives natural language input from the user. When the user inputs a survey subject in natural language, the reception unit receives the input. For example, the reception unit can receive natural language input in the form of text input, voice input, or the like. Step 2: The analysis unit analyzes the natural language input received by the reception unit and extracts keywords. Using generative AI, the analysis unit analyzes the natural language input using methods such as morphological analysis, grammatical analysis, and semantic analysis to extract keywords. For example, the analysis unit can extract frequently occurring words and words with high importance. Step 3: The identification unit identifies related information based on the keywords extracted by the analysis unit. The identification unit uses a generation AI to identify related information based on the extracted keywords. For example, the identification unit can identify information from a database or information from the web. Step 4: The search unit searches for the related information identified by the identification unit. The search unit uses the generation AI to search for the identified related information. For example, the search unit can search for the related information using a search algorithm or a search scope. Step 5: The providing unit generates an answer based on the information searched by the search unit and provides it to the user. The providing unit uses a generation AI to generate an answer based on the searched information and provides it to the user. For example, the providing unit can generate answers based on templates or using machine learning.

[0058] (Example 2) An information retrieval system according to an embodiment of the present invention is a system in which, when a user inputs a subject of research in natural language, a generation AI understands the content, extracts relevant information from big data, generates a language, and provides an answer to the user. In this information retrieval system, a user inputs a subject of research in natural language, and a generation AI analyzes the input and extracts keywords and phrases related to the subject of research. The generation AI then searches for relevant information in big data and generates an appropriate answer. Finally, the generated answer is provided to the user. For example, a reception unit is required in which a user inputs the subject of research in natural language. Next, an analysis unit is required in which the generation AI analyzes the input and extracts keywords. Then, a search unit is required to search for relevant information from big data. Finally, a provision unit is required to provide the generated answer to the user. This enables the information retrieval system to efficiently collect information related to the subject of research by the user and provide an appropriate answer.

[0059] An information retrieval system according to an embodiment includes a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives natural language input from a user. When a user inputs a search target in natural language, the reception unit receives the input. For example, the reception unit can receive natural language input in the form of text input or voice input. The analysis unit analyzes the natural language input received by the reception unit and extracts keywords. The analysis unit uses a generation AI to analyze the natural language input using methods such as morphological analysis, grammatical analysis, and semantic analysis to extract keywords. For example, the analysis unit can extract frequently occurring words and words with high importance. The identification unit identifies related information based on the keywords extracted by the analysis unit. The identification unit uses the generation AI to identify related information based on the extracted keywords. For example, the identification unit can identify information from a database or information from the web. The search unit searches for the related information identified by the identification unit. The search unit uses the generation AI to search for the identified related information. For example, the search unit can search for related information using a search algorithm or a search scope. The providing unit generates an answer based on the information searched by the searching unit and provides it to the user. The providing unit uses a generation AI to generate an answer based on the searched information and provides it to the user. For example, the providing unit can generate an answer using a template or machine learning. This allows the information retrieval system according to the embodiment to efficiently search for related information for a search subject entered by a user in natural language and provide an appropriate answer.

[0060] The information retrieval system further includes a reception unit that estimates a user's emotion and adjusts the timing of input reception based on the estimated user emotion. The reception unit estimates the user's emotion and adjusts the timing of input reception based on the estimated user emotion. For example, if the user is stressed, the reception unit delays the timing of input reception to allow the user to relax. Furthermore, if the user is relaxed, the reception unit can also accelerate the timing of input reception to promote smooth operation. Furthermore, if the user is in a hurry, the reception unit can immediately accept input and provide a prompt response. Thus, by adjusting the timing of input reception according to the user's emotion, the user's stress can be reduced and smooth operation can be promoted. The emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input the user's facial expression data into a generation AI and cause the generation AI to perform emotion estimation.

[0061] The information retrieval system further includes a reception unit that analyzes the user's past input history and suggests an optimal input method. The reception unit analyzes the user's past input history and suggests the optimal input method. For example, the reception unit prioritizes suggesting input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can also suggest similar input methods based on the content the user has previously input. This improves user convenience by suggesting an optimal input method based on the user's past input history. The input history is analyzed, for example, using analysis of past input data and input patterns. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input data to a generation AI and have the generation AI suggest an optimal input method.

[0062] The information retrieval system further includes a reception unit that filters input content based on the user's current areas of interest when natural language input is performed. The reception unit filters the input content based on the user's current areas of interest when natural language input is performed. For example, the reception unit preferentially receives input content related to keywords recently searched by the user. The reception unit can also filter the input content based on topics in which the user has previously shown interest. The reception unit can also analyze the user's social media activities and preferentially receive related input content. In this way, by filtering the input content based on the user's areas of interest, highly relevant information can be preferentially received. The areas of interest are identified using, for example, past search history or social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to identify the user's areas of interest.

[0063] The information retrieval system further includes a reception unit that estimates a user's emotion and prioritizes input content based on the estimated user emotion. The reception unit estimates the user's emotion and prioritizes the input content based on the estimated user emotion. For example, if the user is stressed, the reception unit may prioritize important input content. Furthermore, if the user is relaxed, the reception unit may prioritize detailed input content. Furthermore, if the user is in a hurry, the reception unit may prioritize concise input content. This allows important information to be prioritized by prioritizing the input content according to the user's emotion. The emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0064] The information retrieval system further includes a reception unit that, when inputting natural language, prioritizes accepting highly relevant inputs in consideration of the user's geographical location information. The reception unit, when inputting natural language, prioritizes accepting highly relevant inputs in consideration of the user's geographical location information. For example, when a user inputs information related to their current location, the reception unit prioritizes accepting the information. Furthermore, when a user inputs information related to a specific region, the reception unit can also prioritize accepting information related to the region. Furthermore, when a user is traveling, the reception unit can also prioritize accepting information related to the user's travel destination. This allows highly relevant information to be prioritized by considering the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's GPS data to a generation AI and cause the generation AI to identify highly relevant information.

[0065] The information retrieval system further includes a reception unit that analyzes the user's social media activity and accepts related inputs when natural language input is received. The reception unit analyzes the user's social media activity and accepts related inputs when natural language input is received. For example, the reception unit may preferentially accept related inputs based on content recently posted by the user. The reception unit may also preferentially accept related inputs based on the activity of accounts the user follows. The reception unit may also preferentially accept related inputs based on the activity of groups the user participates in. This allows the user's social media activity to be analyzed and highly relevant information to be preferentially accepted. The analysis of social media activity is performed using, for example, the content of posts, the number of likes, the number of followers, etc. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's social media data to a generation AI and cause the generation AI to identify related inputs.

[0066] The information retrieval system further includes an analysis unit that estimates a user's emotions and adjusts the presentation of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide analysis results that are concise. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0067] The information retrieval system further includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the input content during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the input content during analysis. For example, the analysis unit performs a detailed analysis of important input content. The analysis unit can also perform a concise analysis of general input content. Furthermore, the analysis unit can also perform a quick analysis of input content that is highly urgent. In this way, by adjusting the level of detail of the analysis based on the importance of the input content, detailed analysis of important information can be performed. The importance evaluation is performed, for example, using a user specification or an automatic evaluation by the system. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI evaluate the importance.

[0068] Furthermore, the information retrieval system includes an analysis unit that applies different analysis algorithms depending on the category of the input content during analysis. The analysis unit applies different analysis algorithms depending on the category of the input content during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical content. The analysis unit can also apply a standard analysis algorithm to general content. The analysis unit can also apply a rapid analysis algorithm to content with high urgency. This allows for applying an appropriate analysis algorithm depending on the category of the input content, thereby improving the accuracy of the analysis. Categories are classified using, for example, technical categories or business categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input input data to a generation AI and have the generation AI classify the categories and apply the analysis algorithm.

[0069] The information retrieval system further includes an analysis unit that estimates a user's emotions and adjusts the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result of an appropriate length for the user. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0070] The information retrieval system further includes an analysis unit that, during analysis, determines the analysis priority based on the submission time of the input content. The analysis unit, during analysis, determines the analysis priority based on the submission time of the input content. For example, the analysis unit prioritizes analysis of recently submitted input content. The analysis unit can also prioritize analysis of input content with high urgency. The analysis unit can also determine the analysis priority based on a deadline specified by the user. In this way, by determining the analysis priority based on the submission time of the input content, it is possible to prioritize analysis of information with high urgency. The submission time is evaluated using, for example, the submission date and time or the submission frequency. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the input data to a generation AI and have the generation AI evaluate the submission time and determine the priority.

[0071] The information retrieval system further includes an analysis unit that adjusts the order of analysis based on the relevance of the input content during analysis. The analysis unit adjusts the order of analysis based on the relevance of the input content during analysis. For example, the analysis unit prioritizes analysis of highly relevant input content. The analysis unit can also postpone analysis of less relevant input content. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. This allows highly relevant information to be analyzed preferentially by adjusting the order of analysis based on the relevance of the input content. The relevance is evaluated using, for example, common keywords or related topics. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input input data to a generation AI and have the generation AI evaluate the relevance and adjust the analysis order.

[0072] The information retrieval system further includes an identification unit that estimates a user's emotions and adjusts the specific criteria based on the estimated user emotions. The identification unit estimates the user's emotions and adjusts the specific criteria based on the estimated user emotions. For example, if the user is nervous, the identification unit provides simple, highly visible specific criteria. If the user is relaxed, the identification unit can also provide detailed specific criteria. If the user is in a hurry, the identification unit can also provide specific criteria that are concise. By adjusting the specific criteria according to the user's emotions, it is possible to provide specific criteria that are easy for the user to understand. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or without AI. For example, the identification unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0073] The information retrieval system further includes an identification unit that, during identification, improves the accuracy of identification by taking into account the interrelationships of input contents. The identification unit, during identification, improves the accuracy of identification by taking into account the interrelationships of input contents. For example, the identification unit analyzes the interrelationships of input contents to identify highly relevant information. The identification unit can also improve the accuracy of identification by taking into account the interrelationships of input contents. Furthermore, the identification unit can provide optimal identification results based on the interrelationships of input contents. In this way, the accuracy of identification can be improved by taking into account the interrelationships of input contents. The evaluation of the interrelationships is performed using, for example, common keywords or related topics. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input input data to a generation AI and cause the generation AI to evaluate the interrelationships and improve the accuracy of identification.

[0074] Furthermore, the information retrieval system includes an identification unit that performs identification by taking into account attribute information of the person who submitted the input content. The identification unit performs identification by taking into account attribute information of the person who submitted the input content. For example, the identification unit improves the accuracy of identification by taking into account the submitter's occupation and field of expertise. The identification unit can also improve the accuracy of identification by referring to the submitter's past input history. Furthermore, the identification unit can provide optimal identification results based on the submitter's attribute information. This allows the accuracy of identification to be improved by taking into account the submitter's attribute information. The attribute information is acquired using, for example, age, gender, occupation, etc. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can input the submitter's attribute data into a generation AI and have the generation AI evaluate and identify the attribute information.

[0075] The information search system further includes a determination unit that estimates a user's emotion and adjusts the order in which specific results are displayed based on the estimated user emotion. The determination unit estimates the user's emotion and adjusts the order in which specific results are displayed based on the estimated user emotion. For example, if the user is nervous, the determination unit may prioritize displaying important specific results. Furthermore, if the user is relaxed, the determination unit may prioritize displaying detailed specific results. Furthermore, if the user is in a hurry, the determination unit may prioritize displaying concise specific results. Thus, by adjusting the order in which specific results are displayed based on the user's emotion, information important to the user can be prioritized. The emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0076] The information search system further includes an identification unit that performs identification taking into account the geographical distribution of the input content during identification. The identification unit performs identification taking into account the geographical distribution of the input content during identification. For example, the identification unit analyzes the geographical distribution of the input content to identify highly relevant information. The identification unit can also improve the accuracy of identification by taking the geographical distribution into account. Furthermore, the identification unit can provide optimal identification results based on the geographical distribution. This makes it possible to identify highly relevant information by taking the geographical distribution of the input content into account. The evaluation of the geographical distribution is performed using, for example, regional data or geographical trends. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input input data to a generation AI and have the generation AI evaluate and identify the geographical distribution.

[0077] Furthermore, the information retrieval system includes an identification unit that, during identification, refers to related literature of the input content to improve the accuracy of identification. The identification unit, during identification, refers to related literature of the input content to improve the accuracy of identification. For example, the identification unit refers to related literature to improve the accuracy of identification. The identification unit can also provide optimal identification results based on the related literature. Furthermore, the identification unit can also improve the accuracy of identification by taking related literature into consideration. In this way, the accuracy of identification can be improved by referring to related literature. The reference to related literature is performed, for example, by searching for literature from a database or analyzing cited literature. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input input data to a generation AI and cause the generation AI to refer to related literature and improve the accuracy of identification.

[0078] The information retrieval system further includes a search unit that estimates a user's emotions and adjusts search criteria based on the estimated user emotions. The search unit estimates the user's emotions and adjusts the search criteria based on the estimated user emotions. For example, if the user is nervous, the search unit provides simple, highly visible search criteria. Furthermore, if the user is relaxed, the search unit can provide detailed search criteria. Furthermore, if the user is in a hurry, the search unit can provide search criteria that are concise. By adjusting the search criteria according to the user's emotions, it is possible to provide search criteria that are easy for the user to understand. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or without AI. For example, the search unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0079] Furthermore, the information retrieval system includes a search unit that improves search accuracy by taking into account interrelationships between input contents during a search. The search unit improves search accuracy by taking into account interrelationships between input contents during a search. For example, the search unit analyzes interrelationships between input contents and searches for highly relevant information. The search unit can also improve search accuracy by taking into account interrelationships between input contents. Furthermore, the search unit can provide optimal search results based on interrelationships between input contents. In this way, by taking interrelationships between input contents into account, search accuracy can be improved. Interrelationships are evaluated using, for example, common keywords or related topics. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input input data to a generation AI and cause the generation AI to evaluate interrelationships and improve search accuracy.

[0080] The information retrieval system further includes a search unit that estimates a user's emotions and adjusts the order in which search results are displayed based on the estimated user emotions. The search unit estimates the user's emotions and adjusts the order in which search results are displayed based on the estimated user emotions. For example, if the user is nervous, the search unit may prioritize displaying important search results. Furthermore, if the user is relaxed, the search unit may prioritize displaying detailed search results. Furthermore, if the user is in a hurry, the search unit may prioritize displaying concise search results. This allows information important to the user to be prioritized by adjusting the order in which search results are displayed based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or without AI. For example, the search unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0081] The information retrieval system further includes a search unit that performs a search taking into account the geographical distribution of the input content. The search unit performs a search taking into account the geographical distribution of the input content. For example, the search unit analyzes the geographical distribution of the input content and searches for highly relevant information. The search unit can also improve search accuracy by taking the geographical distribution into account. Furthermore, the search unit can provide optimal search results based on the geographical distribution. This makes it possible to search for highly relevant information by taking the geographical distribution of the input content into account. The evaluation of the geographical distribution is performed, for example, using regional data or geographical trends. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input input data to a generation AI and cause the generation AI to evaluate the geographical distribution and perform a search.

[0082] Furthermore, the information retrieval system includes a search unit that, during a search, refers to literature related to the input content to improve search accuracy. The search unit, during a search, refers to literature related to the input content to improve search accuracy. For example, the search unit refers to related literature to improve search accuracy. The search unit can also provide optimal search results based on the related literature. Furthermore, the search unit can also improve search accuracy by taking related literature into consideration. In this way, the search accuracy can be improved by referring to related literature. The reference to related literature is performed, for example, by searching for literature from a database or analyzing cited literature. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input input data to a generation AI and cause the generation AI to refer to related literature and improve search accuracy.

[0083] The information search system further includes a providing unit that estimates a user's emotion and adjusts the expression of the answer to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the expression of the answer to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit provides a simple, highly visible answer. Furthermore, if the user is relaxed, the providing unit can provide a detailed answer. Furthermore, if the user is in a hurry, the providing unit can provide an answer that focuses on the main points. This allows the user to be provided with an answer that is easy to understand by adjusting the expression of the answer according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0084] The information search system further includes a providing unit that adjusts the level of detail of the provided answer based on the importance of the answer when the answer is provided. The providing unit adjusts the level of detail of the provided answer based on the importance of the answer when the answer is provided. For example, the providing unit provides detailed information for an important answer. The providing unit can also provide concise information for a general answer. The providing unit can also quickly provide information for an answer with high urgency. In this way, by adjusting the level of detail of the provided answer based on the importance of the answer, a detailed answer can be provided for important information. The importance is evaluated, for example, using a user specification or an automatic evaluation by the system. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the importance and adjust the level of detail.

[0085] The information search system further includes a providing unit that applies different providing algorithms depending on the category of the answer when providing the answer. The providing unit applies different providing algorithms depending on the category of the answer when providing the answer. For example, the providing unit applies a specialized providing algorithm to technical answers. The providing unit can also apply a standard providing algorithm to general answers. The providing unit can also apply a rapid providing algorithm to highly urgent answers. This allows for applying an appropriate providing algorithm depending on the category of the answer, thereby improving the accuracy of the answer provided. The categories are classified using, for example, technical categories or business categories. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input answer data to a generation AI and cause the generation AI to classify the categories and apply the providing algorithm.

[0086] The information retrieval system further includes a providing unit that estimates a user's emotion and adjusts the length of the answer to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the length of the answer to be provided based on the estimated user's emotion. For example, if the user is in a hurry, the providing unit provides a short, to-the-point answer. Furthermore, if the user is relaxed, the providing unit can also provide a detailed answer. Furthermore, if the user is excited, the providing unit can also provide a visually stimulating answer. By adjusting the length of the answer according to the user's emotion, an answer of an appropriate length for the user can be provided. The emotion estimation is performed using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit may input the user's facial expression data into a generation AI and cause the generation AI to estimate the emotion.

[0087] The information search system further includes a providing unit that, at the time of providing, determines the priority of providing based on the time of submission of the answer. The providing unit, at the time of providing, determines the priority of providing based on the time of submission of the answer. For example, the providing unit prioritizes providing the most recently submitted answer. The providing unit can also prioritize providing answers with high urgency. Furthermore, the providing unit can also determine the priority of providing based on a deadline specified by the user. In this way, by determining the priority of providing based on the time of submission of the answer, it is possible to prioritize the provision of information with high urgency. The evaluation of the submission time is performed using, for example, the submission date and time or the submission frequency. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the submission time and determine the priority.

[0088] The information search system further includes a providing unit that adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. The providing unit adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. For example, the providing unit prioritizes providing highly relevant answers. The providing unit can also postpone less relevant answers. The providing unit can also adjust the order of answers to be provided based on the relevance specified by the user. In this way, by adjusting the order of answers to be provided based on the relevance of the answers, highly relevant information can be provided preferentially. The relevance is evaluated using, for example, common keywords or related topics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the relevance and adjust the order of answers to be provided.

[0089] The information search system further includes a providing unit that adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. The providing unit adjusts the order of answers to be provided based on the relevance of the answers when the answers are provided. For example, the providing unit prioritizes providing highly relevant answers. The providing unit can also postpone less relevant answers. The providing unit can also adjust the order of answers to be provided based on the relevance specified by the user. In this way, by adjusting the order of answers to be provided based on the relevance of the answers, highly relevant information can be provided preferentially. The relevance is evaluated using, for example, common keywords or related topics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input answer data to a generation AI and cause the generation AI to evaluate the relevance and adjust the order of answers to be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a natural language input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input natural language and extracts keywords. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for related information from big data. The provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the generated answer to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a natural language input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input natural language and extracts keywords. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for related information from big data. The provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the generated answer to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives natural language input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input natural language and extracts keywords. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for related information from big data. The provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides the generated answer to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives natural language input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input natural language and extracts keywords. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for related information from big data. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides the generated answer to the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The information retrieval system can analyze a user's past search history and prioritize providing relevant information based on the user's previous searches. For example, if a user has frequently searched for a particular topic in the past, new information related to that topic will be displayed preferentially. It can also suggest new related keywords based on the keywords the user has previously searched for. Furthermore, it can automatically add news and articles on related topics to the feed based on the user's past searches. This makes it possible to provide more relevant information by utilizing the user's past search history.

[0092] The information retrieval system can estimate the user's emotions and adjust the way search results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the system can display search results in a simple, highly visible format. If the user is relaxed, the system can provide detailed search results. Furthermore, if the user is in a hurry, the system can display concise search results that focus on the main points. In this way, by adjusting the way search results are displayed according to the user's emotions, it is possible to provide the user with the most appropriate information.

[0093] The information search system can prioritize providing relevant local information by taking into account the user's current geographic location information. For example, if the user is in a specific area, news and event information related to that area can be displayed preferentially. If the user is traveling, tourist information and restaurant reviews related to the user's destination can be provided. Furthermore, if the user is searching for information about a specific area, detailed maps and traffic information related to that area can be provided. In this way, the information search system can utilize the user's geographic location information to provide more relevant information.

[0094] The information retrieval system can estimate the user's emotions and filter search results based on the estimated user emotions. For example, if the user is feeling stressed, search results with positive content can be displayed preferentially. If the user is relaxed, search results containing detailed information can be provided. Furthermore, if the user is in a hurry, search results that are concise and to the point can be displayed. In this way, by filtering search results according to the user's emotions, it is possible to provide the most appropriate information for the user.

[0095] The information retrieval system can analyze a user's social media activity and provide relevant information preferentially. For example, it can prioritize information related to topics that the user has recently shown interest in on social media. It can also provide relevant news and articles based on the content posted by accounts the user follows. It can also add relevant information to the feed based on the activity of groups the user participates in. This makes it possible to utilize the user's social media activity to provide more relevant information.

[0096] The information retrieval system can estimate the user's emotions and adjust the display order of search results based on the estimated user's emotions. For example, if the user is nervous, important search results can be displayed preferentially. If the user is relaxed, detailed search results can be displayed preferentially. Furthermore, if the user is in a hurry, concise search results can be displayed preferentially. In this way, by adjusting the display order of search results according to the user's emotions, it is possible to provide information that is important to the user preferentially.

[0097] Information retrieval systems can automatically notify users of new related information based on their past search history. For example, if a user has frequently searched for a particular topic in the past, they can automatically notify them of new articles or news related to that topic. They can also suggest new related keywords based on keywords the user has searched for in the past. Furthermore, they can notify users of forums and discussions on related topics based on their past searches. This makes it possible to provide highly relevant information by utilizing the user's past search history.

[0098] The information retrieval system can estimate the user's emotions and adjust the display format of search results based on the estimated user's emotions. For example, if the user is feeling stressed, the system can display search results in a simple, highly visible format. If the user is relaxed, the system can provide detailed search results. Furthermore, if the user is in a hurry, the system can display concise search results that focus on the main points. In this way, by adjusting the display format of search results according to the user's emotions, the system can provide the user with the most appropriate information.

[0099] Information retrieval systems can analyze a user's current areas of interest and prioritize providing relevant information. For example, they can prioritize displaying new related information based on keywords recently searched by the user. They can also provide relevant news and articles based on topics in which the user has shown interest in the past. They can also analyze a user's social media activity and add related information to the feed. This allows them to utilize the user's areas of interest to provide more relevant information.

[0100] The information retrieval system can estimate the user's emotions and adjust the display order of search results based on the estimated user's emotions. For example, if the user is nervous, important search results can be displayed preferentially. If the user is relaxed, detailed search results can be displayed preferentially. Furthermore, if the user is in a hurry, concise search results can be displayed preferentially. In this way, by adjusting the display order of search results according to the user's emotions, it is possible to provide information that is important to the user preferentially.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives natural language input from the user. When the user inputs a survey subject in natural language, the reception unit receives the input. For example, the reception unit can receive natural language input in the form of text input, voice input, or the like. Step 2: The analysis unit analyzes the natural language input received by the reception unit and extracts keywords. Using generative AI, the analysis unit analyzes the natural language input using methods such as morphological analysis, grammatical analysis, and semantic analysis to extract keywords. For example, the analysis unit can extract frequently occurring words and words with high importance. Step 3: The identification unit identifies related information based on the keywords extracted by the analysis unit. The identification unit uses a generation AI to identify related information based on the extracted keywords. For example, the identification unit can identify information from a database or information from the web. Step 4: The search unit searches for the related information identified by the identification unit. The search unit uses the generation AI to search for the identified related information. For example, the search unit can search for the related information using a search algorithm or a search scope. Step 5: The providing unit generates an answer based on the information searched by the search unit and provides it to the user. The providing unit uses a generation AI to generate an answer based on the searched information and provides it to the user. For example, the providing unit can generate answers based on templates or using machine learning.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the 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.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0154] 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.

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input in natural language from a user; an analysis unit that analyzes the natural language input received by the reception unit and extracts keywords; an identifying unit that identifies related information based on the keywords extracted by the analyzing unit; a search unit that searches for related information identified by the identification unit; a providing unit that generates an answer based on the information searched by the searching unit and provides the answer to the user. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions. The system of claim 1 .

3. The reception unit Analyzes the user's past input history and suggests the optimal input method The system of claim 1 .

4. The reception unit Filter natural language input based on the user's current interests The system of claim 1 .

5. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions. The system of claim 1 .

6. The reception unit When inputting natural language, the system takes into account the user's geographic location and prioritizes relevant input. The system of claim 1 .

7. The reception unit When natural language input is used, the app analyzes the user's social media activity and accepts relevant input. The system of claim 1 .

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

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