System

Abstract

To promptly and appropriately provide responses to questions of a user.SOLUTION: A system according to an embodiment comprises a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives questions from a user. The search unit analyzes the questions received by the reception unit and searches for related information. The generation unit generates responses on the basis of the information obtained by the search unit. The provision unit provides the responses generated by the generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to quickly provide appropriate answers to users' questions.

[0005] The system according to the embodiment aims to provide a quick and appropriate answer to a user's question. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives a question from a user. The search unit analyzes the question received by the reception unit and searches for related information. The generation unit generates an answer based on the information obtained by the search unit. The provision unit provides the answer generated by the generation unit to the user. Effect of the Invention

[0007] The system according to the embodiment can provide a quick and appropriate answer to a user's question. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The information provision system according to the embodiment of the present invention is a system that uses a generation AI to improve the user experience. This information provision system allows a user to ask a question in the same window where the user inputs a keyword, and the generation AI analyzes the question, searches for related information, and generates and provides an answer. In addition, a generation AI version of search-linked advertisements is also displayed, and advertisements related to the user's questions are automatically generated. Furthermore, a dedicated entrance for the generation AI is provided to provide a detailed weather forecast for questions about the weather. In the messenger app, the user is also allowed to ask a question to the generation AI in the chat, and the generation AI provides an answer to the question in real time. For example, when a user inputs "What's the weather going to be like tomorrow?", the generation AI provides a specific weather forecast for the question. In addition, the process of how the generation AI generates advertisements for the generation of search-linked advertisements is clarified. The interface of the generation AI in each service is also described in detail. For example, in Yahoo! Search, an interface is provided that allows a user to ask a question in the window where the user inputs a keyword. In Yahoo! Weather and the messenger app, a dedicated entrance and an interface in the chat are provided to access the generation AI. This allows the information provision system to efficiently generate and provide answers to user questions.

[0029] The information provision system according to the embodiment includes a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives a question from a user. For example, the reception unit provides an interface that allows a user to ask a question in a window for inputting a keyword. The reception unit can receive, for example, a text input or a voice input. The search unit analyzes the question received by the reception unit and searches for related information. For example, the search unit can perform a database search or a web search. The search unit analyzes the question using a natural language processing technique and searches for related information. The generation unit generates an answer based on the information obtained by the search unit. For example, the generation unit generates an answer using template-based generation or a generation AI. The generation unit generates an appropriate answer based on the search result using a generation AI. The provision unit provides the answer generated by the generation unit to the user. For example, the provision unit can perform text display or voice output. The provision unit provides an interface for providing the generated answer to the user. For example, the provision unit provides the answer through a web application or a mobile application. This allows the information provision system according to the embodiment to efficiently generate and provide an answer to a user's question. Furthermore, the providing unit can automatically generate and display an advertisement related to the user's question. For example, the providing unit uses a generation AI to generate and display an advertisement related to the user's question. The providing unit provides an interface for providing the generated advertisement to the user. For example, the providing unit provides the advertisement through a web application or a mobile application. In this way, the information providing system according to the embodiment can automatically generate and display an advertisement related to the user's question.

[0030] The reception unit receives questions from the user. For example, the reception unit provides an interface that allows the user to ask a question in a window for inputting a keyword. The reception unit can receive, for example, text input or voice input. Specifically, the reception unit provides a user interface that the user can operate intuitively, and arranges a text box and a microphone icon equipped with a voice recognition function. When the user inputs a question in the text box, the input content is displayed in real time, and in the case of voice input, it is converted into text using voice recognition technology. Furthermore, the reception unit has a buffer function for temporarily storing the user's input content and passing it to subsequent processing. This ensures that the question input by the user is taken into the system, and the system can smoothly move to the next processing step. The reception unit also has a function for providing appropriate feedback to the user's input content. For example, if the input content is unclear or if a specific keyword is missing, a prompt is displayed to the user requesting additional information. This allows the reception unit to accurately receive the user's question and support the subsequent processing to be carried out smoothly.

[0031] The search unit analyzes the question received by the reception unit and searches for related information. For example, the search unit can perform a database search or a web search. The search unit uses natural language processing technology to analyze the question and search for related information. Specifically, the search unit tokenizes the user's question and extracts important keywords and phrases. Then, based on these keywords, it searches internal databases and external web resources to collect related information. The search unit makes full use of natural language processing technology to accurately understand the intent of the question and identify the most relevant information. For example, even if the question is ambiguous, it can generate an appropriate search query taking into account the context. Furthermore, the search unit ranks the search results and preferentially displays the most relevant information. This allows the user to quickly obtain the information he or she needs. In addition, the search unit can also learn the user's past search history and behavior patterns and provide personalized search results. This allows the search unit to provide optimal information according to the user's needs and improve the user experience.

[0032] The generation unit generates an answer based on the information obtained by the search unit. For example, the generation unit generates an answer using template-based generation or a generation AI. The generation unit uses the generation AI to generate an appropriate answer based on the search results. Specifically, the generation unit analyzes the information provided by the search unit and executes an algorithm for generating an optimal answer to the user's question. The generation AI uses natural language generation technology to generate an answer in a format that is easy for the user to understand. For example, the generation AI selects an appropriate style and tone according to the content of the question and generates an answer. In addition, the generation unit can quickly generate a standard answer to a specific question by using a template-based generation method in combination. This allows the generation unit to quickly and accurately provide an answer to the user's question. In addition, the generation unit also has a function of evaluating the quality of the generated answer and correcting it as necessary. For example, if the answer generated by the generation AI is inappropriate or does not match the user's intention, the generation unit automatically regenerates it and provides an optimal answer. This allows the generation unit to always provide high-quality answers and improve user satisfaction.

[0033] The providing unit provides the answer generated by the generating unit to the user. For example, the providing unit can perform text display or voice output. The providing unit provides an interface for providing the generated answer to the user. For example, the providing unit provides the answer through a web application or a mobile application. Specifically, the providing unit provides an interface that is easily accessible to the user and quickly displays the generated answer. In the case of text display, the answer is instantly displayed on the user's screen, and in the case of voice output, the answer is read out using voice synthesis technology. Furthermore, the providing unit also has a function of collecting user feedback and continuously improving the quality of the answer and the method of providing it. For example, by providing an interface that allows the user to leave an evaluation or comment on the provided answer, the service is improved to reflect the user's opinion. In addition, the providing unit also has a function of automatically generating and displaying advertisements related to the user's question. Using the generation AI, an advertisement related to the content of the user's question is generated and displayed through a web application or a mobile application. As a result, the providing unit can provide useful information to the user and obtain advertising revenue. Furthermore, the providing unit can maximize the effectiveness of the advertisement by analyzing the user's behavioral data and providing personalized advertisements. This enables the providing unit to provide high quality answers and related information to the user, improving the user experience.

[0034] The reception unit may be provided with an interface through which the user inputs a keyword. The reception unit may be provided with an interface through which the user can ask a question in a window through which the user inputs a keyword, for example. The interface may include, for example, a text box and a voice input function. The text box is for the user to input a keyword using a keyboard, and the voice input function is for the user to input a keyword by voice using a microphone. This allows the user to ask a question in the window through which the keyword is input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's voice data to the generation AI and cause the generation AI to convert the voice data into text data.

[0035] The search unit can search for information related to the user's question. The search unit, for example, searches for information related to the user's question. The search unit can perform a database search or a web search. A database search is a search for information in a specific database, and a web search is a search for information on the Internet. The search unit analyzes the question using a natural language processing technique and searches for related information. For example, the search unit extracts important keywords from the question using a keyword extraction technique and searches for information based on the keywords. This makes it possible to efficiently search for information related to the user's question. A part or all of the above-mentioned processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input the user's question data to the generation AI and cause the generation AI to search for related information.

[0036] The generation unit can generate an answer based on the search results. The generation unit generates an answer based on the search results, for example. The generation unit can generate an answer using template-based generation or a generation AI. Template-based generation generates an answer based on a template prepared in advance, and generation using a generation AI generates an appropriate answer by the generation AI analyzing the search results. For example, the generation unit generates an appropriate answer based on the search results using the generation AI. The generation AI analyzes the search results using natural language processing technology and generates an answer. This makes it possible to generate an appropriate answer based on the search results. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to generate an answer.

[0037] The providing unit can provide the generated answer to the user. The providing unit, for example, provides the generated answer to the user. The providing unit can perform text display and voice output. The text display is to display the generated answer in text format, and the voice output is to output the generated answer in voice format. For example, the providing unit provides the generated answer to the user through a web application or a mobile application. This makes it possible to provide the generated answer to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the generated answer data to the generation AI and cause the generation AI to provide the answer.

[0038] The provision unit can automatically generate and display an advertisement related to the user's question. The provision unit can automatically generate and display an advertisement related to the user's question, for example. The provision unit can use a generation AI to generate and display an advertisement related to the user's question. The generation AI analyzes the user's question and generates an advertisement related to the question. For example, the provision unit uses a generation AI to generate and display an advertisement related to the user's question. The generation AI uses a natural language processing technology to analyze the user's question and generate an advertisement. This makes it possible to automatically generate and display an advertisement related to the user's question. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the provision unit can input the generated advertisement data to the generation AI and cause the generation AI to generate and display the advertisement.

[0039] The reception unit can provide an interface dedicated to the generation AI so that the user can input a question about the weather. The reception unit can provide an interface dedicated to the generation AI so that the user can input a question about the weather. The interface includes, for example, a text box and a voice input function. The text box is for the user to input a question about the weather using a keyboard, and the voice input function is for the user to input a question about the weather by voice using a microphone. This allows the user to ask a question about the weather. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's voice data to the generation AI and cause the generation AI to convert the voice data into text data.

[0040] The generation unit can generate a detailed weather forecast for a question about the weather. The generation unit generates a detailed weather forecast for a question about the weather, for example. The generation unit can generate a detailed weather forecast for a question about the weather using a generation AI. The generation AI analyzes a user's question and generates a detailed weather forecast for the question. For example, the generation unit generates a detailed weather forecast for a question about the weather using a generation AI. The generation AI analyzes a user's question and generates a weather forecast using a natural language processing technique. This makes it possible to generate a detailed weather forecast for a question about the weather. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user's question data to the generation AI and cause the generation AI to generate a weather forecast.

[0041] The reception unit may provide an interface that allows a user to ask a question to the generation AI within the messenger app. The reception unit may provide an interface that allows a user to ask a question to the generation AI within the messenger app. The interface may include, for example, a chat box and a voice input function. The chat box is for a user to input a question using a keyboard, and the voice input function is for a user to input a question by voice using a microphone. This allows a user to ask a question to the generation AI within the messenger app. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's voice data to the generation AI and cause the generation AI to convert the voice data into text data.

[0042] The generation unit can generate an answer to a question in the messenger app in real time. The generation unit can generate an answer to a question in the messenger app in real time, for example, by using a generation AI. The generation AI analyzes a user's question and generates an answer to the question in real time. For example, the generation unit can generate an answer to a question in the messenger app in real time by using a generation AI. The generation AI analyzes a user's question and generates an answer to the question in real time. For example, the generation unit can generate an answer to a question in the messenger app in real time by using a generation AI. The generation AI uses a natural language processing technique to analyze a user's question and generate an answer. This makes it possible to generate an answer to a question in the messenger app in real time. A part or all of the above-mentioned processing in the generation unit may be performed, for example, by using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user's question data to the generation AI and cause the generation AI to generate an answer.

[0043] The reception unit can analyze the user's past question history and select an appropriate reception method. The reception unit can analyze the user's past question history, for example, and select an appropriate reception method. The reception unit can analyze the user's past question history using the generation AI. For example, the reception unit acquires the user's past question history from a database and inputs it to the generation AI. The generation AI analyzes the question history and selects an appropriate reception method. For example, the reception unit preferentially accepts questions that the user has frequently asked in the past. The reception unit can also find a specific pattern from the user's past question history and suggest an optimal reception method. This makes it possible to select an optimal reception method based on the user's past question history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's question history data to the generation AI and cause the generation AI to select a reception method.

[0044] The reception unit can perform filtering based on the user's current field of interest when receiving a question. For example, the reception unit performs filtering based on the user's current field of interest when receiving a question. The reception unit can identify the user's current field of interest using the generation AI. For example, the reception unit analyzes the user's past search history and social media activity to identify the user's current field of interest. The generation AI identifies the user's field of interest and filters questions based on the field of interest. For example, the reception unit preferentially accepts related questions based on keywords recently searched by the user. The reception unit can also analyze the user's social media activity and preferentially accept questions related to the user's current field of interest. This makes it possible to filter questions based on the user's current field of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's field of interest data to the generation AI and cause the generation AI to perform filtering.

[0045] The reception unit can preferentially accept questions with high relevance based on the geographical location information of the user when accepting a question. For example, the reception unit preferentially accepts questions with high relevance based on the geographical location information of the user when accepting a question. The reception unit can identify the geographical location information of the user using the generation AI. For example, the reception unit acquires geographical location information using GPS data of the user or a location information service. The generation AI analyzes the geographical location information of the user and preferentially accepts questions with high relevance based on the information. For example, when the user is in a specific area, the reception unit preferentially accepts questions related to the area. In addition, when the user is traveling, the reception unit can also preferentially accept questions related to the travel destination. This makes it possible to preferentially accept questions with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute filtering of questions.

[0046] The reception unit can analyze the social media activity of the user when receiving a question and accept related questions. For example, the reception unit analyzes the social media activity of the user when receiving a question and accepts related questions. The reception unit can analyze the social media activity of the user using the generation AI. For example, the reception unit acquires the post content and the number of likes from the user's social media account and inputs them to the generation AI. The generation AI analyzes the social media activity and identifies related questions. For example, the reception unit preferentially accepts related questions based on the content recently posted by the user. The reception unit can also preferentially accept related questions based on the activity of an account followed by the user. This makes it possible to accept related questions based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to perform filtering of questions.

[0047] The search unit can adjust the level of detail of the search based on the importance of the question at the time of the search. For example, the search unit adjusts the level of detail of the search based on the importance of the question at the time of the search. The search unit can evaluate the importance of the question using the generation AI. For example, the search unit analyzes the content of the question and evaluates its importance. The generation AI evaluates the importance of the question and adjusts the level of detail of the search based on the evaluation result. For example, the search unit provides detailed search results for questions with high importance. In addition, the search unit can also provide concise search results for questions with low importance. This makes it possible to adjust the level of detail of the search based on the importance of the question. Part or all of the above-mentioned processing in the search unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to adjust the level of detail of the search.

[0048] The search unit can apply different search algorithms depending on the category of the question at the time of search. For example, the search unit applies different search algorithms depending on the category of the question at the time of search. The search unit can identify the category of the question using the generation AI. For example, the search unit analyzes the content of the question and identifies the category. The generation AI identifies the category of the question and applies an appropriate search algorithm depending on the category. For example, the search unit applies a search algorithm based on meteorological data to a question about the weather. The search unit can also apply a search algorithm based on medical data to a question about health. This makes it possible to apply an appropriate search algorithm depending on the category of the question. A part or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to apply the search algorithm.

[0049] The search unit can determine the priority of the search based on the time of submission of the question at the time of the search. For example, the search unit determines the priority of the search based on the time of submission of the question at the time of the search. The search unit can evaluate the time of submission of the question using the generation AI. For example, the search unit analyzes the submission date and time of the question and evaluates its urgency. The generation AI evaluates the time of submission of the question and determines the priority of the search based on the evaluation result. For example, the search unit preferentially searches for recently submitted questions. In addition, the search unit can also lower the search priority for old questions. This makes it possible to determine the priority of the search based on the time of submission of the question. Part or all of the above-mentioned processing in the search unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to execute the determination of the search priority.

[0050] The search unit can adjust the search order based on the relevance of the question at the time of search. For example, the search unit adjusts the search order based on the relevance of the question at the time of search. The search unit can evaluate the relevance of the question using the generation AI. For example, the search unit analyzes the content of the question and evaluates its relevance. The generation AI evaluates the relevance of the question and adjusts the search order based on the evaluation result. For example, the search unit performs a search preferentially when the relevance of the question is high. In addition, the search unit can also lower the priority of the search when the relevance of the question is low. This makes it possible to adjust the search order based on the relevance of the question. Part or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to adjust the search order.

[0051] The generation unit can adjust the level of detail of the answer based on the importance of the search results when generating the answer. The generation unit adjusts the level of detail of the answer based on the importance of the search results when generating the answer, for example. The generation unit can evaluate the importance of the search results using the generation AI. For example, the generation unit analyzes the contents of the search results and evaluates their importance. The generation AI evaluates the importance of the search results and adjusts the level of detail of the answer based on the evaluation result. For example, the generation unit generates a detailed answer based on a search result with high importance. The generation unit can also generate a concise answer based on a search result with low importance. This makes it possible to adjust the level of detail of the answer based on the importance of the search results. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to adjust the level of detail of the answer.

[0052] The generation unit can apply different generation algorithms depending on the category of the question when generating an answer. For example, the generation unit applies different generation algorithms depending on the category of the question when generating an answer. The generation unit can identify the category of the question using the generation AI. For example, the generation unit analyzes the content of the question and identifies the category. The generation AI identifies the category of the question and applies an appropriate generation algorithm depending on the category. For example, the generation unit applies a generation algorithm based on meteorological data to a question about the weather. In addition, the generation unit can also apply a generation algorithm based on medical data to a question about health. This makes it possible to apply an appropriate generation algorithm depending on the category of the question. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input question data to the generation AI and cause the generation AI to apply the generation algorithm.

[0053] The generation unit can determine the priority of the answers based on the time of submission of the search results when generating the answers. For example, the generation unit determines the priority of the answers based on the time of submission of the search results when generating the answers. The generation unit can evaluate the time of submission of the search results using the generation AI. For example, the generation unit analyzes the submission date and time of the search results and evaluates the urgency. The generation AI evaluates the time of submission of the search results and determines the priority of the answers based on the evaluation result. For example, the generation unit generates answers preferentially based on recently submitted search results. In addition, the generation unit can also lower the priority of answers based on old search results. This makes it possible to determine the priority of the answers based on the time of submission of the search results. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to execute the determination of the priority of the answers.

[0054] The generation unit can adjust the order of answers based on the relevance of the search results when generating answers. The generation unit adjusts the order of answers based on the relevance of the search results when generating answers, for example. The generation unit can evaluate the relevance of the search results using the generation AI. For example, the generation unit analyzes the contents of the search results and evaluates their relevance. The generation AI evaluates the relevance of the search results and adjusts the order of answers based on the evaluation result. For example, the generation unit generates answers preferentially when the relevance of the search results is high. In addition, the generation unit can also lower the priority of the answers when the relevance of the search results is low. This makes it possible to adjust the order of answers based on the relevance of the search results. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to adjust the order of answers.

[0055] The providing unit can select the optimal providing method by referring to the user's past question history when providing an answer. For example, the providing unit selects the optimal providing method by referring to the user's past question history when providing an answer. The providing unit can analyze the user's past question history using the generation AI. For example, the providing unit acquires the user's past question history from a database and inputs it to the generation AI. The generation AI analyzes the question history and selects the optimal providing method. For example, the providing unit preferentially provides a providing method (text, voice, etc.) that the user has used in the past. The providing unit can also find a specific pattern from the user's past question history and suggest the optimal providing method. This makes it possible to select the optimal providing method based on the user's past question history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's question history data to the generation AI and cause the generation AI to select the providing method.

[0056] The providing unit can customize the provided content based on the user's current field of interest when providing an answer. For example, the providing unit customizes the provided content based on the user's current field of interest when providing an answer. The providing unit can identify the user's current field of interest using the generation AI. For example, the providing unit analyzes the user's past search history and social media activity to identify the user's current field of interest. The generation AI identifies the user's field of interest and customizes the provided content based on the field of interest. For example, the providing unit provides related information based on keywords recently searched by the user. The providing unit can also analyze the user's social media activity and provide information related to the current field of interest. This makes it possible to customize the provided content based on the user's current field of interest. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's field of interest data to the generation AI and cause the generation AI to execute customization of the provided content.

[0057] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing an answer. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing an answer. The providing unit can identify the geographical location information of the user using the generation AI. For example, the providing unit acquires geographical location information using the user's GPS data or a location information service. The generating AI analyzes the geographical location information of the user and selects the optimal providing method based on the information. For example, when the user is in a specific area, the providing unit can preferentially provide information related to the area. In addition, when the user is traveling, the providing unit can also preferentially provide information related to the travel destination. This makes it possible to select the optimal providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the providing method.

[0058] The providing unit can customize the content to be provided by analyzing the social media activity of the user when providing an answer. For example, the providing unit customizes the content to be provided by analyzing the social media activity of the user when providing an answer. The providing unit can analyze the social media activity of the user using the generation AI. For example, the providing unit obtains the posted content and the number of likes from the user's social media account and inputs them to the generation AI. The generation AI analyzes the social media activity and customizes the content to be provided based on the activity. For example, the providing unit provides related information based on the content recently posted by the user. The providing unit can also provide related information based on the activity of an account followed by the user. This makes it possible to customize the content to be provided based on the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the social media data of the user to the generation AI and cause the generation AI to customize the content to be provided.

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

[0060] The reception unit can analyze the user's past search history and preferentially receive questions related to topics in which the user has shown interest in the past. For example, questions related to topics in which the user has spent a lot of time in the past are preferentially processed. The reception unit can also automatically suggest related questions based on keywords that the user has frequently searched for in the past. Furthermore, the reception unit can find specific patterns from the user's past search history and suggest questions related to new topics that the user may be interested in.

[0061] The search unit can prioritize searching for information specific to a region based on the user's current geographic location information. For example, if the user is in a particular city, event information and news related to that city are searched for with priority. In addition, if the user is traveling, the search unit can prioritize providing tourist information and restaurant reviews related to the travel destination. Furthermore, the search unit can provide local weather forecasts and traffic information in real time based on the user's geographic location information.

[0062] The providing unit can provide a preferred answer format (text, audio, video, etc.) for the user based on the user's past question history. For example, if the user has preferred audio answers in the past, the providing unit can provide answers in audio format. Also, if the user has preferred video answers, the providing unit can provide answers in video format. Furthermore, the providing unit can find specific patterns from the user's past question history and suggest the optimal answer format.

[0063] The reception unit can analyze the user's social media activity and preferentially receive questions related to topics that the user has recently been interested in. For example, questions related to topics that the user has recently spent a lot of time on are preferentially processed. The reception unit can also automatically suggest related questions based on the activity of accounts the user follows. Furthermore, the reception unit can find certain patterns from the user's social media activity and suggest questions related to new topics that the user may be interested in.

[0064] The generator may preferentially include relevant information in the answer based on the user's current field of interest. For example, the generator may preferentially provide information about a topic on which the user has recently spent a lot of time. The generator may also include relevant information in the answer based on keywords recently searched by the user. The generator may also suggest new information related to the user's current field of interest.

[0065] The providing unit can provide information specific to a region preferentially based on the user's geographical location information. For example, if the user is in a particular city, event information and news related to that city are provided preferentially. In addition, if the user is traveling, the providing unit can also provide tourist information and restaurant reviews related to the travel destination preferentially. Furthermore, the providing unit can provide local weather forecasts and traffic information in real time based on the user's geographical location information.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The reception unit receives a question from a user. For example, the reception unit provides an interface that allows the user to ask a question in a window for inputting a keyword. The reception unit can receive, for example, a text input or a voice input. Step 2: The search unit analyzes the question received by the reception unit and searches for related information. For example, the search unit can perform a database search or a web search. The search unit analyzes the question using natural language processing technology and searches for related information. Step 3: The generator generates an answer based on the information obtained by the search unit. For example, the generator generates an answer using template-based generation or a generation AI. The generator uses a generation AI to generate an appropriate answer based on the search results. Step 4: The providing unit provides the answer generated by the generating unit to the user. For example, the providing unit can perform text display or voice output. The providing unit provides an interface for providing the generated answer to the user. For example, the providing unit provides the answer through a web application or a mobile application.

[0068] (Example 2) The information provision system according to the embodiment of the present invention is a system that uses a generation AI to improve the user experience. This information provision system allows a user to ask a question in the same window where the user inputs a keyword, and the generation AI analyzes the question, searches for related information, and generates and provides an answer. In addition, a generation AI version of search-linked advertisements is also displayed, and advertisements related to the user's questions are automatically generated. Furthermore, a dedicated entrance for the generation AI is provided to provide a detailed weather forecast for questions about the weather. In the messenger app, the user is also allowed to ask a question to the generation AI in the chat, and the generation AI provides an answer to the question in real time. For example, when a user inputs "What's the weather going to be like tomorrow?", the generation AI provides a specific weather forecast for the question. In addition, the process of how the generation AI generates advertisements for the generation of search-linked advertisements is clarified. The interface of the generation AI in each service is also described in detail. For example, in Yahoo! Search, an interface is provided that allows a user to ask a question in the window where the user inputs a keyword. In Yahoo! Weather and the messenger app, a dedicated entrance and an interface in the chat are provided to access the generation AI. This allows the information provision system to efficiently generate and provide answers to user questions.

[0069] The information provision system according to the embodiment includes a reception unit, a search unit, a generation unit, and a provision unit. The reception unit receives a question from a user. For example, the reception unit provides an interface that allows a user to ask a question in a window for inputting a keyword. The reception unit can receive, for example, a text input or a voice input. The search unit analyzes the question received by the reception unit and searches for related information. For example, the search unit can perform a database search or a web search. The search unit analyzes the question using a natural language processing technique and searches for related information. The generation unit generates an answer based on the information obtained by the search unit. For example, the generation unit generates an answer using template-based generation or a generation AI. The generation unit generates an appropriate answer based on the search result using a generation AI. The provision unit provides the answer generated by the generation unit to the user. For example, the provision unit can perform text display or voice output. The provision unit provides an interface for providing the generated answer to the user. For example, the provision unit provides the answer through a web application or a mobile application. This allows the information provision system according to the embodiment to efficiently generate and provide an answer to a user's question. Furthermore, the providing unit can automatically generate and display an advertisement related to the user's question. For example, the providing unit uses a generation AI to generate and display an advertisement related to the user's question. The providing unit provides an interface for providing the generated advertisement to the user. For example, the providing unit provides the advertisement through a web application or a mobile application. In this way, the information providing system according to the embodiment can automatically generate and display an advertisement related to the user's question.

[0070] The reception unit receives questions from the user. For example, the reception unit provides an interface that allows the user to ask a question in a window for inputting a keyword. The reception unit can receive, for example, text input or voice input. Specifically, the reception unit provides a user interface that the user can operate intuitively, and arranges a text box and a microphone icon equipped with a voice recognition function. When the user inputs a question in the text box, the input content is displayed in real time, and in the case of voice input, it is converted into text using voice recognition technology. Furthermore, the reception unit has a buffer function for temporarily storing the user's input content and passing it to subsequent processing. This ensures that the question input by the user is taken into the system, and the system can smoothly move to the next processing step. The reception unit also has a function for providing appropriate feedback to the user's input content. For example, if the input content is unclear or if a specific keyword is missing, a prompt is displayed to the user requesting additional information. This allows the reception unit to accurately receive the user's question and support the subsequent processing to be carried out smoothly.

[0071] The search unit analyzes the question received by the reception unit and searches for related information. For example, the search unit can perform a database search or a web search. The search unit uses natural language processing technology to analyze the question and search for related information. Specifically, the search unit tokenizes the user's question and extracts important keywords and phrases. Then, based on these keywords, it searches internal databases and external web resources to collect related information. The search unit makes full use of natural language processing technology to accurately understand the intent of the question and identify the most relevant information. For example, even if the question is ambiguous, it can generate an appropriate search query taking into account the context. Furthermore, the search unit ranks the search results and preferentially displays the most relevant information. This allows the user to quickly obtain the information he or she needs. In addition, the search unit can also learn the user's past search history and behavior patterns and provide personalized search results. This allows the search unit to provide optimal information according to the user's needs and improve the user experience.

[0072] The generation unit generates an answer based on the information obtained by the search unit. For example, the generation unit generates an answer using template-based generation or a generation AI. The generation unit uses the generation AI to generate an appropriate answer based on the search results. Specifically, the generation unit analyzes the information provided by the search unit and executes an algorithm for generating an optimal answer to the user's question. The generation AI uses natural language generation technology to generate an answer in a format that is easy for the user to understand. For example, the generation AI selects an appropriate style and tone according to the content of the question and generates an answer. In addition, the generation unit can quickly generate a standard answer to a specific question by using a template-based generation method in combination. This allows the generation unit to quickly and accurately provide an answer to the user's question. In addition, the generation unit also has a function of evaluating the quality of the generated answer and correcting it as necessary. For example, if the answer generated by the generation AI is inappropriate or does not match the user's intention, the generation unit automatically regenerates it and provides an optimal answer. This allows the generation unit to always provide high-quality answers and improve user satisfaction.

[0073] The providing unit provides the answer generated by the generating unit to the user. For example, the providing unit can perform text display or voice output. The providing unit provides an interface for providing the generated answer to the user. For example, the providing unit provides the answer through a web application or a mobile application. Specifically, the providing unit provides an interface that is easily accessible to the user and quickly displays the generated answer. In the case of text display, the answer is instantly displayed on the user's screen, and in the case of voice output, the answer is read out using voice synthesis technology. Furthermore, the providing unit also has a function of collecting user feedback and continuously improving the quality of the answer and the method of providing it. For example, by providing an interface that allows the user to leave an evaluation or comment on the provided answer, the service is improved to reflect the user's opinion. In addition, the providing unit also has a function of automatically generating and displaying advertisements related to the user's question. Using the generation AI, an advertisement related to the content of the user's question is generated and displayed through a web application or a mobile application. As a result, the providing unit can provide useful information to the user and obtain advertising revenue. Furthermore, the providing unit can maximize the effectiveness of the advertisement by analyzing the user's behavioral data and providing personalized advertisements. This enables the providing unit to provide high quality answers and related information to the user, improving the user experience.

[0074] The reception unit may be provided with an interface through which the user inputs a keyword. The reception unit may be provided with an interface through which the user can ask a question in a window through which the user inputs a keyword, for example. The interface may include, for example, a text box and a voice input function. The text box is for the user to input a keyword using a keyboard, and the voice input function is for the user to input a keyword by voice using a microphone. This allows the user to ask a question in the window through which the keyword is input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's voice data to the generation AI and cause the generation AI to convert the voice data into text data.

[0075] The search unit can search for information related to the user's question. The search unit, for example, searches for information related to the user's question. The search unit can perform a database search or a web search. A database search is a search for information in a specific database, and a web search is a search for information on the Internet. The search unit analyzes the question using a natural language processing technique and searches for related information. For example, the search unit extracts important keywords from the question using a keyword extraction technique and searches for information based on the keywords. This makes it possible to efficiently search for information related to the user's question. A part or all of the above-mentioned processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input the user's question data to the generation AI and cause the generation AI to search for related information.

[0076] The generation unit can generate an answer based on the search results. The generation unit generates an answer based on the search results, for example. The generation unit can generate an answer using template-based generation or a generation AI. Template-based generation generates an answer based on a template prepared in advance, and generation using a generation AI generates an appropriate answer by the generation AI analyzing the search results. For example, the generation unit generates an appropriate answer based on the search results using the generation AI. The generation AI analyzes the search results using natural language processing technology and generates an answer. This makes it possible to generate an appropriate answer based on the search results. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to generate an answer.

[0077] The providing unit can provide the generated answer to the user. The providing unit, for example, provides the generated answer to the user. The providing unit can perform text display and voice output. The text display is to display the generated answer in text format, and the voice output is to output the generated answer in voice format. For example, the providing unit provides the generated answer to the user through a web application or a mobile application. This makes it possible to provide the generated answer to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the generated answer data to the generation AI and cause the generation AI to provide the answer.

[0078] The provision unit can automatically generate and display an advertisement related to the user's question. The provision unit can automatically generate and display an advertisement related to the user's question, for example. The provision unit can use a generation AI to generate and display an advertisement related to the user's question. The generation AI analyzes the user's question and generates an advertisement related to the question. For example, the provision unit uses a generation AI to generate and display an advertisement related to the user's question. The generation AI uses a natural language processing technology to analyze the user's question and generate an advertisement. This makes it possible to automatically generate and display an advertisement related to the user's question. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the provision unit can input the generated advertisement data to the generation AI and cause the generation AI to generate and display the advertisement.

[0079] The reception unit can provide an interface dedicated to the generation AI so that the user can input a question about the weather. The reception unit can provide an interface dedicated to the generation AI so that the user can input a question about the weather. The interface includes, for example, a text box and a voice input function. The text box is for the user to input a question about the weather using a keyboard, and the voice input function is for the user to input a question about the weather by voice using a microphone. This allows the user to ask a question about the weather. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's voice data to the generation AI and cause the generation AI to convert the voice data into text data.

[0080] The generation unit can generate a detailed weather forecast for a question about the weather. The generation unit generates a detailed weather forecast for a question about the weather, for example. The generation unit can generate a detailed weather forecast for a question about the weather using a generation AI. The generation AI analyzes a user's question and generates a detailed weather forecast for the question. For example, the generation unit generates a detailed weather forecast for a question about the weather using a generation AI. The generation AI analyzes a user's question and generates a weather forecast using a natural language processing technique. This makes it possible to generate a detailed weather forecast for a question about the weather. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user's question data to the generation AI and cause the generation AI to generate a weather forecast.

[0081] The reception unit may provide an interface that allows a user to ask a question to the generation AI within the messenger app. The reception unit may provide an interface that allows a user to ask a question to the generation AI within the messenger app. The interface may include, for example, a chat box and a voice input function. The chat box is for a user to input a question using a keyboard, and the voice input function is for a user to input a question by voice using a microphone. This allows a user to ask a question to the generation AI within the messenger app. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's voice data to the generation AI and cause the generation AI to convert the voice data into text data.

[0082] The generation unit can generate an answer to a question in the messenger app in real time. The generation unit can generate an answer to a question in the messenger app in real time, for example, by using a generation AI. The generation AI analyzes a user's question and generates an answer to the question in real time. For example, the generation unit can generate an answer to a question in the messenger app in real time by using a generation AI. The generation AI analyzes a user's question and generates an answer to the question in real time. For example, the generation unit can generate an answer to a question in the messenger app in real time by using a generation AI. The generation AI uses a natural language processing technique to analyze a user's question and generate an answer. This makes it possible to generate an answer to a question in the messenger app in real time. A part or all of the above-mentioned processing in the generation unit may be performed, for example, by using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user's question data to the generation AI and cause the generation AI to generate an answer.

[0083] The reception unit can estimate the user's emotion and adjust the timing of receiving the question based on the estimated user's emotion. The reception unit can estimate the user's emotion, for example, and adjust the timing of receiving the question based on the estimated user's emotion. The reception unit can estimate the user's emotion using an emotion estimation algorithm. For example, the reception unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The reception unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to adjust the timing of receiving the question according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0084] The reception unit can analyze the user's past question history and select an appropriate reception method. The reception unit can analyze the user's past question history, for example, and select an appropriate reception method. The reception unit can analyze the user's past question history using the generation AI. For example, the reception unit acquires the user's past question history from a database and inputs it to the generation AI. The generation AI analyzes the question history and selects an appropriate reception method. For example, the reception unit preferentially accepts questions that the user has frequently asked in the past. The reception unit can also find a specific pattern from the user's past question history and suggest an optimal reception method. This makes it possible to select an optimal reception method based on the user's past question history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's question history data to the generation AI and cause the generation AI to select a reception method.

[0085] The reception unit can perform filtering based on the user's current field of interest when receiving a question. For example, the reception unit performs filtering based on the user's current field of interest when receiving a question. The reception unit can identify the user's current field of interest using the generation AI. For example, the reception unit analyzes the user's past search history and social media activity to identify the user's current field of interest. The generation AI identifies the user's field of interest and filters questions based on the field of interest. For example, the reception unit preferentially accepts related questions based on keywords recently searched by the user. The reception unit can also analyze the user's social media activity and preferentially accept questions related to the user's current field of interest. This makes it possible to filter questions based on the user's current field of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's field of interest data to the generation AI and cause the generation AI to perform filtering.

[0086] The reception unit can estimate the user's emotion and determine the priority of the questions to be received based on the estimated user's emotion. The reception unit can estimate the user's emotion, for example, and determine the priority of the questions to be received based on the estimated user's emotion. The reception unit can estimate the user's emotion using an emotion estimation algorithm. For example, the reception unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The reception unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to determine the priority of the questions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0087] The reception unit can preferentially accept questions with high relevance based on the geographical location information of the user when accepting a question. For example, the reception unit preferentially accepts questions with high relevance based on the geographical location information of the user when accepting a question. The reception unit can identify the geographical location information of the user using the generation AI. For example, the reception unit acquires geographical location information using GPS data of the user or a location information service. The generation AI analyzes the geographical location information of the user and preferentially accepts questions with high relevance based on the information. For example, when the user is in a specific area, the reception unit preferentially accepts questions related to the area. In addition, when the user is traveling, the reception unit can also preferentially accept questions related to the travel destination. This makes it possible to preferentially accept questions with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute filtering of questions.

[0088] The reception unit can analyze the social media activity of the user when receiving a question and accept related questions. For example, the reception unit analyzes the social media activity of the user when receiving a question and accepts related questions. The reception unit can analyze the social media activity of the user using the generation AI. For example, the reception unit acquires the post content and the number of likes from the user's social media account and inputs them to the generation AI. The generation AI analyzes the social media activity and identifies related questions. For example, the reception unit preferentially accepts related questions based on the content recently posted by the user. The reception unit can also preferentially accept related questions based on the activity of an account followed by the user. This makes it possible to accept related questions based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to perform filtering of questions.

[0089] The search unit can estimate the user's emotion and adjust the expression method of the search based on the estimated user's emotion. The search unit can estimate the user's emotion, for example, and adjust the expression method of the search based on the estimated user's emotion. The search unit can estimate the user's emotion using an emotion estimation algorithm. For example, the search unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The search unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the search unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to adjust the expression method of the search according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input a user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] The search unit can adjust the level of detail of the search based on the importance of the question at the time of the search. For example, the search unit adjusts the level of detail of the search based on the importance of the question at the time of the search. The search unit can evaluate the importance of the question using the generation AI. For example, the search unit analyzes the content of the question and evaluates its importance. The generation AI evaluates the importance of the question and adjusts the level of detail of the search based on the evaluation result. For example, the search unit provides detailed search results for questions with high importance. In addition, the search unit can also provide concise search results for questions with low importance. This makes it possible to adjust the level of detail of the search based on the importance of the question. Part or all of the above-mentioned processing in the search unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to adjust the level of detail of the search.

[0091] The search unit can apply different search algorithms depending on the category of the question at the time of search. For example, the search unit applies different search algorithms depending on the category of the question at the time of search. The search unit can identify the category of the question using the generation AI. For example, the search unit analyzes the content of the question and identifies the category. The generation AI identifies the category of the question and applies an appropriate search algorithm depending on the category. For example, the search unit applies a search algorithm based on meteorological data to a question about the weather. The search unit can also apply a search algorithm based on medical data to a question about health. This makes it possible to apply an appropriate search algorithm depending on the category of the question. A part or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to apply the search algorithm.

[0092] The search unit can estimate the user's emotion and adjust the length of the search based on the estimated user's emotion. The search unit can estimate the user's emotion, for example, and adjust the length of the search based on the estimated user's emotion. The search unit can estimate the user's emotion using an emotion estimation algorithm. For example, the search unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The search unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the search unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to adjust the length of the search according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The search unit can determine the priority of the search based on the time of submission of the question at the time of the search. For example, the search unit determines the priority of the search based on the time of submission of the question at the time of the search. The search unit can evaluate the time of submission of the question using the generation AI. For example, the search unit analyzes the submission date and time of the question and evaluates its urgency. The generation AI evaluates the time of submission of the question and determines the priority of the search based on the evaluation result. For example, the search unit preferentially searches for recently submitted questions. In addition, the search unit can also lower the search priority for old questions. This makes it possible to determine the priority of the search based on the time of submission of the question. Part or all of the above-mentioned processing in the search unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to execute the determination of the search priority.

[0094] The search unit can adjust the search order based on the relevance of the question at the time of search. For example, the search unit adjusts the search order based on the relevance of the question at the time of search. The search unit can evaluate the relevance of the question using the generation AI. For example, the search unit analyzes the content of the question and evaluates its relevance. The generation AI evaluates the relevance of the question and adjusts the search order based on the evaluation result. For example, the search unit performs a search preferentially when the relevance of the question is high. In addition, the search unit can also lower the priority of the search when the relevance of the question is low. This makes it possible to adjust the search order based on the relevance of the question. Part or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input question data to the generation AI and cause the generation AI to adjust the search order.

[0095] The generation unit can estimate the user's emotion and adjust the answer generation method based on the estimated user's emotion. The generation unit can estimate the user's emotion, for example, and adjust the answer generation method based on the estimated user's emotion. The generation unit can estimate the user's emotion using an emotion estimation algorithm. For example, the generation unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to adjust the answer generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0096] The generation unit can adjust the level of detail of the answer based on the importance of the search results when generating the answer. The generation unit adjusts the level of detail of the answer based on the importance of the search results when generating the answer, for example. The generation unit can evaluate the importance of the search results using the generation AI. For example, the generation unit analyzes the contents of the search results and evaluates their importance. The generation AI evaluates the importance of the search results and adjusts the level of detail of the answer based on the evaluation result. For example, the generation unit generates a detailed answer based on a search result with high importance. The generation unit can also generate a concise answer based on a search result with low importance. This makes it possible to adjust the level of detail of the answer based on the importance of the search results. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to adjust the level of detail of the answer.

[0097] The generation unit can apply different generation algorithms depending on the category of the question when generating an answer. For example, the generation unit applies different generation algorithms depending on the category of the question when generating an answer. The generation unit can identify the category of the question using the generation AI. For example, the generation unit analyzes the content of the question and identifies the category. The generation AI identifies the category of the question and applies an appropriate generation algorithm depending on the category. For example, the generation unit applies a generation algorithm based on meteorological data to a question about the weather. In addition, the generation unit can also apply a generation algorithm based on medical data to a question about health. This makes it possible to apply an appropriate generation algorithm depending on the category of the question. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input question data to the generation AI and cause the generation AI to apply the generation algorithm.

[0098] The generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user's emotion. The generation unit can estimate the user's emotion, for example, and adjust the length of the answer based on the estimated user's emotion. The generation unit can estimate the user's emotion using an emotion estimation algorithm. For example, the generation unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to adjust the length of the answer according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The generation unit can determine the priority of the answers based on the time of submission of the search results when generating the answers. For example, the generation unit determines the priority of the answers based on the time of submission of the search results when generating the answers. The generation unit can evaluate the time of submission of the search results using the generation AI. For example, the generation unit analyzes the submission date and time of the search results and evaluates the urgency. The generation AI evaluates the time of submission of the search results and determines the priority of the answers based on the evaluation result. For example, the generation unit generates answers preferentially based on recently submitted search results. In addition, the generation unit can also lower the priority of answers based on old search results. This makes it possible to determine the priority of the answers based on the time of submission of the search results. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to execute the determination of the priority of the answers.

[0100] The generation unit can adjust the order of answers based on the relevance of the search results when generating answers. The generation unit adjusts the order of answers based on the relevance of the search results when generating answers, for example. The generation unit can evaluate the relevance of the search results using the generation AI. For example, the generation unit analyzes the contents of the search results and evaluates their relevance. The generation AI evaluates the relevance of the search results and adjusts the order of answers based on the evaluation result. For example, the generation unit generates answers preferentially when the relevance of the search results is high. In addition, the generation unit can also lower the priority of the answers when the relevance of the search results is low. This makes it possible to adjust the order of answers based on the relevance of the search results. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs search result data to the generation AI and causes the generation AI to adjust the order of answers.

[0101] The providing unit can estimate the user's emotion and adjust the answer providing method based on the estimated user's emotion. The providing unit can estimate the user's emotion, for example, and adjust the answer providing method based on the estimated user's emotion. The providing unit can estimate the user's emotion using an emotion estimation algorithm. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to adjust the answer providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0102] The providing unit can select the optimal providing method by referring to the user's past question history when providing an answer. For example, the providing unit selects the optimal providing method by referring to the user's past question history when providing an answer. The providing unit can analyze the user's past question history using the generation AI. For example, the providing unit acquires the user's past question history from a database and inputs it to the generation AI. The generation AI analyzes the question history and selects the optimal providing method. For example, the providing unit preferentially provides a providing method (text, voice, etc.) that the user has used in the past. The providing unit can also find a specific pattern from the user's past question history and suggest the optimal providing method. This makes it possible to select the optimal providing method based on the user's past question history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's question history data to the generation AI and cause the generation AI to select the providing method.

[0103] The providing unit can customize the provided content based on the user's current field of interest when providing an answer. For example, the providing unit customizes the provided content based on the user's current field of interest when providing an answer. The providing unit can identify the user's current field of interest using the generation AI. For example, the providing unit analyzes the user's past search history and social media activity to identify the user's current field of interest. The generation AI identifies the user's field of interest and customizes the provided content based on the field of interest. For example, the providing unit provides related information based on keywords recently searched by the user. The providing unit can also analyze the user's social media activity and provide information related to the current field of interest. This makes it possible to customize the provided content based on the user's current field of interest. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's field of interest data to the generation AI and cause the generation AI to execute customization of the provided content.

[0104] The providing unit can estimate the user's emotion and determine the priority of answers to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion, for example, and determine the priority of answers to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion using an emotion estimation algorithm. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotion. This makes it possible to determine the priority of answers to be provided according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's facial expression data to the generating AI and cause the generating AI to estimate emotions.

[0105] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing an answer. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing an answer. The providing unit can identify the geographical location information of the user using the generation AI. For example, the providing unit acquires geographical location information using the user's GPS data or a location information service. The generating AI analyzes the geographical location information of the user and selects the optimal providing method based on the information. For example, when the user is in a specific area, the providing unit can preferentially provide information related to the area. In addition, when the user is traveling, the providing unit can also preferentially provide information related to the travel destination. This makes it possible to select the optimal providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the providing method.

[0106] The providing unit can customize the content to be provided by analyzing the social media activity of the user when providing an answer. For example, the providing unit customizes the content to be provided by analyzing the social media activity of the user when providing an answer. The providing unit can analyze the social media activity of the user using the generation AI. For example, the providing unit obtains the posted content and the number of likes from the user's social media account and inputs them to the generation AI. The generation AI analyzes the social media activity and customizes the content to be provided based on the activity. For example, the providing unit provides related information based on the content recently posted by the user. The providing unit can also provide related information based on the activity of an account followed by the user. This makes it possible to customize the content to be provided based on the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the social media data of the user to the generation AI and cause the generation AI to customize the content to be provided.

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

[0108] The reception unit can analyze the user's past search history and preferentially receive questions related to topics in which the user has shown interest in the past. For example, questions related to topics in which the user has spent a lot of time in the past are preferentially processed. The reception unit can also automatically suggest related questions based on keywords that the user has frequently searched for in the past. Furthermore, the reception unit can find specific patterns from the user's past search history and suggest questions related to new topics that the user may be interested in.

[0109] The search unit can prioritize searching for information specific to a region based on the user's current geographic location information. For example, if the user is in a particular city, event information and news related to that city are searched for with priority. In addition, if the user is traveling, the search unit can prioritize providing tourist information and restaurant reviews related to the travel destination. Furthermore, the search unit can provide local weather forecasts and traffic information in real time based on the user's geographic location information.

[0110] The generator can estimate the user's emotion and adjust the tone and style of the answer based on the estimated emotion. For example, if the user is stressed, the generator can generate an answer in a relaxing and gentle tone. If the user is excited, the generator can generate an answer in an energetic tone. If the user is sad, the generator can generate an answer in a comforting tone.

[0111] The providing unit can provide a preferred answer format (text, audio, video, etc.) for the user based on the user's past question history. For example, if the user has preferred audio answers in the past, the providing unit can provide answers in audio format. Also, if the user has preferred video answers, the providing unit can provide answers in video format. Furthermore, the providing unit can find specific patterns from the user's past question history and suggest the optimal answer format.

[0112] The providing unit can estimate the user's emotion and adjust the way of providing the answer based on the estimated emotion. For example, if the user is tired, the providing unit can provide a simple and easy-to-understand answer. If the user is excited, the providing unit can provide detailed and in-depth information. Furthermore, if the user is anxious, the providing unit can provide a reassuring answer.

[0113] The reception unit can analyze the user's social media activity and preferentially receive questions related to topics that the user has recently been interested in. For example, questions related to topics that the user has recently spent a lot of time on are preferentially processed. The reception unit can also automatically suggest related questions based on the activity of accounts the user follows. Furthermore, the reception unit can find certain patterns from the user's social media activity and suggest questions related to new topics that the user may be interested in.

[0114] The search unit can estimate the user's emotions and adjust the way search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can display search results that are simple and easy to view. If the user is excited, the search unit can display search results that are detailed and rich in information. If the user is sad, the search unit can display search results that include comforting information.

[0115] The generator may preferentially include relevant information in the answer based on the user's current field of interest. For example, the generator may preferentially provide information about a topic on which the user has recently spent a lot of time. The generator may also include relevant information in the answer based on keywords recently searched by the user. The generator may also suggest new information related to the user's current field of interest.

[0116] The providing unit can provide information specific to a region preferentially based on the user's geographical location information. For example, if the user is in a particular city, event information and news related to that city are provided preferentially. In addition, if the user is traveling, the providing unit can also provide tourist information and restaurant reviews related to the travel destination preferentially. Furthermore, the providing unit can provide local weather forecasts and traffic information in real time based on the user's geographical location information.

[0117] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated emotion. For example, if the user is stressed, the providing unit can provide information that relaxes the user with priority. Also, if the user is excited, the providing unit can provide energetic information with priority. Furthermore, if the user is sad, the providing unit can provide comforting information with priority.

[0118] The process flow of the second embodiment will be briefly described below.

[0119] Step 1: The reception unit receives a question from a user. For example, the reception unit provides an interface that allows the user to ask a question in a window for inputting a keyword. The reception unit can receive, for example, a text input or a voice input. Step 2: The search unit analyzes the question received by the reception unit and searches for related information. For example, the search unit can perform a database search or a web search. The search unit analyzes the question using natural language processing technology and searches for related information. Step 3: The generator generates an answer based on the information obtained by the search unit. For example, the generator generates an answer using template-based generation or a generation AI. The generator uses a generation AI to generate an appropriate answer based on the search results. Step 4: The providing unit provides the answer generated by the generating unit to the user. For example, the providing unit can perform text display or voice output. The providing unit provides an interface for providing the generated answer to the user. For example, the providing unit provides the answer through a web application or a mobile application.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0122] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a question from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question and searches for related information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the search result. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated answer to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0131] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a question from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question and searches for related information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the search result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated answer to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0150] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a question from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question and searches for related information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the search result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated answer to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] Each of the multiple elements including the above-mentioned reception unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a question from a user. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question and searches for related information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the search result. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated answer to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0174] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0178] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] In addition, 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 upon request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0185] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0189] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0191] (Appendix 1) A reception unit that receives questions from users; a search unit that analyzes the question received by the reception unit and searches for related information; A generator that generates an answer based on the information obtained by the search unit; a providing unit that provides the answer generated by the generating unit to a user. A system characterized by: (Appendix 2) The reception unit is Provide an interface for users to input keywords 2. The system of claim 1, (Appendix 3) The search unit is Find information related to your question 2. The system of claim 1, (Appendix 4) The generation unit is Generate answers based on search results 2. The system of claim 1, (Appendix 5) The providing unit is Providing the generated answer to the user 2. The system of claim 1, (Appendix 6) The providing unit is Automatically generate and display ads relevant to the user's questions 2. The system of claim 1, (Appendix 7) The reception unit is Create a dedicated interface for generative AI, allowing users to input weather-related questions 2. The system of claim 1, (Appendix 8) The generation unit is Generate detailed weather forecasts for weather questions 2. The system of claim 1, (Appendix 9) The reception unit is Create an interface within the messenger app that allows users to ask questions to the generative AI 2. The system of claim 1, (Appendix 10) The generation unit is Generate real-time answers to questions within messenger apps 2. The system of claim 1, (Appendix 11) The reception unit is The system estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The reception unit is Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1, (Appendix 13) The reception unit is When questions are asked, they are filtered based on the user's current interests 2. The system of claim 1, (Appendix 14) The reception unit is Estimate the user's emotions and prioritize questions to be accepted based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The reception unit is When accepting questions, prioritize questions that are more relevant to the user based on their geographic location 2. The system of claim 1, (Appendix 16) The reception unit is When a question is submitted, the system analyzes the user's social media activity and asks related questions. 2. The system of claim 1, (Appendix 17) The search unit is Infer user sentiment and adjust search wording based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The search unit is When searching, adjust search specificity based on the importance of the question 2. The system of claim 1, (Appendix 19) The search unit is When searching, apply different search algorithms depending on the question category 2. The system of claim 1, (Appendix 20) The search unit is Infer user sentiment and adjust the length of the search based on the estimated user sentiment 2. The system of claim 1, (Appendix 21) The search unit is When searching, prioritize searches based on when a question was submitted 2. The system of claim 1, (Appendix 22) The search unit is When searching, adjust search order based on question relevance 2. The system of claim 1, (Appendix 23) The generation unit is Infer user sentiment and adjust answer generation method based on the inferred user sentiment 2. The system of claim 1, (Appendix 24) The generation unit is When generating answers, adjust the detail of the answer based on the importance of the search result 2. The system of claim 1, (Appendix 25) The generation unit is When generating answers, different generation algorithms are applied depending on the question category. 2. The system of claim 1, (Appendix 26) The generation unit is Infer user sentiment and adjust the length of answers based on the estimated user sentiment 2. The system of claim 1, (Appendix 27) The generation unit is When generating answers, prioritize answers based on when the search results were submitted 2. The system of claim 1, (Appendix 28) The generation unit is When generating answers, adjust the order of answers based on the relevance of the search results 2. The system of claim 1, (Appendix 29) The providing unit is Inferring user sentiment and adjusting answer delivery based on the inferred user sentiment 2. The system of claim 1, (Appendix 30) The providing unit is When providing an answer, the system selects the most appropriate method by referring to the user's past question history. 2. The system of claim 1, (Appendix 31) The providing unit is When providing answers, tailor the offering based on the user's current areas of interest 2. The system of claim 1, (Appendix 32) The providing unit is Inferring user sentiment and prioritizing answers to provide based on the estimated user sentiment 2. The system of claim 1, (Appendix 33) The providing unit is When providing answers, the optimal method of providing them is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 34) The providing unit is Analyze your social media activity to customize your responses when you provide them 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A reception unit that receives questions from users; a search unit that analyzes the question received by the reception unit and searches for related information; A generator that generates an answer based on the information obtained by the search unit; a providing unit that provides the answer generated by the generating unit to a user. A system characterized by:

2. The reception unit is Provide an interface for users to input keywords 2. The system of claim 1 .

3. The search unit is Find information related to your question 2. The system of claim 1 .

4. The providing unit is Automatically generate and display ads relevant to the user's questions 2. The system of claim 1 .

5. The reception unit is Create an interface for the generative AI to allow users to input weather-related questions.

2. The system of claim 1 .

6. The generation unit is Generate detailed weather forecasts for weather questions 2. The system of claim 1 .

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