system

Abstract

The system according to the embodiment aims to automatically generate appropriate responses to in-house inquiries and improve the efficiency of information provision and inquiry response. [Solution] A system according to an embodiment includes a reception unit, an understanding unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a question. The understanding unit understands the question received by the reception unit. The analysis unit analyzes the question understood by the understanding unit. The generation unit generates an answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to provide appropriate answers to internal inquiries quickly, and there is room for improvement in efficiency.

[0005] The system according to the embodiment aims to automatically generate appropriate responses to in-house inquiries and improve the efficiency of information provision and inquiry response. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an understanding unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a question. The understanding unit understands the question received by the reception unit. The analysis unit analyzes the question understood by the understanding unit. The generation unit generates an answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate appropriate responses to internal inquiries, thereby improving the efficiency of information provision and inquiry response. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automated Q&A system according to an embodiment of the present invention is intended to be used as an internal sales inquiry tool. This system is designed to streamline internal inquiry responses. Specifically, it comprises the following steps: First, a question is accepted from an internal inquiry system. Next, artificial intelligence (AI) analyzes the question and automatically generates an appropriate answer. The generated answer is provided to the questioner. This mechanism improves the efficiency of inquiry responses. Furthermore, the development of AI-based personal assistants can also provide task support and information. For example, a question is accepted from an internal inquiry system. At this time, the question is entered in text format. For example, a question such as "How much does the new product cost?" is entered into an AI. Next, the AI ​​analyzes the entered question. The AI ​​understands the content of the question and generates an appropriate answer. For example, in response to the question "How much does the new product cost?", the AI ​​generates an answer such as "The price of the new product is 5,000 yen." The generated answer is provided to the questioner. For example, the AI-generated answer "The price of the new product is 5,000 yen" is displayed to the questioner. This allows the questioner to quickly obtain an answer. This system improves the efficiency of responding to inquiries. Because AI automatically generates answers, there is no need for staff to respond manually, saving time and effort. In addition, the development of AI-based personal assistants can also provide task support and information. For example, AI can manage schedules and set reminders, improving user work efficiency. In this way, the automatic Q&A system can improve the efficiency of responding to inquiries within the company and improve user work efficiency.

[0029] An automated Q&A system according to an embodiment includes a reception unit, an understanding unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions from an internal inquiry system. The questions are input, for example, in text format. For example, a question such as "What is the price of the new product?" is input. The understanding unit understands the questions received by the reception unit. The understanding unit analyzes the content of the questions using, for example, natural language processing technology and extracts keywords. The analysis unit analyzes the questions understood by the understanding unit. The analysis unit analyzes the intent of the questions using, for example, statistical analysis or a machine learning algorithm. The generation unit generates answers based on the questions analyzed by the analysis unit. The generation unit generates appropriate answers using, for example, a text generation AI (for example, LLM). The provision unit provides the answers generated by the generation unit. The provision unit displays the generated answers to the questioner, for example. In this way, the automated Q&A system according to an embodiment automates processes from receiving questions to providing answers, thereby achieving efficient inquiry responses. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit receives a question in text format and inputs it to an AI. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit analyzes the content of the question using natural language processing technology and extracts keywords. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit analyzes the intent of the question using statistical analysis or a machine learning algorithm. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit generates an appropriate answer using a text generation AI (e.g., LLM). Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit displays the generated answer to the questioner. As a result, the automated Q&A system according to the embodiment automates processes from receiving a question to providing an answer, thereby achieving efficient inquiry response.

[0030] The automated Q&A system further includes a support unit that provides task support. The support unit, for example, manages the user's schedule. For example, the support unit adds events to the user's calendar. The support unit can also set reminders. For example, the support unit can send reminders to the user at specific times. The support unit can also manage task progress. For example, the support unit tracks the progress of the user's tasks and records completed tasks. This allows the support unit to improve the user's work efficiency. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit inputs the user's schedule management into AI, and the AI ​​manages the schedule.

[0031] Furthermore, the automated Q&A system includes an information providing unit that provides information. The information providing unit provides information to the user in real time, for example. For example, the information providing unit provides the latest news to the user. The information providing unit can also provide information periodically. For example, the information providing unit provides information to the user at a specific time every day. The information providing unit can also provide information on a specific topic. For example, the information providing unit provides information on topics in which the user is interested. This allows the information providing unit to quickly provide the user with necessary information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit inputs the latest news for the user into AI, and the AI ​​provides the news.

[0032] The reception unit can analyze past inquiry history and select an appropriate reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. In this way, the optimal reception method can be selected by analyzing the past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the past inquiry history into AI, which selects the optimal reception method.

[0033] When receiving a question, the reception unit can filter the questions based on the user's current work situation and areas of interest. For example, the reception unit preferentially receives questions related to a project the user is currently working on. The reception unit can also filter and receive related questions based on the user's areas of interest. The reception unit can also automatically select and receive appropriate questions according to the user's work situation. In this way, by filtering questions based on the user's work situation and areas of interest, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's work situation and areas of interest into AI, and the AI ​​filters the questions.

[0034] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving questions related to that area. The reception unit can also filter and receive related questions based on the user's geographical location information. If the user is moving, the reception unit can also prioritize receiving questions related to the user's current location. This allows for more appropriate responses by prioritizing receiving highly relevant questions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit inputs the user's geographical location information into AI, which selects highly relevant questions.

[0035] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can preferentially receive questions related to topics in which the user has shown interest on social media. The reception unit can also filter and receive related questions from the user's social media activity. The reception unit can also receive questions based on keywords frequently mentioned by the user on social media. This allows for more appropriate responses by receiving related questions based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI, for example. For example, the reception unit inputs the user's social media activity into AI, which selects related questions.

[0036] When understanding a question, the understanding unit can adjust the level of detail of the understanding based on the importance of the question. For example, the understanding unit performs detailed understanding for a question with a high level of importance. The understanding unit can also perform concise understanding for a question with a low level of importance. The understanding unit can also dynamically adjust the level of detail of the understanding according to the importance of the question. This allows for more appropriate understanding by adjusting the level of detail of the understanding according to the importance of the question. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit inputs the importance of the question to AI, and the AI ​​adjusts the level of detail of the understanding.

[0037] When understanding a question, the understanding unit can apply an appropriate understanding algorithm depending on the category of the question. For example, the understanding unit applies a specialized understanding algorithm to a technical question. The understanding unit can also apply a general-purpose understanding algorithm to a general question. The understanding unit can also select and apply an optimal understanding algorithm depending on the category of the question. This enables more appropriate understanding by applying the optimal understanding algorithm depending on the category of the question. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit inputs the question category into AI, which selects the optimal understanding algorithm.

[0038] When understanding a question, the understanding unit can determine the priority of understanding based on the time when the question was submitted. For example, the understanding unit prioritizes understanding of recently submitted questions. The understanding unit can also postpone questions that were submitted earlier. The understanding unit can also dynamically adjust the priority of understanding depending on the time when the question was submitted. This enables more appropriate understanding by determining the priority of understanding based on the time when the question was submitted. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit inputs the time when the question was submitted into AI, and the AI ​​determines the priority of understanding.

[0039] When understanding questions, the understanding unit can adjust the order of understanding based on the relevance of the questions. For example, the understanding unit prioritizes understanding of questions with high relevance. The understanding unit can also postpone questions with low relevance. The understanding unit can also dynamically adjust the order of understanding according to the relevance of the questions. This allows for more appropriate understanding by adjusting the order of understanding based on the relevance of the questions. Some or all of the above-mentioned processing in the understanding unit may be performed using, or without, AI, for example. For example, the understanding unit inputs the relevance of the questions into AI, and the AI ​​adjusts the order of understanding.

[0040] When analyzing questions, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between questions. For example, the analysis unit analyzes the interrelationships between questions and groups related questions for analysis. The analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between questions. The analysis unit can also select and apply an optimal analysis method based on the interrelationships between questions. In this way, the accuracy of the analysis is improved by taking the interrelationships between questions into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the interrelationships between questions into AI, which selects the optimal analysis method.

[0041] When analyzing a question, the analysis unit can perform the analysis while taking into account the attribute information of the person who submitted the question. The analysis unit selects an appropriate analysis method based on, for example, the job title or department of the person who submitted the question. The analysis unit can also perform the analysis while taking into account the question submitter's past inquiry history. The analysis unit can also apply an optimal analysis method based on the attribute information of the person who submitted the question. This enables more appropriate analysis by taking into account the attribute information of the person who submitted the question. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit inputs the attribute information of the person who submitted the question into AI, and the AI ​​selects the optimal analysis method.

[0042] When analyzing questions, the analysis unit can perform the analysis taking into account the geographical distribution of the questions. For example, the analysis unit analyzes the geographical distribution of questions and performs the analysis taking into account the characteristics of each region. The analysis unit can also select the optimal analysis method based on the geographical distribution of questions. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of questions. This enables more appropriate analysis by taking into account the geographical distribution of questions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the geographical distribution of questions into AI, which selects the optimal analysis method.

[0043] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to literature related to the question. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the question. The analysis unit can also select an optimal analysis method based on literature related to the question. The analysis unit can also improve the accuracy of the analysis by taking literature related to the question into consideration. In this way, the accuracy of the analysis is improved by referring to literature related to the question. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs literature related to the question into AI, which selects the optimal analysis method.

[0044] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also dynamically adjust the level of detail of the answer according to the importance of the question. In this way, by adjusting the level of detail of the answer according to the importance of the question, a more appropriate answer is generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the importance of the question to AI, and the AI ​​adjusts the level of detail of the answer.

[0045] When generating an answer, the generation unit can apply an appropriate generation algorithm depending on the category of the question. For example, the generation unit applies a specialized generation algorithm to technical questions. The generation unit can also apply a general-purpose generation algorithm to general questions. The generation unit can also select and apply an optimal generation algorithm depending on the category of the question. In this way, by applying the optimal generation algorithm depending on the category of the question, a more appropriate answer is generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the question category into AI, which selects the optimal generation algorithm.

[0046] When generating answers, the generation unit can determine the priority of answers based on the time when the question was submitted. For example, the generation unit prioritizes generating answers for recently submitted questions. The generation unit can also postpone questions that were submitted earlier. The generation unit can also dynamically adjust the priority of answers depending on the time when the question was submitted. In this way, by determining the priority of answers based on the time when the question was submitted, more appropriate answers are generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the time when the question was submitted to AI, and the AI ​​determines the priority of answers.

[0047] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, the generation unit prioritizes generating answers for questions with high relevance. The generation unit can also postpone questions with low relevance. The generation unit can also dynamically adjust the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, more appropriate answers are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the relevance of questions into AI, and the AI ​​adjusts the order of answers.

[0048] When providing an answer, the providing unit can select an appropriate delivery method by referring to the user's past inquiry history. For example, the providing unit preferentially selects a delivery method that the user has frequently used in the past. The providing unit can also predict and select a specific delivery method from the user's past inquiry history. The providing unit can also analyze the user's past inquiry history and select the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past inquiry history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's past inquiry history into AI, which selects the optimal delivery method.

[0049] When providing an answer, the providing unit can customize the means of providing the answer based on the user's current work situation. For example, if the user is in a meeting, the providing unit can provide the answer in the form of a concise message. Also, if the user is doing desk work, the providing unit can provide the answer in the form of a detailed document. The providing unit can also select and customize the optimal means of providing the answer according to the user's work situation. This enables more appropriate provision by customizing the means of providing the answer according to the user's work situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's work situation into AI, which selects the optimal means of providing the answer.

[0050] When providing an answer, the providing unit can select an appropriate providing method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. The providing unit can also filter and provide related information based on the user's geographical location information. Furthermore, if the user is moving, the providing unit can prioritize providing information related to the user's current location. This enables more appropriate provision by selecting the optimal providing method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's geographical location information into AI, which selects the optimal providing method.

[0051] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means of providing the answer. For example, the providing unit can prioritize providing information related to topics in which the user has shown interest on social media. The providing unit can also filter and provide related information from the user's social media activity. The providing unit can also provide information based on keywords frequently mentioned by the user on social media. This enables more appropriate provision by suggesting a means of providing the answer based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit inputs the user's social media activity into AI, which then suggests the optimal means of providing the answer.

[0052] When assisting a task, the support unit can select an appropriate support method by referring to the user's past work history. For example, the support unit preferentially selects support methods that the user has frequently used in the past. The support unit can also predict and select a specific support method from the user's past work history. The support unit can also analyze the user's past work history and select the optimal support method. In this way, the optimal support method can be selected by referring to the user's past work history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit inputs the user's past work history into AI, which selects the optimal support method.

[0053] When assisting a task, the assistance unit can select an appropriate assistance method by taking into account the user's device information. For example, if the user is using a smartphone, the assistance unit can provide an assistance method that matches the screen size. Furthermore, if the user is using a tablet, the assistance unit can also provide an assistance method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the assistance unit can also provide an assistance method that is simple and highly visible. This enables more appropriate assistance by selecting the optimal assistance method based on the user's device information. Some or all of the above-described processing in the assistance unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance unit inputs the user's device information into AI, which then selects the optimal assistance method.

[0054] When providing information, the information providing unit can select an appropriate information providing method by referring to the user's past information acquisition history. For example, the information providing unit can prioritize providing information that the user frequently acquired in the past. The information providing unit can also predict and select a specific information providing method from the user's past information acquisition history. The information providing unit can also analyze the user's past information acquisition history and select an optimal information providing method. In this way, the optimal information providing method can be selected by referring to the user's past information acquisition history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit inputs the user's past information acquisition history into AI, which selects the optimal information providing method.

[0055] When providing information, the information providing unit can select an appropriate information providing method by taking into account the user's device information. For example, if the user is using a smartphone, the information providing unit can provide an information providing method that matches the screen size. Furthermore, if the user is using a tablet, the information providing unit can also provide an information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide an information providing method that is concise and highly visible. This enables more appropriate information provision by selecting the optimal information providing method based on the user's device information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit inputs the user's device information into AI, which then selects the optimal information providing method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the user's past inquiry history and automatically display frequently asked questions as candidates. For example, if the user has frequently asked the question "What is the price of the new product?" in the past, this question will automatically be displayed as a candidate the next time the user makes an inquiry. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. For example, if the user has made many voice inquiries in the past, voice input will be preferentially suggested the next time the user makes an inquiry. Furthermore, the reception unit can predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. In this way, the optimal reception method can be selected by analyzing the past inquiry history.

[0058] The support unit can dynamically adjust task priorities to improve the user's work efficiency. For example, the support unit can prioritize and display important meetings or tasks with upcoming deadlines based on the user's calendar. The support unit can also analyze the user's past task completion history and automatically suggest similar tasks. Furthermore, the support unit can track task progress in real time according to the user's work situation and send reminders as needed. This allows the support unit to improve the user's work efficiency.

[0059] The information providing unit can provide relevant information preferentially based on the user's areas of interest. For example, the information providing unit analyzes keywords searched by the user and pages viewed by the user in the past, and automatically suggests related news and articles. The information providing unit can also provide information periodically based on the user's areas of interest. For example, if the user is interested in technology-related news, the information providing unit can provide the latest technology news at a specific time every day. Furthermore, the information providing unit can collect user feedback and improve the quality of the information it provides. This allows the information providing unit to quickly provide the user with the information they need.

[0060] The reception unit can filter questions based on the user's current work situation and areas of interest. For example, questions related to a project the user is currently working on are preferentially received. The reception unit can also filter and receive related questions based on the user's areas of interest. Furthermore, the reception unit can automatically select and receive appropriate questions according to the user's work situation. In this way, by filtering questions based on the user's work situation and areas of interest, more relevant questions can be received.

[0061] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, questions related to that area are prioritized. Furthermore, related questions can be filtered and received based on the user's geographical location information. Furthermore, if the user is traveling, questions related to the user's current location can be prioritized. This allows for more appropriate responses by prioritizing the reception of highly relevant questions based on the user's geographical location information.

[0062] When analyzing questions, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between questions. For example, the analysis unit analyzes the interrelationships between questions and groups related questions for analysis. The analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between questions. Furthermore, the analysis unit can select and apply an optimal analysis method based on the interrelationships between questions. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between questions.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives a question from an internal inquiry system. The question is input, for example, in text format. For example, a question such as "What is the price of the new product?" is input. Step 2: The understanding unit understands the question received by the reception unit. The understanding unit analyzes the content of the question using, for example, natural language processing technology, and extracts keywords. Step 3: The analysis unit analyzes the question understood by the understanding unit. The analysis unit analyzes the intent of the question using, for example, statistical analysis or machine learning algorithms. Step 4: The generator generates an answer based on the question analyzed by the analyzer. The generator generates an appropriate answer using, for example, a text generation AI (e.g., LLM). Step 5: The providing unit provides the answer generated by the generating unit. For example, the providing unit displays the generated answer to the questioner.

[0065] (Example 2) The automated Q&A system according to an embodiment of the present invention is intended to be used as an internal sales inquiry tool. This system is designed to streamline internal inquiry responses. Specifically, it comprises the following steps: First, a question is accepted from an internal inquiry system. Next, artificial intelligence (AI) analyzes the question and automatically generates an appropriate answer. The generated answer is provided to the questioner. This mechanism improves the efficiency of inquiry responses. Furthermore, the development of AI-based personal assistants can also provide task support and information. For example, a question is accepted from an internal inquiry system. At this time, the question is entered in text format. For example, a question such as "How much does the new product cost?" is entered into an AI. Next, the AI ​​analyzes the entered question. The AI ​​understands the content of the question and generates an appropriate answer. For example, in response to the question "How much does the new product cost?", the AI ​​generates an answer such as "The price of the new product is 5,000 yen." The generated answer is provided to the questioner. For example, the AI-generated answer "The price of the new product is 5,000 yen" is displayed to the questioner. This allows the questioner to quickly obtain an answer. This system improves the efficiency of responding to inquiries. Because AI automatically generates answers, there is no need for staff to respond manually, saving time and effort. In addition, the development of AI-based personal assistants can also provide task support and information. For example, AI can manage schedules and set reminders, improving user work efficiency. In this way, the automatic Q&A system can improve the efficiency of responding to inquiries within the company and improve user work efficiency.

[0066] An automated Q&A system according to an embodiment includes a reception unit, an understanding unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions from an internal inquiry system. The questions are input, for example, in text format. For example, a question such as "What is the price of the new product?" is input. The understanding unit understands the questions received by the reception unit. The understanding unit analyzes the content of the questions using, for example, natural language processing technology and extracts keywords. The analysis unit analyzes the questions understood by the understanding unit. The analysis unit analyzes the intent of the questions using, for example, statistical analysis or a machine learning algorithm. The generation unit generates answers based on the questions analyzed by the analysis unit. The generation unit generates appropriate answers using, for example, a text generation AI (for example, LLM). The provision unit provides the answers generated by the generation unit. The provision unit displays the generated answers to the questioner, for example. In this way, the automated Q&A system according to an embodiment automates processes from receiving questions to providing answers, thereby achieving efficient inquiry responses. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit receives a question in text format and inputs it to an AI. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit analyzes the content of the question using natural language processing technology and extracts keywords. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit analyzes the intent of the question using statistical analysis or a machine learning algorithm. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit generates an appropriate answer using a text generation AI (e.g., LLM). Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit displays the generated answer to the questioner. As a result, the automated Q&A system according to the embodiment automates processes from receiving a question to providing an answer, thereby achieving efficient inquiry response.

[0067] The automated Q&A system further includes a support unit that provides task support. The support unit, for example, manages the user's schedule. For example, the support unit adds events to the user's calendar. The support unit can also set reminders. For example, the support unit can send reminders to the user at specific times. The support unit can also manage task progress. For example, the support unit tracks the progress of the user's tasks and records completed tasks. This allows the support unit to improve the user's work efficiency. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit inputs the user's schedule management into AI, and the AI ​​manages the schedule.

[0068] Furthermore, the automated Q&A system includes an information providing unit that provides information. The information providing unit provides information to the user in real time, for example. For example, the information providing unit provides the latest news to the user. The information providing unit can also provide information periodically. For example, the information providing unit provides information to the user at a specific time every day. The information providing unit can also provide information on a specific topic. For example, the information providing unit provides information on topics in which the user is interested. This allows the information providing unit to quickly provide the user with necessary information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit inputs the latest news for the user into AI, and the AI ​​provides the news.

[0069] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive questions and respond immediately. Furthermore, if the user is relaxed, the reception unit can receive questions at a normal pace and collect detailed information. Furthermore, if the user is in a hurry, the reception unit can provide a simple question reception form and quickly receive questions. This allows for more appropriate responses by adjusting the timing of question reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit inputs the user's emotions into an AI, which then estimates the emotions.

[0070] The reception unit can analyze past inquiry history and select an appropriate reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. In this way, the optimal reception method can be selected by analyzing the past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the past inquiry history into AI, which selects the optimal reception method.

[0071] When receiving a question, the reception unit can filter the questions based on the user's current work situation and areas of interest. For example, the reception unit preferentially receives questions related to a project the user is currently working on. The reception unit can also filter and receive related questions based on the user's areas of interest. The reception unit can also automatically select and receive appropriate questions according to the user's work situation. In this way, by filtering questions based on the user's work situation and areas of interest, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's work situation and areas of interest into AI, and the AI ​​filters the questions.

[0072] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize urgent questions. Furthermore, when the user is relaxed, the reception unit can also prioritize simple questions when the user is in a hurry. This allows for more appropriate responses by determining the priority of questions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit inputs the user's emotions into an AI, which then estimates the emotions.

[0073] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving questions related to that area. The reception unit can also filter and receive related questions based on the user's geographical location information. If the user is moving, the reception unit can also prioritize receiving questions related to the user's current location. This allows for more appropriate responses by prioritizing receiving highly relevant questions based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit inputs the user's geographical location information into AI, which selects highly relevant questions.

[0074] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit can preferentially receive questions related to topics in which the user has shown interest on social media. The reception unit can also filter and receive related questions from the user's social media activity. The reception unit can also receive questions based on keywords frequently mentioned by the user on social media. This allows for more appropriate responses by receiving related questions based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI, for example. For example, the reception unit inputs the user's social media activity into AI, which selects related questions.

[0075] The understanding unit can estimate the user's emotions and adjust the question understanding method based on the estimated user emotions. For example, if the user is stressed, the understanding unit can apply a concise and clear understanding method. Furthermore, if the user is relaxed, the understanding unit can also apply a detailed understanding method. Furthermore, if the user is in a hurry, the understanding unit can also apply a method that allows for quick understanding. This allows for more appropriate understanding by adjusting the question understanding method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the understanding unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the understanding unit inputs the user's emotions into an AI, and the AI ​​infers the emotions.

[0076] When understanding a question, the understanding unit can adjust the level of detail of the understanding based on the importance of the question. For example, the understanding unit performs detailed understanding for a question with a high level of importance. The understanding unit can also perform concise understanding for a question with a low level of importance. The understanding unit can also dynamically adjust the level of detail of the understanding according to the importance of the question. This allows for more appropriate understanding by adjusting the level of detail of the understanding according to the importance of the question. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit inputs the importance of the question to AI, and the AI ​​adjusts the level of detail of the understanding.

[0077] When understanding a question, the understanding unit can apply an appropriate understanding algorithm depending on the category of the question. For example, the understanding unit applies a specialized understanding algorithm to a technical question. The understanding unit can also apply a general-purpose understanding algorithm to a general question. The understanding unit can also select and apply an optimal understanding algorithm depending on the category of the question. This enables more appropriate understanding by applying the optimal understanding algorithm depending on the category of the question. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit inputs the question category into AI, which selects the optimal understanding algorithm.

[0078] The understanding unit can estimate the user's emotions and adjust the length of the understanding based on the estimated user emotions. For example, if the user is stressed, the understanding unit can provide a short, concise understanding. Furthermore, if the user is relaxed, the understanding unit can provide a detailed understanding so that the user can understand quickly. This allows for more appropriate understanding by adjusting the length of the understanding according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the understanding unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the understanding unit inputs the user's emotions into an AI, which then estimates the emotions.

[0079] When understanding a question, the understanding unit can determine the priority of understanding based on the time when the question was submitted. For example, the understanding unit prioritizes understanding of recently submitted questions. The understanding unit can also postpone questions that were submitted earlier. The understanding unit can also dynamically adjust the priority of understanding depending on the time when the question was submitted. This enables more appropriate understanding by determining the priority of understanding based on the time when the question was submitted. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit inputs the time when the question was submitted into AI, and the AI ​​determines the priority of understanding.

[0080] When understanding questions, the understanding unit can adjust the order of understanding based on the relevance of the questions. For example, the understanding unit prioritizes understanding of questions with high relevance. The understanding unit can also postpone questions with low relevance. The understanding unit can also dynamically adjust the order of understanding according to the relevance of the questions. This allows for more appropriate understanding by adjusting the order of understanding based on the relevance of the questions. Some or all of the above-mentioned processing in the understanding unit may be performed using, or without, AI, for example. For example, the understanding unit inputs the relevance of the questions into AI, and the AI ​​adjusts the order of understanding.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is stressed, the analysis unit applies concise and clear analysis criteria. Furthermore, if the user is relaxed, the analysis unit can also apply detailed analysis criteria. Furthermore, if the user is in a hurry, the analysis unit can also apply criteria that enable quick analysis. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit inputs the user's emotions into an AI, which then estimates the emotions.

[0082] When analyzing questions, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between questions. For example, the analysis unit analyzes the interrelationships between questions and groups related questions for analysis. The analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between questions. The analysis unit can also select and apply an optimal analysis method based on the interrelationships between questions. In this way, the accuracy of the analysis is improved by taking the interrelationships between questions into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the interrelationships between questions into AI, which selects the optimal analysis method.

[0083] When analyzing a question, the analysis unit can perform the analysis while taking into account the attribute information of the person who submitted the question. The analysis unit selects an appropriate analysis method based on, for example, the job title or department of the person who submitted the question. The analysis unit can also perform the analysis while taking into account the question submitter's past inquiry history. The analysis unit can also apply an optimal analysis method based on the attribute information of the person who submitted the question. This enables more appropriate analysis by taking into account the attribute information of the person who submitted the question. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit inputs the attribute information of the person who submitted the question into AI, and the AI ​​selects the optimal analysis method.

[0084] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important results. Furthermore, if the user is relaxed, the analysis unit can also display detailed results in an orderly manner. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying results that highlight the key points. This allows for more appropriate results to be displayed by adjusting the order in which the analysis results are displayed according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit inputs the user's emotions into an AI, which then estimates the emotions.

[0085] When analyzing questions, the analysis unit can perform the analysis taking into account the geographical distribution of the questions. For example, the analysis unit analyzes the geographical distribution of questions and performs the analysis taking into account the characteristics of each region. The analysis unit can also select the optimal analysis method based on the geographical distribution of questions. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of questions. This enables more appropriate analysis by taking into account the geographical distribution of questions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the geographical distribution of questions into AI, which selects the optimal analysis method.

[0086] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to literature related to the question. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the question. The analysis unit can also select an optimal analysis method based on literature related to the question. The analysis unit can also improve the accuracy of the analysis by taking literature related to the question into consideration. In this way, the accuracy of the analysis is improved by referring to literature related to the question. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs literature related to the question into AI, which selects the optimal analysis method.

[0087] The generation unit can estimate the user's emotions and adjust the answer generation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a concise and clear answer. Furthermore, if the user is relaxed, the generation unit can also generate a detailed answer so that the user can respond quickly. By adjusting the answer generation method according to the user's emotions, a more appropriate answer can be generated. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit inputs the user's emotions into an AI, which then estimates the emotions.

[0088] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also dynamically adjust the level of detail of the answer according to the importance of the question. In this way, by adjusting the level of detail of the answer according to the importance of the question, a more appropriate answer is generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the importance of the question to AI, and the AI ​​adjusts the level of detail of the answer.

[0089] When generating an answer, the generation unit can apply an appropriate generation algorithm depending on the category of the question. For example, the generation unit applies a specialized generation algorithm to technical questions. The generation unit can also apply a general-purpose generation algorithm to general questions. The generation unit can also select and apply an optimal generation algorithm depending on the category of the question. In this way, by applying the optimal generation algorithm depending on the category of the question, a more appropriate answer is generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the question category into AI, which selects the optimal generation algorithm.

[0090] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a short, to-the-point answer. Furthermore, if the user is relaxed, the generation unit can generate a longer answer with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a shortened answer so that the answer can be answered quickly. By adjusting the length of the answer according to the user's emotions, a more appropriate answer can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit inputs the user's emotions into an AI, which then estimates the emotions.

[0091] When generating answers, the generation unit can determine the priority of answers based on the time when the question was submitted. For example, the generation unit prioritizes generating answers for recently submitted questions. The generation unit can also postpone questions that were submitted earlier. The generation unit can also dynamically adjust the priority of answers depending on the time when the question was submitted. In this way, by determining the priority of answers based on the time when the question was submitted, more appropriate answers are generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the time when the question was submitted to AI, and the AI ​​determines the priority of answers.

[0092] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, the generation unit prioritizes generating answers for questions with high relevance. The generation unit can also postpone questions with low relevance. The generation unit can also dynamically adjust the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, more appropriate answers are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the relevance of questions into AI, and the AI ​​adjusts the order of answers.

[0093] The providing unit can estimate the user's emotions and adjust the answer providing method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit selects a concise and clear answer providing method. Furthermore, if the user is relaxed, the providing unit can select a method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can select a method that can provide the answer quickly. This allows for more appropriate answer providing by adjusting the answer providing method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit inputs the user's emotions into an AI, which then estimates the emotions.

[0094] When providing an answer, the providing unit can select an appropriate delivery method by referring to the user's past inquiry history. For example, the providing unit preferentially selects a delivery method that the user has frequently used in the past. The providing unit can also predict and select a specific delivery method from the user's past inquiry history. The providing unit can also analyze the user's past inquiry history and select the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the user's past inquiry history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's past inquiry history into AI, which selects the optimal delivery method.

[0095] When providing an answer, the providing unit can customize the means of providing the answer based on the user's current work situation. For example, if the user is in a meeting, the providing unit can provide the answer in the form of a concise message. Also, if the user is doing desk work, the providing unit can provide the answer in the form of a detailed document. The providing unit can also select and customize the optimal means of providing the answer according to the user's work situation. This enables more appropriate provision by customizing the means of providing the answer according to the user's work situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's work situation into AI, which selects the optimal means of providing the answer.

[0096] The providing unit can estimate the user's emotions and determine the priority of answer provision based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing answers with a high level of urgency. Furthermore, if the user is relaxed, the providing unit can also provide answers with a normal priority. Furthermore, if the user is in a hurry, the providing unit can also prioritize providing concise answers. This enables more appropriate answer provision by determining the priority of answer provision according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit inputs the user's emotions into an AI, which then estimates the emotions.

[0097] When providing an answer, the providing unit can select an appropriate providing method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. The providing unit can also filter and provide related information based on the user's geographical location information. Furthermore, if the user is moving, the providing unit can prioritize providing information related to the user's current location. This enables more appropriate provision by selecting the optimal providing method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's geographical location information into AI, which selects the optimal providing method.

[0098] When providing an answer, the providing unit can analyze the user's social media activity and suggest a means of providing the answer. For example, the providing unit can prioritize providing information related to topics in which the user has shown interest on social media. The providing unit can also filter and provide related information from the user's social media activity. The providing unit can also provide information based on keywords frequently mentioned by the user on social media. This enables more appropriate provision by suggesting a means of providing the answer based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit inputs the user's social media activity into AI, which then suggests the optimal means of providing the answer.

[0099] The support unit can estimate the user's emotions and adjust the task support method based on the estimated user emotions. For example, if the user is feeling stressed, the support unit can provide a concise and clear support method. Furthermore, if the user is relaxed, the support unit can provide a detailed support method. Furthermore, if the user is in a hurry, the support unit can provide a method that can provide quick support. This enables more appropriate support by adjusting the task support method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit inputs the user's emotions into an AI, which then estimates the emotions.

[0100] When assisting a task, the support unit can select an appropriate support method by referring to the user's past work history. For example, the support unit preferentially selects support methods that the user has frequently used in the past. The support unit can also predict and select a specific support method from the user's past work history. The support unit can also analyze the user's past work history and select the optimal support method. In this way, the optimal support method can be selected by referring to the user's past work history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit inputs the user's past work history into AI, which selects the optimal support method.

[0101] The support unit can estimate the user's emotions and determine the priority of task support based on the estimated user emotions. For example, when the user is feeling stressed, the support unit prioritizes support for tasks with a high level of urgency. Furthermore, when the user is relaxed, the support unit can also prioritize support for tasks with a normal priority. Furthermore, when the user is in a hurry, the support unit can also prioritize support for simple tasks. This enables more appropriate support by determining the priority of task support according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit inputs the user's emotions into an AI, which then estimates the emotions.

[0102] When assisting a task, the assistance unit can select an appropriate assistance method by taking into account the user's device information. For example, if the user is using a smartphone, the assistance unit can provide an assistance method that matches the screen size. Furthermore, if the user is using a tablet, the assistance unit can also provide an assistance method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the assistance unit can also provide an assistance method that is simple and highly visible. This enables more appropriate assistance by selecting the optimal assistance method based on the user's device information. Some or all of the above-described processing in the assistance unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance unit inputs the user's device information into AI, which then selects the optimal assistance method.

[0103] The information providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is feeling stressed, the information providing unit selects a concise and clear information providing method. Furthermore, if the user is relaxed, the information providing unit can select a providing method that includes detailed information. Furthermore, if the user is in a hurry, the information providing unit can select a method that can provide information quickly. This allows for more appropriate information provision by adjusting the information providing method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit inputs the user's emotions into an AI, which then estimates the emotions.

[0104] When providing information, the information providing unit can select an appropriate information providing method by referring to the user's past information acquisition history. For example, the information providing unit can prioritize providing information that the user frequently acquired in the past. The information providing unit can also predict and select a specific information providing method from the user's past information acquisition history. The information providing unit can also analyze the user's past information acquisition history and select an optimal information providing method. In this way, the optimal information providing method can be selected by referring to the user's past information acquisition history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit inputs the user's past information acquisition history into AI, which selects the optimal information providing method.

[0105] The information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is feeling stressed, the information providing unit can prioritize providing information with a high level of urgency. Furthermore, when the user is relaxed, the information providing unit can also provide information with a normal priority. Furthermore, when the user is in a hurry, the information providing unit can also prioritize providing concise information. This enables more appropriate information provision by determining the priority of information provision according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit inputs the user's emotions into an AI, which then estimates the emotions.

[0106] When providing information, the information providing unit can select an appropriate information providing method by taking into account the user's device information. For example, if the user is using a smartphone, the information providing unit can provide an information providing method that matches the screen size. Furthermore, if the user is using a tablet, the information providing unit can also provide an information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide an information providing method that is concise and highly visible. This enables more appropriate information provision by selecting the optimal information providing method based on the user's device information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit inputs the user's device information into AI, which then selects the optimal information providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, understanding unit, analysis unit, generation unit, provision unit, support unit, and information 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 receives a question via the reception device 38 of the smart device 14 and transmits it to the specific processing unit 290 of the data processing device 12. The understanding unit analyzes the question and extracts keywords via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the intent of the question via the specific processing unit 290 of the data processing device 12. The generation unit generates an appropriate answer via the specific processing unit 290 of the data processing device 12. The provision unit displays the generated answer to the questioner via the output device 40 of the smart device 14. The support unit manages the user's schedule and sets reminders via the control unit 46A of the smart device 14. The information provision unit provides the user with the latest news and information via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, understanding unit, analysis unit, generation unit, provision unit, support unit, and information 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 receives a question via the microphone 238 of the smart glasses 214 and transmits it to the specific processing unit 290 of the data processing device 12. The understanding unit analyzes the question and extracts keywords via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the intent of the question via the specific processing unit 290 of the data processing device 12. The generation unit generates an appropriate answer via the specific processing unit 290 of the data processing device 12. The provision unit provides the generated answer to the questioner via the speaker 240 of the smart glasses 214. The support unit manages the user's schedule and sets reminders via the control unit 46A of the smart glasses 214. The information provision unit provides the user with the latest news and information via the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, understanding unit, analysis unit, generation unit, provision unit, support unit, and information 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 receives a question via the microphone 238 of the headset type terminal 314 and transmits it to the identification processing unit 290 of the data processing device 12. The understanding unit analyzes the question and extracts keywords via the identification processing unit 290 of the data processing device 12. The analysis unit analyzes the intent of the question via the identification processing unit 290 of the data processing device 12. The generation unit generates an appropriate answer via the identification processing unit 290 of the data processing device 12. The provision unit displays the generated answer to the questioner via the display 343 of the headset type terminal 314. The support unit manages the user's schedule and sets reminders via the control unit 46A of the headset type terminal 314. The information provision unit provides the user with the latest news and information via the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, understanding unit, analysis unit, generation unit, provision unit, support unit, and information 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 receives a question via the microphone 238 of the robot 414 and transmits it to the specific processing unit 290 of the data processing device 12. The understanding unit analyzes the question and extracts keywords via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the intent of the question via the specific processing unit 290 of the data processing device 12. The generation unit generates an appropriate answer via the specific processing unit 290 of the data processing device 12. The provision unit provides the answer generated via the speaker 240 of the robot 414 to the questioner. The support unit manages the user's schedule and sets reminders via the control unit 46A of the robot 414. The information provision unit provides the user with the latest news and information via the speaker 240 of the robot 414.

[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 inquiry history and automatically display frequently asked questions as candidates. For example, if the user has frequently asked the question "What is the price of the new product?" in the past, this question will automatically be displayed as a candidate the next time the user makes an inquiry. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. For example, if the user has made many voice inquiries in the past, voice input will be preferentially suggested the next time the user makes an inquiry. Furthermore, the reception unit can predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. In this way, the optimal reception method can be selected by analyzing the past inquiry history.

[0109] The support unit can dynamically adjust task priorities to improve the user's work efficiency. For example, the support unit can prioritize and display important meetings or tasks with upcoming deadlines based on the user's calendar. The support unit can also analyze the user's past task completion history and automatically suggest similar tasks. Furthermore, the support unit can track task progress in real time according to the user's work situation and send reminders as needed. This allows the support unit to improve the user's work efficiency.

[0110] The information providing unit can provide relevant information preferentially based on the user's areas of interest. For example, the information providing unit analyzes keywords searched by the user and pages viewed by the user in the past, and automatically suggests related news and articles. The information providing unit can also provide information periodically based on the user's areas of interest. For example, if the user is interested in technology-related news, the information providing unit can provide the latest technology news at a specific time every day. Furthermore, the information providing unit can collect user feedback and improve the quality of the information it provides. This allows the information providing unit to quickly provide the user with the information they need.

[0111] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive questions and respond immediately. If the user is relaxed, the reception unit can receive questions at a normal pace and collect detailed information. Furthermore, if the user is in a hurry, the reception unit can provide a simple question reception form and quickly receive questions. This allows for more appropriate responses by adjusting the timing of receiving questions according to the user's emotions.

[0112] The reception unit can filter questions based on the user's current work situation and areas of interest. For example, questions related to a project the user is currently working on are preferentially received. The reception unit can also filter and receive related questions based on the user's areas of interest. Furthermore, the reception unit can automatically select and receive appropriate questions according to the user's work situation. In this way, by filtering questions based on the user's work situation and areas of interest, more relevant questions can be received.

[0113] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, questions related to that area are prioritized. Furthermore, related questions can be filtered and received based on the user's geographical location information. Furthermore, if the user is traveling, questions related to the user's current location can be prioritized. This allows for more appropriate responses by prioritizing the reception of highly relevant questions based on the user's geographical location information.

[0114] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user's emotions. For example, if the user is feeling stressed, questions with a high level of urgency can be received with priority. If the user is relaxed, questions can be received with normal priority. Furthermore, if the user is in a hurry, simple questions can be received with priority. In this way, by determining the priority of questions according to the user's emotions, more appropriate responses can be made.

[0115] The understanding unit can estimate the user's emotions and adjust the question understanding method based on the estimated user's emotions. For example, if the user is stressed, a concise and clear understanding method can be applied. If the user is relaxed, a detailed understanding method can be applied. Furthermore, if the user is in a hurry, a method that allows for quick understanding can be applied. In this way, adjusting the question understanding method according to the user's emotions enables more appropriate understanding.

[0116] When analyzing questions, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between questions. For example, the analysis unit analyzes the interrelationships between questions and groups related questions for analysis. The analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between questions. Furthermore, the analysis unit can select and apply an optimal analysis method based on the interrelationships between questions. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between questions.

[0117] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is feeling stressed, simple and clear analysis criteria can be applied. If the user is relaxed, detailed analysis criteria can be applied. Furthermore, if the user is in a hurry, criteria that allow for quick analysis can be applied. In this way, adjusting the analysis criteria according to the user's emotions enables more appropriate analysis.

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

[0119] Step 1: The reception unit receives a question from an internal inquiry system. The question is input, for example, in text format. For example, a question such as "What is the price of the new product?" is input. Step 2: The understanding unit understands the question received by the reception unit. The understanding unit analyzes the content of the question using, for example, natural language processing technology, and extracts keywords. Step 3: The analysis unit analyzes the question understood by the understanding unit. The analysis unit analyzes the intent of the question using, for example, statistical analysis or machine learning algorithms. Step 4: The generator generates an answer based on the question analyzed by the analyzer. The generator generates an appropriate answer using, for example, a text generation AI (e.g., LLM). Step 5: The providing unit provides the answer generated by the generating unit. For example, the providing unit displays the generated answer to the questioner.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

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

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

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

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

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

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

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

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

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

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

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

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

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

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

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

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

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

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

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

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

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0191] [Explanation of symbols]

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

Claims

1. a reception section for accepting questions; an understanding unit that understands the question received by the reception unit; an analysis unit that analyzes the question understood by the understanding unit; a generation unit that generates an answer based on the question analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit. A system characterized by:

2. Equipped with a support department to provide task support 2. The system of claim 1.

3. Equipped with an information providing section that provides information 2. The system of claim 1.

4. The reception unit The method for estimating the user's emotions and adjusting the timing of accepting questions based on the estimated user emotions is clearly shown.

2. The system of claim 1.

5. The reception unit Analyze past inquiry history and select the appropriate reception method 2. The system of claim 1.

6. The reception unit When accepting questions, provide specific ways to filter based on the user's current work situation and areas of interest.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize the questions to be accepted based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When accepting questions, clearly indicate the means to specifically prioritize relevant questions based on the user's geographic location information.

2. The system of claim 1.

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