system

Abstract

The system according to the embodiment aims to provide a personalized Q&A service by utilizing information on a personal identification card. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires information from a personal identification card. The analysis unit analyzes the information acquired by the acquisition unit. The generation unit generates a Q&A based on the information analyzed by the analysis unit. The provision unit provides the Q&A 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] Conventional technologies have not adequately utilized personal identification card information to provide personalized Q&A services, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized Q&A service by utilizing information on a personal identification card. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires information from a personal identification card. The analysis unit analyzes the information acquired by the acquisition unit. The generation unit generates a Q&A based on the information analyzed by the analysis unit. The provision unit provides the Q&A generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized Q&A service by utilizing information on a personal identification card. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention adds a generation AI function to a personal identification card reader used in hospitals, pharmacies, and medical facilities, and provides an optimal Q&A service based on the owner's attributes. In this system, the personal identification card reader reads the owner's personal identification card, and the generation AI analyzes the read information to identify the owner's attributes (e.g., age, gender, medical history, etc.). The system then provides an optimal Q&A service based on the identified attributes. For example, the owner inserts the personal identification card into the reader. At this time, the reader acquires information on the personal identification card, including the owner's age, gender, medical history, etc. This information is input into the generation AI. The generation AI then analyzes the input information. The generation AI identifies the owner's attributes and generates an optimal Q&A service based on those attributes. For example, the system may provide health care advice to the elderly and vaccination information to younger people. The generated Q&A service is then provided to the owner. For example, the service may be displayed on the reader's display or provided via audio guidance. This allows the owner to easily obtain the information most suited to them. This system improves services at hospitals, pharmacies, and medical facilities, increasing owner satisfaction. For example, elderly people can receive specific advice on health management, and younger people can understand the importance of vaccinations. In addition, because the generating AI provides information based on the owner's attributes, it is possible to provide personalized support and improve the quality of services. Furthermore, the generating AI can collect owner feedback and use it to improve services. For example, if the owner rates their satisfaction with the information provided, the generating AI can reflect this in future service provision. This allows for continuous improvement in the quality of services and further increases owner satisfaction.

[0029] A personal identification card reading system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires information from a personal identification card. Examples of personal identification cards include, but are not limited to, driver's licenses, employee ID cards, and student ID cards. The acquisition unit reads information from the personal identification card using, for example, a scanner. The acquisition unit can also acquire information using an RFID reader. The acquisition unit can also acquire an image of the personal identification card using a camera and extract information using image analysis technology. The analysis unit analyzes the information acquired by the acquisition unit using a generation AI. The analysis can be performed using, for example, database search, pattern recognition, or natural language processing technology, but is not limited to these examples. For example, the generation AI analyzes the acquired information using a text generation AI (e.g., LLM). The analysis unit can also analyze information from multiple modalities using a multimodal generation AI. The generation unit generates a Q&A based on the information analyzed by the analysis unit using the generation AI. The Q&A can be generated using, for example, template-based generation or a machine learning algorithm, but is not limited to these examples. For example, the generation AI generates an optimal Q&A based on the attributes of the owner. The generation unit can also generate a more appropriate Q&A by referring to past Q&A history. The provision unit provides the generated Q&A to the owner. The provision can be performed, for example, by displaying the Q&A on a display or by providing audio guidance, but is not limited to these examples. For example, the provision unit displays the Q&A on a smartphone screen. The provision unit can also provide the Q&A by audio guidance using a speaker. Furthermore, the provision unit can notify the Q&A through a mobile app. As a result, the personal identification card reading system according to the embodiment can provide optimal information to the owner by acquiring and analyzing information on a personal identification card, generating and providing a Q&A.

[0030] The acquisition unit can acquire information on the owner's age, gender, and medical history. The acquisition unit can acquire the information on the owner's age, gender, and medical history, for example, from a database. The acquisition unit can also collect the information on the owner's age, gender, and medical history through a questionnaire survey. For example, the acquisition unit can conduct an online survey to collect the information on the owner's age, gender, and medical history. The acquisition unit can also collect information using a paper-based questionnaire. In this way, by acquiring information such as the owner's age, gender, and medical history, more personalized services can be provided.

[0031] The analysis unit can analyze the acquired information and identify the attributes of the owner. For example, the analysis unit can analyze the acquired information using a database search and identify the attributes of the owner. The analysis unit can also analyze the information using pattern recognition technology and identify the attributes of the owner. For example, the analysis unit can identify attributes such as the owner's age, gender, occupation, and hobbies. The analysis unit can also analyze the information using natural language processing technology and identify the attributes of the owner. For example, the analysis unit can analyze the owner's text data and identify the attributes. By identifying the attributes of the owner in this way, more appropriate Q&As can be generated.

[0032] The generation unit can generate appropriate Q&As based on the identified attributes. For example, the generation unit generates customized Q&As based on the attributes of the owner. The generation unit can also generate more appropriate Q&As by referring to past Q&A history. For example, the generation unit generates Q&As related to health management based on the age, gender, and medical history of the owner. The generation unit can also generate related Q&As based on the owner's occupation and hobbies. For example, if the owner enjoys sports as a hobby, the generation unit provides health information related to sports. This makes it possible to provide appropriate information to the owner by generating optimal Q&As based on the identified attributes.

[0033] The providing unit can display the generated Q&A on a display or provide audio guidance. The providing unit, for example, displays the Q&A on a smartphone screen. The providing unit can also provide audio guidance of the Q&A using a speaker. For example, the providing unit displays the Q&A on a display so that the owner can visually confirm the information. The providing unit can also provide audio guidance of the Q&A so that the owner can audibly confirm the information. The providing unit can also notify the Q&A through a mobile app. For example, the providing unit sends a push notification to the owner's smartphone to provide the Q&A. This allows the owner to receive information by displaying the generated Q&A on a display or providing audio guidance.

[0034] The providing unit can collect feedback from owners and use it to improve the service. The providing unit collects feedback from owners through, for example, a questionnaire survey. The providing unit can also collect feedback through user interviews. For example, the providing unit conducts an online survey to evaluate the satisfaction of owners. The providing unit can also analyze usage logs to collect feedback from owners. For example, the providing unit analyzes the Q&A usage history of owners and uses it to improve the service. In this way, collecting feedback from owners and using it to improve the service improves the quality of the service.

[0035] The acquisition unit can analyze the owner's past medical history and select an appropriate information acquisition method. The acquisition unit can, for example, analyze electronic medical records and analyze the owner's past medical history. For example, the acquisition unit can collect information on the owner's past medical treatments and prescribed medications. The acquisition unit can also analyze the owner's medical history by referring to past medical records. For example, the acquisition unit can prioritize acquiring information on hospitals the owner has frequently visited in the past. The acquisition unit can also prioritize acquiring information related to specific illnesses from the owner's past medical history. Furthermore, the acquisition unit can select an optimal information acquisition method based on the owner's past medical history. For example, the acquisition unit can select an appropriate information acquisition means, such as a scanner, RFID reader, or camera, based on the owner's medical history. This allows the optimal information acquisition method to be selected by analyzing the past medical history. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or without AI.

[0036] When acquiring information from the personal identification card, the acquisition unit can filter the information based on the owner's current health condition and lifestyle habits. The acquisition unit, for example, refers to health checkup results to determine the owner's current health condition. For example, the acquisition unit collects data such as the owner's blood pressure, body temperature, and heart rate. The acquisition unit can also analyze a lifestyle questionnaire to determine the owner's lifestyle habits. For example, the acquisition unit collects information such as the owner's smoking, drinking, and exercise habits. This allows the acquisition unit to preferentially acquire information about medications the owner is currently taking. The acquisition unit can also filter relevant information based on the owner's lifestyle habits. Furthermore, the acquisition unit can filter information based on the owner's current health condition. For example, if the owner's blood pressure is high, the acquisition unit preferentially acquires information about blood pressure management. This allows more relevant information to be acquired by filtering information based on the owner's current health condition and lifestyle habits. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0037] When acquiring information from a personal identification card, the acquisition unit can select an appropriate acquisition means depending on the owner's input method. For example, when voice input is used, the acquisition unit acquires information using voice recognition technology. For example, when the owner inputs information by voice using a microphone, the acquisition unit converts the voice into text using voice recognition software. Furthermore, when text input is used, the acquisition unit can also acquire information using text analysis technology. For example, when the owner inputs information by text using a keyboard, the acquisition unit analyzes the information using text analysis software. Furthermore, when image input is used, the acquisition unit can also acquire information using image recognition technology. For example, when the owner inputs information by image using a camera, the acquisition unit analyzes the image using image recognition software and extracts the information. This improves the accuracy of information acquisition by selecting the optimal acquisition means depending on the input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI.

[0038] When acquiring information from a personal identification card, the acquisition unit can prioritize acquisition of highly relevant information based on the owner's geographical location information. The acquisition unit, for example, acquires the owner's geographical location information using GPS data. For example, the acquisition unit prioritizes acquisition of information on medical facilities in the owner's current area. The acquisition unit can also acquire the owner's geographical location information using a location information service. For example, the acquisition unit references location information from the owner's smartphone and prioritizes acquisition of information on nearby pharmacies. Furthermore, the acquisition unit can also prioritize acquisition of medical information specific to the area. For example, the acquisition unit acquires information on diseases that are prevalent in the area based on the owner's geographical location information. This makes it possible to prioritize acquisition of highly relevant information by taking the geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI.

[0039] When acquiring information from the personal identification card, the acquisition unit can analyze the owner's social media activities and acquire related information. The acquisition unit, for example, analyzes the content of social media posts to collect the owner's health information. For example, the acquisition unit acquires related medical information based on health information shared by the owner on social media. The acquisition unit can also understand the owner's social media activities through analysis of followers. For example, the acquisition unit acquires related medical information by referring to the activities of the owner's friends on social media. In this way, related information can be acquired by analyzing social media activities. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0040] When acquiring information from a personal identification card, the acquisition unit can customize the acquisition method by reflecting the owner's past feedback. The acquisition unit, for example, analyzes feedback data and customizes the acquisition method by reflecting the owner's past feedback. For example, the acquisition unit selects an optimal information acquisition method based on feedback provided by the owner in the past. The acquisition unit can also adjust the acquisition method using a customization algorithm. For example, the acquisition unit prioritizes acquiring specific information based on the owner's past feedback. Furthermore, the acquisition unit can customize the information acquisition procedure based on the feedback data. For example, the acquisition unit optimizes the information acquisition procedure by referring to the owner's past feedback. In this way, the acquisition method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the owner's medical history. The analysis unit, for example, evaluates the importance of the owner's medical history and adjusts the level of detail of the analysis. For example, the analysis unit performs a detailed analysis when the owner's medical history is important. The analysis unit can also perform a brief analysis when the owner's medical history is minor. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the priority of the owner's medical history. For example, the analysis unit calculates an importance score of the owner's medical history and sets the level of detail of the analysis based on the score. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the medical history. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the attributes of the owner. The analysis unit applies different analysis algorithms depending on attributes such as the owner's age, gender, occupation, and hobbies. For example, the analysis unit applies an analysis algorithm related to health management to elderly people. The analysis unit can also apply an analysis algorithm related to vaccinations to younger people. Furthermore, the analysis unit can apply the optimal analysis algorithm depending on the owner's attributes. For example, the analysis unit applies an algorithm based on the owner's age or gender. This makes it possible to provide more appropriate analysis results by applying different analysis algorithms depending on the attributes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the owner's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the owner's past analysis results. For example, the analysis unit refers to the owner's past analysis results and adjusts the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by using past data. For example, the analysis unit optimizes the analysis algorithm by referring to the owner's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0044] During analysis, the analysis unit can determine the priority of the analysis based on the submission date and time of the owner's medical history. The analysis unit determines the priority of the analysis based on, for example, the submission date and time of the owner's medical history. For example, the analysis unit prioritizes analysis if the owner's medical history has been submitted recently. The analysis unit can also postpone analysis if the owner's medical history is old. Furthermore, the analysis unit can determine the priority of the analysis based on the submission date and time of the owner's medical history. For example, the analysis unit determines the order of analysis using a prioritization algorithm based on the record of the submission date and time. In this way, by determining the priority of analysis based on the submission date and time, more important analyses can be performed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the owner's relevance. The analysis unit, for example, evaluates the relevance of the owner's medical history and adjusts the order of analysis. For example, the analysis unit prioritizes analysis when the owner's medical history is highly relevant. Furthermore, the analysis unit can postpone analysis when the owner's medical history is less relevant. Furthermore, the analysis unit can adjust the order of analysis based on the owner's relevance. For example, the analysis unit calculates a relevance score and sets the order of analysis based on the score. In this way, by adjusting the order of analysis based on relevance, more important analysis can be prioritized. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the owner's expertise. The analysis unit, for example, evaluates the owner's level of expertise and adjusts the use of technical terms in the analysis. For example, if the owner has specialized knowledge, the analysis unit uses a lot of technical terms. Furthermore, if the owner does not have specialized knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the owner's level of expertise. For example, the analysis unit selects terms based on an evaluation of the owner's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0047] When generating a Q&A, the generation unit can adjust the level of detail of the Q&A based on the importance of the owner's medical history. The generation unit, for example, evaluates the importance of the owner's medical history and adjusts the level of detail of the Q&A. For example, the generation unit generates a detailed Q&A if the owner's medical history is important. The generation unit can also generate a concise Q&A if the owner's medical history is minor. Furthermore, the generation unit can adjust the level of detail of the Q&A according to the importance of the owner's medical history. For example, the generation unit calculates an importance score of the owner's medical history and sets the level of detail of the Q&A based on the score. In this way, by adjusting the level of detail of the Q&A based on the importance of the medical history, more appropriate Q&A can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0048] When generating Q&As, the generation unit can apply different generation algorithms depending on the attributes of the owner. The generation unit applies different generation algorithms depending on attributes such as the owner's age, gender, occupation, and hobbies. For example, the generation unit applies an algorithm that generates Q&As about health management to elderly people. The generation unit can also apply an algorithm that generates Q&As about vaccinations to younger people. Furthermore, the generation unit can apply an algorithm that generates optimal Q&As depending on the attributes of the owner. For example, the generation unit applies an algorithm based on the owner's age or gender. In this way, by applying different generation algorithms depending on the attributes, more appropriate Q&As can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0049] When generating a Q&A, the generation unit can improve the accuracy of generation by referring to the owner's past Q&A results. The generation unit, for example, improves the accuracy of the current Q&A based on the owner's past Q&A results. For example, the generation unit refers to the owner's past Q&A results and adjusts the generation algorithm. The generation unit can also improve the accuracy of the Q&A by using past data. For example, the generation unit optimizes the generation algorithm by referring to the owner's past Q&A results. In this way, the accuracy of generation can be improved by referring to the past Q&A results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0050] When generating Q&As, the generation unit can determine the priority of the Q&As based on the submission date and time of the owner's medical history. The generation unit determines the priority of the Q&As based on, for example, the submission date and time of the owner's medical history. For example, the generation unit may prioritize generating Q&As if the owner's medical history was submitted recently. The generation unit may also postpone generating Q&As if the owner's medical history is older. Furthermore, the generation unit can determine the priority of the Q&As based on the submission date and time of the owner's medical history. For example, the generation unit may determine the order of Q&As using a prioritization algorithm based on the record of the submission date and time. In this way, by prioritizing Q&As based on the submission date and time, more important Q&As can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0051] When generating Q&As, the generation unit can adjust the order of Q&As based on the owner's relevance. The generation unit, for example, evaluates the relevance of the owner's medical history and adjusts the order of Q&As. For example, the generation unit prioritizes generating Q&As when the owner's medical history is highly relevant. The generation unit can also postpone generating Q&As when the owner's medical history is less relevant. Furthermore, the generation unit can adjust the order of Q&As based on the owner's relevance. For example, the generation unit calculates a relevance score and sets the order of Q&As based on the score. In this way, by adjusting the order of Q&As based on relevance, more important Q&As can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0052] When generating a Q&A, the generation unit can adjust the use of technical terms in the Q&A according to the owner's expertise. The generation unit, for example, evaluates the owner's level of expertise and adjusts the use of technical terms in the Q&A. For example, if the owner has specialized knowledge, the generation unit uses a lot of technical terms. Furthermore, if the owner does not have specialized knowledge, the generation unit can generate a Q&A using simple language. Furthermore, the generation unit can adjust the use of technical terms in the Q&A according to the owner's level of expertise. For example, the generation unit selects terms based on an evaluation of the owner's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, a Q&A that is easier to understand can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0053] When providing a Q&A, the providing unit can select an appropriate providing method by referring to the owner's past feedback. The providing unit, for example, analyzes feedback data and selects an appropriate providing method by referring to the owner's past feedback. For example, the providing unit selects an optimal Q&A providing method based on feedback provided by the owner in the past. The providing unit can also provide specific information preferentially based on the feedback data. Furthermore, the providing unit can customize the Q&A providing procedure based on the feedback data. For example, the providing unit optimizes the Q&A providing procedure by referring to the owner's past feedback. In this way, the optimal providing method can be selected by referring to the past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0054] When providing Q&As, the providing unit can customize the content to be provided according to the owner's current health condition. The providing unit, for example, refers to health checkup results to understand the owner's current health condition. For example, the providing unit collects data such as the owner's blood pressure, body temperature, and heart rate. The providing unit can also provide information on medications the owner is currently taking preferentially. Furthermore, the providing unit can customize Q&As based on the owner's current health condition. For example, if the owner's blood pressure is high, the providing unit provides Q&As related to blood pressure management. In this way, by customizing the content to be provided according to the owner's current health condition, more appropriate Q&As can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0055] The providing unit can improve the provision method by reflecting the owner's feedback when providing a Q&A. The providing unit, for example, analyzes the feedback data and improves the provision method by reflecting the owner's feedback. For example, the providing unit evaluates the owner's satisfaction with the provided Q&A and improves the provision method based on the evaluation. The providing unit can also improve the Q&A provision procedure based on the feedback data. Furthermore, the providing unit can also reflect the feedback data in providing Q&As from the next time onwards. For example, the providing unit collects the owner's feedback and reflects it in providing Q&As from the next time onwards. In this way, the provision method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0056] When providing Q&A, the providing unit can select an appropriate provision method based on the owner's geographical location information. The providing unit, for example, acquires the owner's geographical location information using GPS data. For example, the providing unit prioritizes providing information about medical facilities in the owner's current area. The providing unit can also acquire the owner's geographical location information using a location information service. For example, the providing unit references the location information of the owner's smartphone and prioritizes providing information about nearby pharmacies. Furthermore, the providing unit can also prioritize providing medical information specific to the area. For example, the providing unit provides information about diseases that are prevalent in the area based on the owner's geographical location information. This makes it possible to select the optimal provision method by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI.

[0057] When providing Q&As, the providing unit can analyze the owner's social media activities and provide related information. The providing unit, for example, analyzes the content of social media posts and collects the owner's health information. For example, the providing unit provides related medical information based on the health information the owner shared on social media. The providing unit can also understand the owner's social media activities through analysis of followers. For example, the providing unit provides related medical information by referring to the activities of the owner's friends on social media. In this way, related information can be provided by analyzing social media activities. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI.

[0058] When providing a Q&A, the providing unit can customize the providing method by reflecting the owner's past feedback. The providing unit, for example, analyzes feedback data and customizes the providing method by reflecting the owner's past feedback. For example, the providing unit selects the optimal Q&A providing method based on feedback provided by the owner in the past. The providing unit can also provide specific information preferentially based on the feedback data. Furthermore, the providing unit can customize the Q&A providing procedure based on the feedback data. For example, the providing unit optimizes the Q&A providing procedure by referring to the owner's past feedback. In this way, the providing method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

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

[0060] The acquisition unit can monitor the owner's health condition in real time and issue a warning if an abnormality is detected. For example, the acquisition unit can continuously monitor the owner's heart rate and blood pressure and issue an alert if an abnormal value is detected. The acquisition unit can also monitor the owner's body temperature and issue a warning if a fever is detected. Furthermore, the acquisition unit can monitor the owner's respiratory rate and issue a warning if an abnormal breathing pattern is detected. This allows the owner's health condition to be monitored in real time and allows for quick response if an abnormality occurs.

[0061] The generator can analyze the owner's past Q&A history and customize Q&As based on the owner's preferences. For example, the generator can analyze patterns of Q&As that the owner has liked to view in the past and generate similar Q&As. The generator can also analyze patterns of Q&As that the owner has avoided in the past and generate Q&As that avoid those patterns. Furthermore, the generator can adjust the content of Q&As based on the owner's feedback. This makes it possible to provide Q&As customized based on the owner's preferences.

[0062] The acquisition unit can provide region-specific medical information by utilizing the owner's geographical location information. For example, the acquisition unit can preferentially acquire information about medical facilities in the area where the owner is currently located. The acquisition unit can also acquire information about diseases that are prevalent in the area. Furthermore, the acquisition unit can provide information about medical resources in the area. This makes it possible to provide medical information that is highly relevant to the owner by taking the geographical location information into consideration.

[0063] The generation unit can generate Q&A about preventive medicine based on the owner's medical history. For example, the generation unit can analyze the owner's past medical records and provide information about vaccinations. The generation unit can also provide information about genetic risks by taking into account the owner's family history. Furthermore, the generation unit can provide information about preventing lifestyle-related diseases based on the owner's lifestyle. This makes it possible to support the owner's health management by providing Q&A about preventive medicine based on the medical history.

[0064] The acquisition unit can analyze the owner's social media activity and provide relevant medical information. For example, the acquisition unit can acquire relevant medical information based on health information shared by the owner on social media. The acquisition unit can also analyze the activity of the owner's followers and provide relevant medical information. Furthermore, the acquisition unit can provide relevant medical information by referring to the activity of the owner's friends on social media. In this way, by analyzing social media activity, it is possible to provide medical information that is highly relevant to the owner.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The acquisition unit acquires information from a personal identification card. Personal identification cards include driver's licenses, employee ID cards, student ID cards, etc. The acquisition unit can acquire information using a scanner, RFID reader, or camera. When using a camera, image analysis technology is used to extract information. Step 2: The analysis unit uses the generation AI to analyze the information acquired by the acquisition unit. The analysis is performed using database search, pattern recognition, and natural language processing techniques. The generation AI analyzes the information using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The generation unit uses the generation AI to generate Q&As based on the information analyzed by the analysis unit. Q&As are generated using template-based generation or machine learning algorithms. The generation AI generates optimal Q&As based on the owner's attributes and can also generate more appropriate Q&As by referring to past Q&A history. Step 4: The provider provides the generated Q&A to the owner. The provision can be done by displaying it on a screen, providing audio guidance, or notifying the owner through a mobile app. For example, the Q&A can be displayed on a smartphone screen or provided via audio guidance using a speaker.

[0067] (Example 2) A system according to an embodiment of the present invention adds a generation AI function to a personal identification card reader used in hospitals, pharmacies, and medical facilities, and provides an optimal Q&A service based on the owner's attributes. In this system, the personal identification card reader reads the owner's personal identification card, and the generation AI analyzes the read information to identify the owner's attributes (e.g., age, gender, medical history, etc.). The system then provides an optimal Q&A service based on the identified attributes. For example, the owner inserts the personal identification card into the reader. At this time, the reader acquires information on the personal identification card, including the owner's age, gender, medical history, etc. This information is input into the generation AI. The generation AI then analyzes the input information. The generation AI identifies the owner's attributes and generates an optimal Q&A service based on those attributes. For example, the system may provide health care advice to the elderly and vaccination information to younger people. The generated Q&A service is then provided to the owner. For example, the service may be displayed on the reader's display or provided via audio guidance. This allows the owner to easily obtain the information most suited to them. This system improves services at hospitals, pharmacies, and medical facilities, increasing owner satisfaction. For example, elderly people can receive specific advice on health management, and younger people can understand the importance of vaccinations. In addition, because the generating AI provides information based on the owner's attributes, it is possible to provide personalized support and improve the quality of services. Furthermore, the generating AI can collect owner feedback and use it to improve services. For example, if the owner rates their satisfaction with the information provided, the generating AI can reflect this in future service provision. This allows for continuous improvement in the quality of services and further increases owner satisfaction.

[0068] A personal identification card reading system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires information from a personal identification card. Examples of personal identification cards include, but are not limited to, driver's licenses, employee ID cards, and student ID cards. The acquisition unit reads information from the personal identification card using, for example, a scanner. The acquisition unit can also acquire information using an RFID reader. The acquisition unit can also acquire an image of the personal identification card using a camera and extract information using image analysis technology. The analysis unit analyzes the information acquired by the acquisition unit using a generation AI. The analysis can be performed using, for example, database search, pattern recognition, or natural language processing technology, but is not limited to these examples. For example, the generation AI analyzes the acquired information using a text generation AI (e.g., LLM). The analysis unit can also analyze information from multiple modalities using a multimodal generation AI. The generation unit generates a Q&A based on the information analyzed by the analysis unit using the generation AI. The Q&A can be generated using, for example, template-based generation or a machine learning algorithm, but is not limited to these examples. For example, the generation AI generates an optimal Q&A based on the attributes of the owner. The generation unit can also generate a more appropriate Q&A by referring to past Q&A history. The provision unit provides the generated Q&A to the owner. The provision can be performed, for example, by displaying the Q&A on a display or by providing audio guidance, but is not limited to these examples. For example, the provision unit displays the Q&A on a smartphone screen. The provision unit can also provide the Q&A by audio guidance using a speaker. Furthermore, the provision unit can notify the Q&A through a mobile app. As a result, the personal identification card reading system according to the embodiment can provide optimal information to the owner by acquiring and analyzing information on a personal identification card, generating and providing a Q&A.

[0069] The acquisition unit can acquire information on the owner's age, gender, and medical history. The acquisition unit can acquire the information on the owner's age, gender, and medical history, for example, from a database. The acquisition unit can also collect the information on the owner's age, gender, and medical history through a questionnaire survey. For example, the acquisition unit can conduct an online survey to collect the information on the owner's age, gender, and medical history. The acquisition unit can also collect information using a paper-based questionnaire. In this way, by acquiring information such as the owner's age, gender, and medical history, more personalized services can be provided.

[0070] The analysis unit can analyze the acquired information and identify the attributes of the owner. For example, the analysis unit can analyze the acquired information using a database search and identify the attributes of the owner. The analysis unit can also analyze the information using pattern recognition technology and identify the attributes of the owner. For example, the analysis unit can identify attributes such as the owner's age, gender, occupation, and hobbies. The analysis unit can also analyze the information using natural language processing technology and identify the attributes of the owner. For example, the analysis unit can analyze the owner's text data and identify the attributes. By identifying the attributes of the owner in this way, more appropriate Q&As can be generated.

[0071] The generation unit can generate appropriate Q&As based on the identified attributes. For example, the generation unit generates customized Q&As based on the attributes of the owner. The generation unit can also generate more appropriate Q&As by referring to past Q&A history. For example, the generation unit generates Q&As related to health management based on the age, gender, and medical history of the owner. The generation unit can also generate related Q&As based on the owner's occupation and hobbies. For example, if the owner enjoys sports as a hobby, the generation unit provides health information related to sports. This makes it possible to provide appropriate information to the owner by generating optimal Q&As based on the identified attributes.

[0072] The providing unit can display the generated Q&A on a display or provide audio guidance. The providing unit, for example, displays the Q&A on a smartphone screen. The providing unit can also provide audio guidance of the Q&A using a speaker. For example, the providing unit displays the Q&A on a display so that the owner can visually confirm the information. The providing unit can also provide audio guidance of the Q&A so that the owner can audibly confirm the information. The providing unit can also notify the Q&A through a mobile app. For example, the providing unit sends a push notification to the owner's smartphone to provide the Q&A. This allows the owner to receive information by displaying the generated Q&A on a display or providing audio guidance.

[0073] The providing unit can collect feedback from owners and use it to improve the service. The providing unit collects feedback from owners through, for example, a questionnaire survey. The providing unit can also collect feedback through user interviews. For example, the providing unit conducts an online survey to evaluate the satisfaction of owners. The providing unit can also analyze usage logs to collect feedback from owners. For example, the providing unit analyzes the Q&A usage history of owners and uses it to improve the service. In this way, collecting feedback from owners and using it to improve the service improves the quality of the service.

[0074] The acquisition unit can estimate the owner's emotions and adjust the timing of information acquisition from the personal identification card based on the estimated emotions. The acquisition unit estimates the owner's emotions using, for example, facial expression recognition technology. For example, the acquisition unit analyzes the owner's facial expression data to determine whether the owner is relaxed or nervous. The acquisition unit can also estimate emotions using voice analysis technology. For example, the acquisition unit analyzes the owner's tone and speed of voice to estimate emotions. The acquisition unit can also estimate emotions using text analysis technology. For example, the acquisition unit analyzes the owner's text messages to estimate emotions. As a result, if the owner is relaxed, information on the personal identification card can be acquired immediately. If the owner is nervous, information can be acquired after a short delay. Furthermore, if the owner is in a hurry, information can be acquired quickly. As a result, by adjusting the information acquisition timing according to the owner's emotions, information can be acquired at a more appropriate time. Emotion estimation is realized using an emotion estimation function using, 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.

[0075] The acquisition unit can analyze the owner's past medical history and select an appropriate information acquisition method. The acquisition unit can, for example, analyze electronic medical records and analyze the owner's past medical history. For example, the acquisition unit can collect information on the owner's past medical treatments and prescribed medications. The acquisition unit can also analyze the owner's medical history by referring to past medical records. For example, the acquisition unit can prioritize acquiring information on hospitals the owner has frequently visited in the past. The acquisition unit can also prioritize acquiring information related to specific illnesses from the owner's past medical history. Furthermore, the acquisition unit can select an optimal information acquisition method based on the owner's past medical history. For example, the acquisition unit can select an appropriate information acquisition means, such as a scanner, RFID reader, or camera, based on the owner's medical history. This allows the optimal information acquisition method to be selected by analyzing the past medical history. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or without AI.

[0076] When acquiring information from the personal identification card, the acquisition unit can filter the information based on the owner's current health condition and lifestyle habits. The acquisition unit, for example, refers to health checkup results to determine the owner's current health condition. For example, the acquisition unit collects data such as the owner's blood pressure, body temperature, and heart rate. The acquisition unit can also analyze a lifestyle questionnaire to determine the owner's lifestyle habits. For example, the acquisition unit collects information such as the owner's smoking, drinking, and exercise habits. This allows the acquisition unit to preferentially acquire information about medications the owner is currently taking. The acquisition unit can also filter relevant information based on the owner's lifestyle habits. Furthermore, the acquisition unit can filter information based on the owner's current health condition. For example, if the owner's blood pressure is high, the acquisition unit preferentially acquires information about blood pressure management. This allows more relevant information to be acquired by filtering information based on the owner's current health condition and lifestyle habits. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0077] When acquiring information from a personal identification card, the acquisition unit can select an appropriate acquisition means depending on the owner's input method. For example, when voice input is used, the acquisition unit acquires information using voice recognition technology. For example, when the owner inputs information by voice using a microphone, the acquisition unit converts the voice into text using voice recognition software. Furthermore, when text input is used, the acquisition unit can also acquire information using text analysis technology. For example, when the owner inputs information by text using a keyboard, the acquisition unit analyzes the information using text analysis software. Furthermore, when image input is used, the acquisition unit can also acquire information using image recognition technology. For example, when the owner inputs information by image using a camera, the acquisition unit analyzes the image using image recognition software and extracts the information. This improves the accuracy of information acquisition by selecting the optimal acquisition means depending on the input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI.

[0078] The acquisition unit can estimate the owner's emotions and determine the priority of information to be acquired based on the estimated emotions. The acquisition unit can estimate the owner's emotions using, for example, facial expression recognition technology. For example, the acquisition unit can analyze the owner's facial expression data to determine whether the owner is relaxed or nervous. The acquisition unit can also estimate emotions using voice analysis technology. For example, the acquisition unit can analyze the tone and speed of the owner's voice to estimate emotions. The acquisition unit can also estimate emotions using text analysis technology. For example, the acquisition unit can analyze the owner's text messages to estimate emotions. As a result, if the owner is relaxed, detailed medical information can be prioritized. Also, if the owner is nervous, concise medical information can be prioritized. Furthermore, if the owner is in a hurry, the most important medical information can be prioritized. As a result, by prioritizing information based on emotions, more important information can be prioritized. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] When acquiring information from a personal identification card, the acquisition unit can prioritize acquisition of highly relevant information based on the owner's geographical location information. The acquisition unit, for example, acquires the owner's geographical location information using GPS data. For example, the acquisition unit prioritizes acquisition of information on medical facilities in the owner's current area. The acquisition unit can also acquire the owner's geographical location information using a location information service. For example, the acquisition unit references location information from the owner's smartphone and prioritizes acquisition of information on nearby pharmacies. Furthermore, the acquisition unit can also prioritize acquisition of medical information specific to the area. For example, the acquisition unit acquires information on diseases that are prevalent in the area based on the owner's geographical location information. This makes it possible to prioritize acquisition of highly relevant information by taking the geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI.

[0080] When acquiring information from the personal identification card, the acquisition unit can analyze the owner's social media activities and acquire related information. The acquisition unit, for example, analyzes the content of social media posts to collect the owner's health information. For example, the acquisition unit acquires related medical information based on health information shared by the owner on social media. The acquisition unit can also understand the owner's social media activities through analysis of followers. For example, the acquisition unit acquires related medical information by referring to the activities of the owner's friends on social media. In this way, related information can be acquired by analyzing social media activities. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI.

[0081] When acquiring information from a personal identification card, the acquisition unit can customize the acquisition method by reflecting the owner's past feedback. The acquisition unit, for example, analyzes feedback data and customizes the acquisition method by reflecting the owner's past feedback. For example, the acquisition unit selects an optimal information acquisition method based on feedback provided by the owner in the past. The acquisition unit can also adjust the acquisition method using a customization algorithm. For example, the acquisition unit prioritizes acquiring specific information based on the owner's past feedback. Furthermore, the acquisition unit can customize the information acquisition procedure based on the feedback data. For example, the acquisition unit optimizes the information acquisition procedure by referring to the owner's past feedback. In this way, the acquisition method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The analysis unit can estimate the owner's emotions and adjust the way the analysis is presented based on the estimated emotions. The analysis unit can estimate the owner's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the owner's facial expression data to determine whether the owner is relaxed or nervous. The analysis unit can also estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the owner's voice to estimate emotions. The analysis unit can also estimate emotions using text analysis technology. For example, the analysis unit can analyze the owner's text messages to estimate emotions. As a result, if the owner is relaxed, a detailed analysis result can be provided. If the owner is nervous, a concise analysis result can be provided. If the owner is in a hurry, a concise analysis result can be provided. As a result, by adjusting the way the analysis is presented based on emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the owner's medical history. The analysis unit, for example, evaluates the importance of the owner's medical history and adjusts the level of detail of the analysis. For example, the analysis unit performs a detailed analysis when the owner's medical history is important. The analysis unit can also perform a brief analysis when the owner's medical history is minor. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the priority of the owner's medical history. For example, the analysis unit calculates an importance score of the owner's medical history and sets the level of detail of the analysis based on the score. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the medical history. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the attributes of the owner. The analysis unit applies different analysis algorithms depending on attributes such as the owner's age, gender, occupation, and hobbies. For example, the analysis unit applies an analysis algorithm related to health management to elderly people. The analysis unit can also apply an analysis algorithm related to vaccinations to younger people. Furthermore, the analysis unit can apply the optimal analysis algorithm depending on the owner's attributes. For example, the analysis unit applies an algorithm based on the owner's age or gender. This makes it possible to provide more appropriate analysis results by applying different analysis algorithms depending on the attributes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the owner's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the owner's past analysis results. For example, the analysis unit refers to the owner's past analysis results and adjusts the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by using past data. For example, the analysis unit optimizes the analysis algorithm by referring to the owner's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0086] The analysis unit can estimate the owner's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can estimate the owner's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the owner's facial expression data to determine whether the owner is relaxed or nervous. The analysis unit can also estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the owner's voice to estimate emotions. The analysis unit can also estimate emotions using text analysis technology. For example, the analysis unit can analyze the owner's text messages to estimate emotions. As a result, if the owner is relaxed, a detailed analysis can be performed. If the owner is nervous, a concise analysis can be performed. Furthermore, if the owner is in a hurry, an analysis that focuses on the main points can be performed. As a result, by adjusting the length of the analysis based on emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] During analysis, the analysis unit can determine the priority of the analysis based on the submission date and time of the owner's medical history. The analysis unit determines the priority of the analysis based on, for example, the submission date and time of the owner's medical history. For example, the analysis unit prioritizes analysis if the owner's medical history has been submitted recently. The analysis unit can also postpone analysis if the owner's medical history is old. Furthermore, the analysis unit can determine the priority of the analysis based on the submission date and time of the owner's medical history. For example, the analysis unit determines the order of analysis using a prioritization algorithm based on the record of the submission date and time. In this way, by determining the priority of analysis based on the submission date and time, more important analyses can be performed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the owner's relevance. The analysis unit, for example, evaluates the relevance of the owner's medical history and adjusts the order of analysis. For example, the analysis unit prioritizes analysis when the owner's medical history is highly relevant. Furthermore, the analysis unit can postpone analysis when the owner's medical history is less relevant. Furthermore, the analysis unit can adjust the order of analysis based on the owner's relevance. For example, the analysis unit calculates a relevance score and sets the order of analysis based on the score. In this way, by adjusting the order of analysis based on relevance, more important analysis can be prioritized. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the owner's expertise. The analysis unit, for example, evaluates the owner's level of expertise and adjusts the use of technical terms in the analysis. For example, if the owner has specialized knowledge, the analysis unit uses a lot of technical terms. Furthermore, if the owner does not have specialized knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the owner's level of expertise. For example, the analysis unit selects terms based on an evaluation of the owner's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0090] The generation unit can estimate the owner's emotions and adjust the Q&A generation method based on the estimated emotions. The generation unit can estimate the owner's emotions using, for example, facial expression recognition technology. For example, the generation unit can analyze the owner's facial expression data to determine whether the owner is relaxed or nervous. The generation unit can also estimate emotions using voice analysis technology. For example, the generation unit can analyze the owner's tone and speed of voice to estimate emotions. The generation unit can also estimate emotions using text analysis technology. For example, the generation unit can analyze the owner's text messages to estimate emotions. As a result, if the owner is relaxed, a detailed Q&A can be generated. If the owner is nervous, a concise Q&A can be generated. If the owner is in a hurry, a Q&A that focuses on the main points can be generated. This allows the Q&A generation method to be adjusted based on emotions, thereby providing more appropriate Q&As. Emotion estimation is achieved 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.

[0091] When generating a Q&A, the generation unit can adjust the level of detail of the Q&A based on the importance of the owner's medical history. The generation unit, for example, evaluates the importance of the owner's medical history and adjusts the level of detail of the Q&A. For example, the generation unit generates a detailed Q&A if the owner's medical history is important. The generation unit can also generate a concise Q&A if the owner's medical history is minor. Furthermore, the generation unit can adjust the level of detail of the Q&A according to the importance of the owner's medical history. For example, the generation unit calculates an importance score of the owner's medical history and sets the level of detail of the Q&A based on the score. In this way, by adjusting the level of detail of the Q&A based on the importance of the medical history, more appropriate Q&A can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0092] When generating Q&As, the generation unit can apply different generation algorithms depending on the attributes of the owner. The generation unit applies different generation algorithms depending on attributes such as the owner's age, gender, occupation, and hobbies. For example, the generation unit applies an algorithm that generates Q&As about health management to elderly people. The generation unit can also apply an algorithm that generates Q&As about vaccinations to younger people. Furthermore, the generation unit can apply an algorithm that generates optimal Q&As depending on the attributes of the owner. For example, the generation unit applies an algorithm based on the owner's age or gender. In this way, by applying different generation algorithms depending on the attributes, more appropriate Q&As can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0093] When generating a Q&A, the generation unit can improve the accuracy of generation by referring to the owner's past Q&A results. The generation unit, for example, improves the accuracy of the current Q&A based on the owner's past Q&A results. For example, the generation unit refers to the owner's past Q&A results and adjusts the generation algorithm. The generation unit can also improve the accuracy of the Q&A by using past data. For example, the generation unit optimizes the generation algorithm by referring to the owner's past Q&A results. In this way, the accuracy of generation can be improved by referring to the past Q&A results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0094] The generation unit can estimate the owner's emotions and adjust the length of the Q&A based on the estimated emotions. The generation unit can estimate the owner's emotions using, for example, facial expression recognition technology. For example, the generation unit can analyze the owner's facial expression data to determine whether the owner is relaxed or nervous. The generation unit can also estimate emotions using voice analysis technology. For example, the generation unit can analyze the owner's tone and speed of voice to estimate emotions. The generation unit can also estimate emotions using text analysis technology. For example, the generation unit can analyze the owner's text messages to estimate emotions. As a result, if the owner is relaxed, a detailed Q&A can be generated. If the owner is nervous, a concise Q&A can be generated. If the owner is in a hurry, a Q&A that focuses on the main points can be generated. This allows the length of the Q&A to be adjusted based on emotions, thereby providing a more appropriate Q&A. Emotion estimation is achieved 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.

[0095] When generating Q&As, the generation unit can determine the priority of the Q&As based on the submission date and time of the owner's medical history. The generation unit determines the priority of the Q&As based on, for example, the submission date and time of the owner's medical history. For example, the generation unit may prioritize generating Q&As if the owner's medical history was submitted recently. The generation unit may also postpone generating Q&As if the owner's medical history is older. Furthermore, the generation unit can determine the priority of the Q&As based on the submission date and time of the owner's medical history. For example, the generation unit may determine the order of Q&As using a prioritization algorithm based on the record of the submission date and time. In this way, by prioritizing Q&As based on the submission date and time, more important Q&As can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0096] When generating Q&As, the generation unit can adjust the order of Q&As based on the owner's relevance. The generation unit, for example, evaluates the relevance of the owner's medical history and adjusts the order of Q&As. For example, the generation unit prioritizes generating Q&As when the owner's medical history is highly relevant. The generation unit can also postpone generating Q&As when the owner's medical history is less relevant. Furthermore, the generation unit can adjust the order of Q&As based on the owner's relevance. For example, the generation unit calculates a relevance score and sets the order of Q&As based on the score. In this way, by adjusting the order of Q&As based on relevance, more important Q&As can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0097] When generating a Q&A, the generation unit can adjust the use of technical terms in the Q&A according to the owner's expertise. The generation unit, for example, evaluates the owner's level of expertise and adjusts the use of technical terms in the Q&A. For example, if the owner has specialized knowledge, the generation unit uses a lot of technical terms. Furthermore, if the owner does not have specialized knowledge, the generation unit can generate a Q&A using simple language. Furthermore, the generation unit can adjust the use of technical terms in the Q&A according to the owner's level of expertise. For example, the generation unit selects terms based on an evaluation of the owner's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, a Q&A that is easier to understand can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0098] The providing unit can estimate the owner's emotions and adjust the Q&A provision method based on the estimated emotions. The providing unit can estimate the owner's emotions using, for example, facial expression recognition technology. For example, the providing unit can analyze the owner's facial expression data to determine whether the owner is relaxed or nervous. The providing unit can also estimate emotions using voice analysis technology. For example, the providing unit can analyze the owner's tone and speed of voice to estimate emotions. The providing unit can also estimate emotions using text analysis technology. For example, the providing unit can analyze the owner's text messages to estimate emotions. As a result, if the owner is relaxed, detailed Q&As can be provided. If the owner is nervous, concise Q&As can be provided. If the owner is in a hurry, Q&As that focus on the main points can be provided. As a result, by adjusting the Q&A provision method based on emotions, more appropriate Q&As can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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.

[0099] When providing a Q&A, the providing unit can select an appropriate providing method by referring to the owner's past feedback. The providing unit, for example, analyzes feedback data and selects an appropriate providing method by referring to the owner's past feedback. For example, the providing unit selects an optimal Q&A providing method based on feedback provided by the owner in the past. The providing unit can also provide specific information preferentially based on the feedback data. Furthermore, the providing unit can customize the Q&A providing procedure based on the feedback data. For example, the providing unit optimizes the Q&A providing procedure by referring to the owner's past feedback. In this way, the optimal providing method can be selected by referring to the past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0100] When providing Q&As, the providing unit can customize the content to be provided according to the owner's current health condition. The providing unit, for example, refers to health checkup results to understand the owner's current health condition. For example, the providing unit collects data such as the owner's blood pressure, body temperature, and heart rate. The providing unit can also provide information on medications the owner is currently taking preferentially. Furthermore, the providing unit can customize Q&As based on the owner's current health condition. For example, if the owner's blood pressure is high, the providing unit provides Q&As related to blood pressure management. In this way, by customizing the content to be provided according to the owner's current health condition, more appropriate Q&As can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0101] The providing unit can improve the provision method by reflecting the owner's feedback when providing a Q&A. The providing unit, for example, analyzes the feedback data and improves the provision method by reflecting the owner's feedback. For example, the providing unit evaluates the owner's satisfaction with the provided Q&A and improves the provision method based on the evaluation. The providing unit can also improve the Q&A provision procedure based on the feedback data. Furthermore, the providing unit can also reflect the feedback data in providing Q&As from the next time onwards. For example, the providing unit collects the owner's feedback and reflects it in providing Q&As from the next time onwards. In this way, the provision method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0102] The providing unit can estimate the owner's emotions and adjust the order in which Q&As are provided based on the estimated emotions. The providing unit can estimate the owner's emotions using, for example, facial expression recognition technology. For example, the providing unit can analyze the owner's facial expression data to determine whether the owner is relaxed or nervous. The providing unit can also estimate emotions using voice analysis technology. For example, the providing unit can analyze the owner's tone and speed of voice to estimate emotions. The providing unit can also estimate emotions using text analysis technology. For example, the providing unit can analyze the owner's text messages to estimate emotions. As a result, if the owner is relaxed, detailed Q&As can be provided preferentially. If the owner is nervous, concise Q&As can be provided preferentially. Furthermore, if the owner is in a hurry, Q&As that focus on the main points can be provided preferentially. By adjusting the provision order based on emotions, more appropriate Q&As can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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.

[0103] When providing Q&A, the providing unit can select an appropriate provision method based on the owner's geographical location information. The providing unit, for example, acquires the owner's geographical location information using GPS data. For example, the providing unit prioritizes providing information about medical facilities in the owner's current area. The providing unit can also acquire the owner's geographical location information using a location information service. For example, the providing unit references the location information of the owner's smartphone and prioritizes providing information about nearby pharmacies. Furthermore, the providing unit can also prioritize providing medical information specific to the area. For example, the providing unit provides information about diseases that are prevalent in the area based on the owner's geographical location information. This makes it possible to select the optimal provision method by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI.

[0104] When providing Q&As, the providing unit can analyze the owner's social media activities and provide related information. The providing unit, for example, analyzes the content of social media posts and collects the owner's health information. For example, the providing unit provides related medical information based on the health information the owner shared on social media. The providing unit can also understand the owner's social media activities through analysis of followers. For example, the providing unit provides related medical information by referring to the activities of the owner's friends on social media. In this way, related information can be provided by analyzing social media activities. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI.

[0105] When providing a Q&A, the providing unit can customize the providing method by reflecting the owner's past feedback. The providing unit, for example, analyzes feedback data and customizes the providing method by reflecting the owner's past feedback. For example, the providing unit selects the optimal Q&A providing method based on feedback provided by the owner in the past. The providing unit can also provide specific information preferentially based on the feedback data. Furthermore, the providing unit can customize the Q&A providing procedure based on the feedback data. For example, the providing unit optimizes the Q&A providing procedure by referring to the owner's past feedback. In this way, the providing method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and provision unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire information from a personal identification card using a scanner or RFID reader of the smart device 14. The analysis unit analyzes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The generation unit generates a Q&A by the specific processing unit 290 of the data processing device 12. The provision unit provides the Q&A to the owner using the display or speaker of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and provision unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire information from a personal identification card using a camera or RFID reader of the smart glasses 214. The analysis unit analyzes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The generation unit generates a Q&A by the specific processing unit 290 of the data processing device 12. The provision unit provides the Q&A to the owner using a display or speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit can acquire information from a personal identification card using a camera or RFID reader of the headset type terminal 314. The analysis unit analyzes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The generation unit generates a Q&A by the specific processing unit 290 of the data processing device 12. The provision unit provides the Q&A to the owner using the display or speaker of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire information from a personal identification card using a camera or RFID reader of the robot 414. The analysis unit analyzes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The generation unit generates a Q&A by the specific processing unit 290 of the data processing device 12. The provision unit provides the Q&A to the owner using a display or speaker of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The acquisition unit can monitor the owner's health condition in real time and issue a warning if an abnormality is detected. For example, the acquisition unit can continuously monitor the owner's heart rate and blood pressure and issue an alert if an abnormal value is detected. The acquisition unit can also monitor the owner's body temperature and issue a warning if a fever is detected. Furthermore, the acquisition unit can monitor the owner's respiratory rate and issue a warning if an abnormal breathing pattern is detected. This allows the owner's health condition to be monitored in real time and allows for quick response if an abnormality occurs.

[0108] The analysis unit can estimate the owner's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the owner is relaxed, the analysis unit can provide detailed analysis results. If the owner is nervous, the analysis unit can provide concise analysis results. If the owner is in a hurry, the analysis unit can provide analysis results that are concise. In this way, by adjusting the way the analysis results are presented based on emotions, it is possible to provide the owner with optimal information.

[0109] The generator can analyze the owner's past Q&A history and customize Q&As based on the owner's preferences. For example, the generator can analyze patterns of Q&As that the owner has liked to view in the past and generate similar Q&As. The generator can also analyze patterns of Q&As that the owner has avoided in the past and generate Q&As that avoid those patterns. Furthermore, the generator can adjust the content of Q&As based on the owner's feedback. This makes it possible to provide Q&As customized based on the owner's preferences.

[0110] The providing unit can estimate the owner's emotions and adjust the way in which Q&A is provided based on the estimated emotions. For example, if the owner is relaxed, the providing unit can provide detailed Q&A. If the owner is nervous, the providing unit can provide concise Q&A. If the owner is in a hurry, the providing unit can provide Q&A that focuses on the main points. In this way, by adjusting the way in which Q&A is provided based on emotions, it is possible to provide the owner with the most appropriate information.

[0111] The acquisition unit can provide region-specific medical information by utilizing the owner's geographical location information. For example, the acquisition unit can preferentially acquire information about medical facilities in the area where the owner is currently located. The acquisition unit can also acquire information about diseases that are prevalent in the area. Furthermore, the acquisition unit can provide information about medical resources in the area. This makes it possible to provide medical information that is highly relevant to the owner by taking the geographical location information into consideration.

[0112] The analysis unit can estimate the owner's emotions and determine the priority of analysis based on the estimated emotions. For example, if the owner is relaxed, the analysis unit can prioritize detailed analysis. If the owner is nervous, the analysis unit can prioritize concise analysis. If the owner is in a hurry, the analysis unit can prioritize analysis that focuses on the main points. In this way, by determining the priority of analysis based on emotions, it is possible to provide the owner with the most optimal analysis results.

[0113] The generation unit can generate Q&A about preventive medicine based on the owner's medical history. For example, the generation unit can analyze the owner's past medical records and provide information about vaccinations. The generation unit can also provide information about genetic risks by taking into account the owner's family history. Furthermore, the generation unit can provide information about preventing lifestyle-related diseases based on the owner's lifestyle. This makes it possible to support the owner's health management by providing Q&A about preventive medicine based on the medical history.

[0114] The providing unit can estimate the owner's emotions and adjust the order in which Q&As are provided based on the estimated emotions. For example, if the owner is relaxed, the providing unit can provide detailed Q&As with priority. If the owner is nervous, the providing unit can provide concise Q&As with priority. Furthermore, if the owner is in a hurry, the providing unit can provide Q&As that focus on the main points with priority. In this way, by adjusting the order in which Q&As are provided based on emotions, it is possible to provide the owner with the most suitable information.

[0115] The acquisition unit can analyze the owner's social media activity and provide relevant medical information. For example, the acquisition unit can acquire relevant medical information based on health information shared by the owner on social media. The acquisition unit can also analyze the activity of the owner's followers and provide relevant medical information. Furthermore, the acquisition unit can provide relevant medical information by referring to the activity of the owner's friends on social media. In this way, by analyzing social media activity, it is possible to provide medical information that is highly relevant to the owner.

[0116] The providing unit can estimate the owner's emotions and adjust the way in which Q&A is provided based on the estimated emotions. For example, if the owner is relaxed, the providing unit can provide detailed Q&A. If the owner is nervous, the providing unit can provide concise Q&A. If the owner is in a hurry, the providing unit can provide Q&A that focuses on the main points. In this way, by adjusting the way in which Q&A is provided based on emotions, it is possible to provide the owner with the most appropriate information.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The acquisition unit acquires information from a personal identification card. Personal identification cards include driver's licenses, employee ID cards, student ID cards, etc. The acquisition unit can acquire information using a scanner, RFID reader, or camera. When using a camera, image analysis technology is used to extract information. Step 2: The analysis unit uses the generation AI to analyze the information acquired by the acquisition unit. The analysis is performed using database search, pattern recognition, and natural language processing techniques. The generation AI analyzes the information using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The generation unit uses the generation AI to generate Q&As based on the information analyzed by the analysis unit. Q&As are generated using template-based generation or machine learning algorithms. The generation AI generates optimal Q&As based on the owner's attributes and can also generate more appropriate Q&As by referring to past Q&A history. Step 4: The provider provides the generated Q&A to the owner. The provision can be done by displaying it on a screen, providing audio guidance, or notifying the owner through a mobile app. For example, the Q&A can be displayed on a smartphone screen or provided via audio guidance using a speaker.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. an acquisition unit for acquiring information on a personal identification card; an analysis unit that analyzes the information acquired by the acquisition unit; a generation unit that generates a Q&A based on the information analyzed by the analysis unit; a providing unit that provides the Q&A generated by the generating unit; Equipped with A system characterized by:

2. The acquisition unit Obtain information on the owner's age, gender, and medical history 2. The system of claim 1.

3. The analysis unit Analyze the acquired information and identify the owner's attributes 2. The system of claim 1.

4. The generation unit Generate appropriate Q&A based on identified attributes 2. The system of claim 1.

5. The providing unit Display the generated Q&A on the screen or provide audio guidance 2. The system of claim 1.

6. The providing unit Collect owner feedback to help improve our services 2. The system of claim 1.

7. The acquisition unit Estimates the owner's emotions and adjusts the timing of information acquisition on the personal identification card based on the estimated emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the owner's past medical history and select the appropriate method of obtaining information 2. The system of claim 1.

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