System

Abstract

An object of the system according to the embodiment is to efficiently collect the expert knowledge and experience of experienced caregivers and healthcare professionals and provide them to those who need them.SOLUTION: A system includes an information collection part, an information analysis part, an information storage part, an information retrieval part, and an information provision part. The information collection unit registers the experience of the experienced caregiver or the medical worker as a document or voice data. The information analysis unit analyzes the information collected by the information collection unit, assigns an appropriate category or tag to the information, and accumulates the information. The information accumulation unit accumulates the information analyzed by the information analysis unit. The information search unit searches for necessary information when the user inputs a keyword or a question. The information providing unit provides the user with the information retrieved by the information retrieval unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 technology has faced the challenge of making it difficult to efficiently collect the specialized knowledge and experience of caregivers and medical professionals and provide it to those who need it.

[0005] The system according to the embodiment aims to efficiently collect the specialized knowledge and experience of people with caregiving experience and medical professionals and provide it to those who need it. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information analysis unit, an information storage unit, an information search unit, and an information provision unit. The information collection unit allows former caregivers and medical professionals to register their experiences as documents or audio data. The information analysis unit analyzes the information collected by the information collection unit and stores the information by assigning appropriate categories and tags. The information storage unit stores the information analyzed by the information analysis unit. The information search unit searches for necessary information when a user inputs keywords or questions. The information provision unit provides the user with the information searched by the information search unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect the specialized knowledge and experience of people with caregiving experience and medical professionals and provide it to those who need it. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The information provision system according to an embodiment of the present invention is a system that collects and stores specialized medical information knowledge on AI, thereby providing specialized information to those who need it. As a result, the information provision system can efficiently collect and store specialized medical information and provide it quickly and accurately to those who need it.

[0029] An information provision system according to an embodiment includes an information collection unit, an information analysis unit, an information storage unit, an information search unit, and an information provision unit. The information collection unit allows former caregivers and medical professionals to register their experiences as documents or audio data. For example, the information collection unit allows former caregivers to register their experiences as text files. The information collection unit can also allow medical professionals to record and register audio data. The information collection unit can also analyze audio data and convert it into text data. For example, the information collection unit can convert audio data into text data using voice recognition technology. The information analysis unit analyzes the information collected by the information collection unit, assigns appropriate categories and tags, and stores the information. For example, the information analysis unit classifies the collected information into information about caregiving, information about a specific disease, etc. The information analysis unit can also analyze the information using natural language processing technology and assign appropriate tags. The information analysis unit can also evaluate the reliability of information and preferentially assign tags to highly reliable information. For example, the reliability is evaluated based on the source or citation of the information. The information storage unit stores the information analyzed by the information analysis unit. For example, the information storage unit stores the analyzed information in a database. The information storage unit can also analyze the frequency of information updates and prioritize displaying the most recent information. For example, the most recent information can be prioritized based on the date and time the information was updated. The information search unit searches for necessary information when the user inputs keywords or questions. For example, when the user inputs keywords such as "how to care for dementia," the information search unit searches for related information. The information search unit can also learn the user's search history and provide optimal search results. For example, the information search unit can prioritize displaying highly relevant information based on past search keywords and click history. The information provision unit provides the user with the information searched by the information search unit. For example, the information provision unit displays the search results as a web page or an app notification. The information provision unit can also analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message can be displayed.As a result, the information provision system according to the embodiment can efficiently collect and store specialized medical information and provide it quickly and accurately to those who need it. For example, when caregivers and medical professionals share their knowledge, other users can use that information to provide appropriate care and treatment. Furthermore, the use of generative AI simplifies the registration and search of information, reducing the burden on users.

[0030] The information collection unit can analyze voice data and convert it into text data. The information collection unit, for example, analyzes voice data and converts it into text data. For example, the voice data is converted into text data using voice recognition technology. The information collection unit can also analyze voice data, automatically determine the speaker's level of expertise, and assign appropriate tags. For example, the speaker's level of expertise is evaluated based on the frequency of use of medical terms and the depth of the specialized content, and appropriate tags are assigned. In this way, converting voice data into text data makes it easier to register information.

[0031] The information analysis unit can classify information according to its type. For example, the information analysis unit classifies collected information into information about nursing care or information about a specific disease. For example, the information analysis unit analyzes information using natural language processing technology and assigns appropriate tags. The information analysis unit can also evaluate the reliability of information and prioritize tagging highly reliable information. For example, the reliability can be evaluated based on the source or citation of the information. In this way, by classifying information according to its type, necessary information can be quickly searched for and retrieved later.

[0032] The information search unit can analyze keywords or questions and provide the most appropriate answer. For example, when a user inputs a keyword such as "dementia care methods," the information search unit searches for related information. For example, the information search unit analyzes keywords or questions using natural language processing technology and provides the most appropriate answer. The information search unit can also learn the user's search history and provide the most appropriate search results. For example, it can prioritize the display of highly relevant information based on past search keywords and click history. This allows the user to quickly obtain the information they need by analyzing keywords and questions and providing the most appropriate answer.

[0033] The information providing unit can provide information to the user in an easy-to-understand manner. For example, the information providing unit displays search results as a web page or an app notification. For example, the information providing unit provides information using illustrations or videos. The information providing unit can also provide information in concise sentences. For example, the information can be explained in general terms, avoiding technical terms. This allows the user to easily obtain specialized knowledge by providing information that is easy to understand.

[0034] The information providing unit can update information based on feedback from users. For example, when a user inputs an evaluation or comment on the provided information, the generation AI analyzes the feedback and improves the accuracy and content of the information. For example, the user provides feedback on the accuracy and usefulness of the information. The information providing unit can also update information based on the user's usage history. For example, it prioritizes updating information that the user accesses frequently. In this way, by updating information based on user feedback, it is possible to always provide the latest and accurate information.

[0035] The information collecting unit can analyze the audio data, automatically determine the speaker's level of expertise, and assign appropriate tags. The information collecting unit, for example, analyzes the audio data and automatically determines the speaker's level of expertise. For example, the information collecting unit evaluates the speaker's level of expertise based on the frequency of use of medical terms and the depth of the specialized content, and assigns appropriate tags. The information collecting unit can also evaluate the reliability of information based on the speaker's level of expertise. For example, information from speakers with a high level of expertise is preferentially displayed. In this way, the reliability of information can be improved by automatically determining the speaker's level of expertise and assigning appropriate tags.

[0036] The information collection unit can learn the registrant's past registration history and suggest the optimal registration method. For example, the information collection unit uses a generation AI to learn the registrant's past registration history and suggest the optimal registration method. For example, it analyzes the content and frequency of past registrations and suggests the optimal registration method for the registrant. The information collection unit can also optimize the registration method based on the registrant's behavioral patterns. For example, it can prioritize displaying functions that the registrant uses frequently. This makes it possible to streamline the registration process by learning the registrant's past registration history and suggesting the optimal registration method.

[0037] The information collection unit can automatically translate voice data in different languages, enabling international information collection. For example, the information collection unit uses a generation AI to automatically translate voice data in different languages, enabling international information collection. For example, it supports multiple languages ​​such as English, French, and Chinese. The information collection unit can also analyze voice data in different languages ​​and assign appropriate tags. For example, it assigns tags based on translated text data. This makes it possible to realize international information collection by automatically translating voice data in different languages.

[0038] The information analysis unit can evaluate the reliability of information and prioritize tags for highly reliable information. For example, the information analysis unit uses a generation AI to evaluate the reliability of information and prioritize tags for highly reliable information. For example, the reliability is evaluated based on the source of the information or the citation source. The information analysis unit can also optimize the display order of information based on the reliability of the information. For example, highly reliable information is displayed with priority. In this way, by evaluating the reliability of information and priority tagging for highly reliable information, users can quickly obtain highly reliable information.

[0039] The information analysis unit can analyze the relevance of information and automatically link related information together. For example, the information analysis unit uses a generation AI to analyze the relevance of information and automatically link related information together. For example, it links information related to the same theme or topic. The information analysis unit can also optimize the display order of information based on the relevance of the information. For example, it can prioritize displaying highly related information. In this way, by analyzing the relevance of information and automatically linking related information together, users can easily obtain related information.

[0040] The information analysis unit can integrate information from different data sources to achieve comprehensive information management. For example, the generative AI integrates information from different data sources to achieve comprehensive information management. For example, it integrates information from medical databases and academic paper databases. The information analysis unit can also evaluate the reliability of information based on information from different data sources. For example, it compares information from multiple data sources and evaluates its reliability. This makes it possible to achieve comprehensive information management by integrating information from different data sources.

[0041] The information analysis unit can analyze the frequency of information updates and prioritize displaying the most recent information. For example, the generation AI analyzes the frequency of information updates and prioritizes displaying the most recent information. For example, the most recent information is displayed prioritized based on the update date and time of the information. The information analysis unit can also optimize the display order of information based on the frequency of information updates. For example, information that is updated more frequently is displayed prioritized. In this way, by analyzing the frequency of information updates and priority displaying the most recent information, the user can always obtain the most recent information.

[0042] The information search unit can learn the user's search history and provide optimal search results. For example, the information search unit uses a generation AI to learn the user's search history and provide optimal search results. For example, it can prioritize displaying highly relevant information based on past search keywords and click history. The information search unit can also optimize search results based on the user's search history. For example, it can prioritize displaying information related to keywords that the user frequently searches for. This allows the system to learn the user's search history and provide optimal search results, allowing the user to quickly obtain the information they need.

[0043] The information search unit can analyze the user's level of expertise and provide appropriate information. For example, the generation AI can analyze the user's level of expertise and provide appropriate information. For example, the information search unit can evaluate the user's level of expertise based on their search history and input content and provide appropriate information. The information search unit can also optimize the display order of information based on the user's level of expertise. For example, more detailed information can be provided to users with a higher level of expertise. In this way, by analyzing the user's level of expertise and providing appropriate information, the user can quickly obtain the information they need.

[0044] The information search unit supports searches in different languages, enabling international information provision. For example, the generation AI supports searches in different languages, enabling international information provision. For example, it supports multiple languages ​​such as English, French, and Chinese. The information search unit can also provide information based on search results in different languages. For example, it displays translated search results. This allows international information provision by supporting searches in different languages.

[0045] The information providing unit can analyze the user's level of expertise and provide appropriate information. For example, the information providing unit uses a generation AI to analyze the user's level of expertise and provide appropriate information. For example, the information providing unit evaluates the user's level of expertise based on the user's search history and input content and provides appropriate information. The information providing unit can also optimize the display order of information based on the user's level of expertise. For example, more detailed information is provided to users with a high level of expertise. In this way, by analyzing the user's level of expertise and providing appropriate information, the user can quickly obtain the information they need.

[0046] The information provision unit supports the provision of information in different languages, thereby realizing international information provision. For example, the generation AI supports the provision of information in different languages, thereby realizing international information provision. For example, it supports multiple languages ​​such as English, French, and Chinese. The information provision unit can also provide information based on information in different languages. For example, it displays translated information. This allows the provision of information in different languages ​​to be supported, thereby realizing international information provision.

[0047] The information providing unit can analyze user feedback and automatically improve the accuracy of the information. For example, the information providing unit uses a generation AI to analyze user feedback and automatically improve the accuracy of the information. For example, the content of the information is automatically corrected and updated based on the feedback provided by the user. The information providing unit can also update the information based on the user's usage history. For example, it prioritizes updating information that the user accesses frequently. In this way, by analyzing user feedback and automatically improving the accuracy of the information, it is possible to always provide accurate information.

[0048] The information providing unit can analyze the frequency of information updates and prioritize displaying the latest information. For example, the generation AI of the information providing unit can analyze the frequency of information updates and prioritize displaying the latest information. For example, the latest information can be prioritized based on the update date and time of the information. The information providing unit can also optimize the display order of information based on the frequency of information updates. For example, information that is updated more frequently can be prioritized. In this way, by analyzing the frequency of information updates and prioritize displaying the latest information, the user can always obtain the latest information.

[0049] The information provision unit can integrate feedback from different data sources to achieve comprehensive information updates. For example, the generation AI can integrate feedback from different data sources to achieve comprehensive information updates. For example, it can consolidate feedback from social media and news sites into a single view. The information provision unit can also improve the accuracy of information based on feedback from different data sources. For example, it can compare feedback from multiple data sources and revise and update the content of the information. This makes it possible to achieve comprehensive information updates by integrating feedback from different data sources.

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

[0051] The information providing unit can monitor the user's health condition and provide appropriate information. For example, it can use a wearable device to measure the user's heart rate and blood pressure and provide health advice based on that data. The information providing unit can also suggest appropriate exercises and meals based on the user's health condition. For example, if the heart rate is high, it can suggest stretches to relax. In this way, the information providing unit can support health management by monitoring the user's health condition and providing appropriate information.

[0052] The information analysis unit can integrate information from different data sources to achieve comprehensive information management. For example, it can integrate information from medical databases and academic paper databases. The information analysis unit can also evaluate the reliability of information based on information from different data sources. For example, it can compare information from multiple data sources and evaluate its reliability. This makes it possible to achieve comprehensive information management by integrating information from different data sources.

[0053] The information providing unit can analyze the user's level of expertise and provide appropriate information. For example, it can evaluate the user's level of expertise based on the user's search history and input content and provide appropriate information. The information providing unit can also optimize the display order of information based on the user's level of expertise. For example, it can provide more detailed information to users with a higher level of expertise. In this way, by analyzing the user's level of expertise and providing appropriate information, the user can quickly obtain the information they need.

[0054] The information collection unit can automatically translate voice data in different languages, enabling international information gathering. For example, the generation AI can automatically translate voice data in different languages, enabling international information gathering. For example, it supports multiple languages ​​such as English, French, and Chinese. The information collection unit can also analyze voice data in different languages ​​and assign appropriate tags. For example, it assigns tags based on translated text data. This makes it possible to realize international information gathering by automatically translating voice data in different languages.

[0055] The information analysis unit can analyze the relevance of information and automatically link related information together. For example, the generation AI analyzes the relevance of information and automatically links related information together. For example, it links information about the same theme or topic. The information analysis unit can also optimize the display order of information based on the relevance of the information. For example, it can prioritize displaying highly related information. In this way, by analyzing the relevance of information and automatically linking related information together, users can easily obtain related information.

[0056] The information providing unit can analyze user feedback and automatically improve the accuracy of the information. For example, the generation AI analyzes user feedback and automatically improves the accuracy of the information. For example, the content of the information is automatically corrected and updated based on the feedback provided by the user. The information providing unit can also update information based on the user's usage history. For example, it prioritizes updating information that the user accesses frequently. In this way, by analyzing user feedback and automatically improving the accuracy of the information, it is possible to always provide accurate information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: In the information collection unit, caregivers and medical professionals register their experiences as documents or audio data. For example, caregivers can register their experiences as text files, and medical professionals can record and register audio data. Audio data can also be converted into text data using voice recognition technology. Step 2: The information analysis unit analyzes the information collected by the information collection unit, assigns appropriate categories and tags, and stores the information. For example, the information collected can be classified into categories such as information about nursing care or information about specific illnesses, and the information can be analyzed using natural language processing technology and assigned appropriate tags. It can also evaluate the reliability of the information and assign tags preferentially to highly reliable information. Step 3: The information storage unit stores the information analyzed by the information analysis unit. For example, the analyzed information can be saved in a database, and the frequency of updates to the information can be analyzed to display the most recent information preferentially. Step 4: The information search unit searches for the necessary information when the user inputs keywords or questions. For example, if the user inputs keywords such as "how to care for dementia," it will search for related information. It can also learn the user's search history and provide optimal search results. Step 5: The information providing unit provides the information searched by the information searching unit to the user. For example, the information providing unit may display the search results as a web page or an app notification, and may analyze the user's emotions and provide feedback in real time to elicit positive emotions.

[0059] (Example 2) The information provision system according to an embodiment of the present invention is a system that collects and stores specialized medical information knowledge on AI, thereby providing specialized information to those who need it. As a result, the information provision system can efficiently collect and store specialized medical information and provide it quickly and accurately to those who need it.

[0060] An information provision system according to an embodiment includes an information collection unit, an information analysis unit, an information storage unit, an information search unit, and an information provision unit. The information collection unit allows former caregivers and medical professionals to register their experiences as documents or audio data. For example, the information collection unit allows former caregivers to register their experiences as text files. The information collection unit can also allow medical professionals to record and register audio data. The information collection unit can also analyze audio data and convert it into text data. For example, the information collection unit can convert audio data into text data using voice recognition technology. The information analysis unit analyzes the information collected by the information collection unit, assigns appropriate categories and tags, and stores the information. For example, the information analysis unit classifies the collected information into information about caregiving, information about a specific disease, etc. The information analysis unit can also analyze the information using natural language processing technology and assign appropriate tags. The information analysis unit can also evaluate the reliability of information and preferentially assign tags to highly reliable information. For example, the reliability is evaluated based on the source or citation of the information. The information storage unit stores the information analyzed by the information analysis unit. For example, the information storage unit stores the analyzed information in a database. The information storage unit can also analyze the frequency of information updates and prioritize displaying the most recent information. For example, the most recent information can be prioritized based on the date and time the information was updated. The information search unit searches for necessary information when the user inputs keywords or questions. For example, when the user inputs keywords such as "how to care for dementia," the information search unit searches for related information. The information search unit can also learn the user's search history and provide optimal search results. For example, the information search unit can prioritize displaying highly relevant information based on past search keywords and click history. The information provision unit provides the user with the information searched by the information search unit. For example, the information provision unit displays the search results as a web page or an app notification. The information provision unit can also analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message can be displayed.As a result, the information provision system according to the embodiment can efficiently collect and store specialized medical information and provide it quickly and accurately to those who need it. For example, when caregivers and medical professionals share their knowledge, other users can use that information to provide appropriate care and treatment. Furthermore, the use of generative AI simplifies the registration and search of information, reducing the burden on users.

[0061] The information collection unit can analyze voice data and convert it into text data. The information collection unit, for example, analyzes voice data and converts it into text data. For example, the voice data is converted into text data using voice recognition technology. The information collection unit can also analyze voice data, automatically determine the speaker's level of expertise, and assign appropriate tags. For example, the speaker's level of expertise is evaluated based on the frequency of use of medical terms and the depth of the specialized content, and appropriate tags are assigned. In this way, converting voice data into text data makes it easier to register information.

[0062] The information analysis unit can classify information according to its type. For example, the information analysis unit classifies collected information into information about nursing care or information about a specific disease. For example, the information analysis unit analyzes information using natural language processing technology and assigns appropriate tags. The information analysis unit can also evaluate the reliability of information and prioritize tagging highly reliable information. For example, the reliability can be evaluated based on the source or citation of the information. In this way, by classifying information according to its type, necessary information can be quickly searched for and retrieved later.

[0063] The information search unit can analyze keywords or questions and provide the most appropriate answer. For example, when a user inputs a keyword such as "dementia care methods," the information search unit searches for related information. For example, the information search unit analyzes keywords or questions using natural language processing technology and provides the most appropriate answer. The information search unit can also learn the user's search history and provide the most appropriate search results. For example, it can prioritize the display of highly relevant information based on past search keywords and click history. This allows the user to quickly obtain the information they need by analyzing keywords and questions and providing the most appropriate answer.

[0064] The information providing unit can provide information to the user in an easy-to-understand manner. For example, the information providing unit displays search results as a web page or an app notification. For example, the information providing unit provides information using illustrations or videos. The information providing unit can also provide information in concise sentences. For example, the information can be explained in general terms, avoiding technical terms. This allows the user to easily obtain specialized knowledge by providing information that is easy to understand.

[0065] The information providing unit can update information based on feedback from users. For example, when a user inputs an evaluation or comment on the provided information, the generation AI analyzes the feedback and improves the accuracy and content of the information. For example, the user provides feedback on the accuracy and usefulness of the information. The information providing unit can also update information based on the user's usage history. For example, it prioritizes updating information that the user accesses frequently. In this way, by updating information based on user feedback, it is possible to always provide the latest and accurate information.

[0066] The information collection unit can analyze the emotions of registrants in real time and provide an interface that elicits positive emotions. For example, when a registrant enters information, the information collection unit uses a generation AI to analyze facial expressions and voice tone and estimate emotions in real time. For example, the information collection unit can analyze the emotions of registrants using a camera or microphone and provide an interface that elicits positive emotions. The information collection unit can also play relaxation music when a registrant is feeling stressed. For example, it can provide music that helps the registrant relax. This allows the registrant's emotions to be analyzed in real time and elicits positive emotions, making it possible to smoothly proceed with the registration process.

[0067] The information collecting unit can analyze the audio data, automatically determine the speaker's level of expertise, and assign appropriate tags. The information collecting unit, for example, analyzes the audio data and automatically determines the speaker's level of expertise. For example, the information collecting unit evaluates the speaker's level of expertise based on the frequency of use of medical terms and the depth of the specialized content, and assigns appropriate tags. The information collecting unit can also evaluate the reliability of information based on the speaker's level of expertise. For example, information from speakers with a high level of expertise is preferentially displayed. In this way, the reliability of information can be improved by automatically determining the speaker's level of expertise and assigning appropriate tags.

[0068] The information collection unit can learn the registrant's past registration history and suggest the optimal registration method. For example, the information collection unit uses a generation AI to learn the registrant's past registration history and suggest the optimal registration method. For example, it analyzes the content and frequency of past registrations and suggests the optimal registration method for the registrant. The information collection unit can also optimize the registration method based on the registrant's behavioral patterns. For example, it can prioritize displaying functions that the registrant uses frequently. This makes it possible to streamline the registration process by learning the registrant's past registration history and suggesting the optimal registration method.

[0069] The information collection unit can automatically translate voice data in different languages, enabling international information collection. For example, the information collection unit uses a generation AI to automatically translate voice data in different languages, enabling international information collection. For example, it supports multiple languages ​​such as English, French, and Chinese. The information collection unit can also analyze voice data in different languages ​​and assign appropriate tags. For example, it assigns tags based on translated text data. This makes it possible to realize international information collection by automatically translating voice data in different languages.

[0070] The information collection unit can estimate the registrant's emotions during the registration process and provide music or relaxation techniques to reduce stress. For example, during the registration process, the information collection unit uses a generation AI to estimate the registrant's emotions and provide music or relaxation techniques to reduce stress. For example, if the registrant is feeling stressed, relaxation music is played. The information collection unit can also analyze the registrant's emotions in real time and provide an interface to elicit positive emotions. For example, a camera or microphone can be used to analyze the registrant's emotions and provide an interface to elicit positive emotions. This makes it possible to estimate the registrant's emotions during the registration process and provide music or relaxation techniques to reduce stress, thereby reducing the burden on the registrant.

[0071] The information collection unit can analyze the emotions of registrants and provide feedback in real time to elicit positive emotions. For example, the information collection unit uses a generation AI to analyze the emotions of registrants and provide feedback in real time to elicit positive emotions. For example, if a registrant shows negative emotions, an encouraging message is displayed. The information collection unit can also analyze the emotions of registrants in real time and provide an interface to elicit positive emotions. For example, a camera or microphone can be used to analyze the emotions of registrants and provide an interface to elicit positive emotions. This allows the registration process to proceed smoothly by analyzing the emotions of registrants and providing feedback in real time to elicit positive emotions.

[0072] The information analysis unit can evaluate the reliability of information and prioritize tags for highly reliable information. For example, the information analysis unit uses a generation AI to evaluate the reliability of information and prioritize tags for highly reliable information. For example, the reliability is evaluated based on the source of the information or the citation source. The information analysis unit can also optimize the display order of information based on the reliability of the information. For example, highly reliable information is displayed with priority. In this way, by evaluating the reliability of information and priority tagging for highly reliable information, users can quickly obtain highly reliable information.

[0073] The information analysis unit can analyze the relevance of information and automatically link related information together. For example, the information analysis unit uses a generation AI to analyze the relevance of information and automatically link related information together. For example, it links information related to the same theme or topic. The information analysis unit can also optimize the display order of information based on the relevance of the information. For example, it can prioritize displaying highly related information. In this way, by analyzing the relevance of information and automatically linking related information together, users can easily obtain related information.

[0074] The information analysis unit can analyze the emotional value of information and prioritize displaying information that elicits positive emotions. For example, the information analysis unit uses a generation AI to analyze the emotional value of information and prioritize displaying information that elicits positive emotions. For example, information with a high emotional score is prioritized for display. The information analysis unit can also analyze the user's emotions and provide feedback to elicit positive emotions. For example, if the user expresses negative emotions, an encouraging message is displayed. In this way, by analyzing the emotional value of information and prioritize displaying information that elicits positive emotions, user satisfaction can be improved.

[0075] The information analysis unit can integrate information from different data sources to achieve comprehensive information management. For example, the generative AI integrates information from different data sources to achieve comprehensive information management. For example, it integrates information from medical databases and academic paper databases. The information analysis unit can also evaluate the reliability of information based on information from different data sources. For example, it compares information from multiple data sources and evaluates its reliability. This makes it possible to achieve comprehensive information management by integrating information from different data sources.

[0076] The information analysis unit can analyze the frequency of information updates and prioritize displaying the most recent information. For example, the generation AI analyzes the frequency of information updates and prioritizes displaying the most recent information. For example, the most recent information is displayed prioritized based on the update date and time of the information. The information analysis unit can also optimize the display order of information based on the frequency of information updates. For example, information that is updated more frequently is displayed prioritized. In this way, by analyzing the frequency of information updates and priority displaying the most recent information, the user can always obtain the most recent information.

[0077] The information search unit can learn the user's search history and provide optimal search results. For example, the information search unit uses a generation AI to learn the user's search history and provide optimal search results. For example, it can prioritize displaying highly relevant information based on past search keywords and click history. The information search unit can also optimize search results based on the user's search history. For example, it can prioritize displaying information related to keywords that the user frequently searches for. This allows the system to learn the user's search history and provide optimal search results, allowing the user to quickly obtain the information they need.

[0078] The information search unit can analyze the user's emotions and prioritize displaying search results that elicit positive emotions. For example, the information search unit uses a generation AI to analyze the user's emotions and prioritize displaying search results that elicit positive emotions. For example, information with a high emotion score is prioritized. The information search unit can also optimize search results based on the user's emotions. For example, if the user expresses negative emotions, information that elicits positive emotions is prioritized. In this way, by analyzing the user's emotions and prioritize displaying search results that elicit positive emotions, user satisfaction can be improved.

[0079] The information search unit can analyze the user's level of expertise and provide appropriate information. For example, the generation AI can analyze the user's level of expertise and provide appropriate information. For example, the information search unit can evaluate the user's level of expertise based on their search history and input content and provide appropriate information. The information search unit can also optimize the display order of information based on the user's level of expertise. For example, more detailed information can be provided to users with a higher level of expertise. In this way, by analyzing the user's level of expertise and providing appropriate information, the user can quickly obtain the information they need.

[0080] The information search unit supports searches in different languages, enabling international information provision. For example, the generation AI supports searches in different languages, enabling international information provision. For example, it supports multiple languages ​​such as English, French, and Chinese. The information search unit can also provide information based on search results in different languages. For example, it displays translated search results. This allows international information provision by supporting searches in different languages.

[0081] The information providing unit can analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, the information providing unit uses a generation AI to analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message is displayed. The information providing unit can also analyze the user's emotions in real time and provide an interface to elicit positive emotions. For example, a camera or microphone can be used to analyze the user's emotions and provide an interface to elicit positive emotions. This makes it possible to analyze the user's emotions and provide feedback in real time to elicit positive emotions, thereby improving user satisfaction.

[0082] The information providing unit can analyze the user's level of expertise and provide appropriate information. For example, the information providing unit uses a generation AI to analyze the user's level of expertise and provide appropriate information. For example, the information providing unit evaluates the user's level of expertise based on the user's search history and input content and provides appropriate information. The information providing unit can also optimize the display order of information based on the user's level of expertise. For example, more detailed information is provided to users with a high level of expertise. In this way, by analyzing the user's level of expertise and providing appropriate information, the user can quickly obtain the information they need.

[0083] The information provision unit supports the provision of information in different languages, thereby realizing international information provision. For example, the generation AI supports the provision of information in different languages, thereby realizing international information provision. For example, it supports multiple languages ​​such as English, French, and Chinese. The information provision unit can also provide information based on information in different languages. For example, it displays translated information. This allows the provision of information in different languages ​​to be supported, thereby realizing international information provision.

[0084] The information providing unit can analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, the information providing unit uses a generation AI to analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message is displayed. The information providing unit can also analyze the user's emotions in real time and provide an interface to elicit positive emotions. For example, a camera or microphone can be used to analyze the user's emotions and provide an interface to elicit positive emotions. This makes it possible to analyze the user's emotions and provide feedback in real time to elicit positive emotions, thereby improving user satisfaction.

[0085] The information providing unit can analyze user feedback and automatically improve the accuracy of the information. For example, the information providing unit uses a generation AI to analyze user feedback and automatically improve the accuracy of the information. For example, the content of the information is automatically corrected and updated based on the feedback provided by the user. The information providing unit can also update the information based on the user's usage history. For example, it prioritizes updating information that the user accesses frequently. In this way, by analyzing user feedback and automatically improving the accuracy of the information, it is possible to always provide accurate information.

[0086] The information providing unit can analyze the frequency of information updates and prioritize displaying the latest information. For example, the generation AI of the information providing unit can analyze the frequency of information updates and prioritize displaying the latest information. For example, the latest information can be prioritized based on the update date and time of the information. The information providing unit can also optimize the display order of information based on the frequency of information updates. For example, information that is updated more frequently can be prioritized. In this way, by analyzing the frequency of information updates and prioritize displaying the latest information, the user can always obtain the latest information.

[0087] The information provision unit can integrate feedback from different data sources to achieve comprehensive information updates. For example, the generation AI can integrate feedback from different data sources to achieve comprehensive information updates. For example, it can consolidate feedback from social media and news sites into a single view. The information provision unit can also improve the accuracy of information based on feedback from different data sources. For example, it can compare feedback from multiple data sources and revise and update the content of the information. This makes it possible to achieve comprehensive information updates by integrating feedback from different data sources.

[0088] The information providing unit can analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, the information providing unit uses a generation AI to analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message is displayed. The information providing unit can also analyze the user's emotions in real time and provide an interface to elicit positive emotions. For example, a camera or microphone can be used to analyze the user's emotions and provide an interface to elicit positive emotions. This makes it possible to analyze the user's emotions and provide feedback in real time to elicit positive emotions, thereby improving user satisfaction.

[0089] The information providing unit can analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, the information providing unit uses a generation AI to analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message is displayed. The information providing unit can also analyze the user's emotions in real time and provide an interface to elicit positive emotions. For example, a camera or microphone can be used to analyze the user's emotions and provide an interface to elicit positive emotions. This makes it possible to analyze the user's emotions and provide feedback in real time to elicit positive emotions, thereby improving user satisfaction.

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

[0091] The information providing unit can monitor the user's health condition and provide appropriate information. For example, it can use a wearable device to measure the user's heart rate and blood pressure and provide health advice based on that data. The information providing unit can also suggest appropriate exercises and meals based on the user's health condition. For example, if the heart rate is high, it can suggest stretches to relax. In this way, the information providing unit can support health management by monitoring the user's health condition and providing appropriate information.

[0092] The information collection unit can estimate the user's emotions and provide information according to the emotions. For example, if the user is feeling stressed, information about relaxation methods and stress relief methods can be provided. Also, if the user is showing positive emotions, information to further increase motivation can be provided. For example, successful experiences and positive messages can be displayed. In this way, by estimating the user's emotions and providing information according to the emotions, user satisfaction can be improved.

[0093] The information analysis unit can integrate information from different data sources to achieve comprehensive information management. For example, it can integrate information from medical databases and academic paper databases. The information analysis unit can also evaluate the reliability of information based on information from different data sources. For example, it can compare information from multiple data sources and evaluate its reliability. This makes it possible to achieve comprehensive information management by integrating information from different data sources.

[0094] The information search unit can analyze the user's emotions and prioritize displaying search results that evoke positive emotions. For example, information with a high emotion score can be prioritized. The information search unit can also optimize search results based on the user's emotions. For example, if the user expresses negative emotions, information that evokes positive emotions can be prioritized. In this way, by analyzing the user's emotions and prioritize displaying search results that evoke positive emotions, user satisfaction can be improved.

[0095] The information providing unit can analyze the user's level of expertise and provide appropriate information. For example, it can evaluate the user's level of expertise based on the user's search history and input content and provide appropriate information. The information providing unit can also optimize the display order of information based on the user's level of expertise. For example, it can provide more detailed information to users with a higher level of expertise. In this way, by analyzing the user's level of expertise and providing appropriate information, the user can quickly obtain the information they need.

[0096] The information collection unit can automatically translate voice data in different languages, enabling international information gathering. For example, the generation AI can automatically translate voice data in different languages, enabling international information gathering. For example, it supports multiple languages ​​such as English, French, and Chinese. The information collection unit can also analyze voice data in different languages ​​and assign appropriate tags. For example, it assigns tags based on translated text data. This makes it possible to realize international information gathering by automatically translating voice data in different languages.

[0097] The information provision unit can analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, the generative AI analyzes the user's emotions and provides feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message is displayed. The information provision unit can also analyze the user's emotions in real time and provide an interface to elicit positive emotions. For example, it can analyze the user's emotions using a camera or microphone and provide an interface to elicit positive emotions. In this way, by analyzing the user's emotions and providing feedback in real time to elicit positive emotions, it is possible to improve user satisfaction.

[0098] The information analysis unit can analyze the relevance of information and automatically link related information together. For example, the generation AI analyzes the relevance of information and automatically links related information together. For example, it links information about the same theme or topic. The information analysis unit can also optimize the display order of information based on the relevance of the information. For example, it can prioritize displaying highly related information. In this way, by analyzing the relevance of information and automatically linking related information together, users can easily obtain related information.

[0099] The information providing unit can analyze user feedback and automatically improve the accuracy of the information. For example, the generation AI analyzes user feedback and automatically improves the accuracy of the information. For example, the content of the information is automatically corrected and updated based on the feedback provided by the user. The information providing unit can also update information based on the user's usage history. For example, it prioritizes updating information that the user accesses frequently. In this way, by analyzing user feedback and automatically improving the accuracy of the information, it is possible to always provide accurate information.

[0100] The information provision unit can analyze the user's emotions and provide feedback in real time to elicit positive emotions. For example, the generative AI analyzes the user's emotions and provides feedback in real time to elicit positive emotions. For example, if the user shows negative emotions, an encouraging message is displayed. The information provision unit can also analyze the user's emotions in real time and provide an interface to elicit positive emotions. For example, it can analyze the user's emotions using a camera or microphone and provide an interface to elicit positive emotions. In this way, by analyzing the user's emotions and providing feedback in real time to elicit positive emotions, it is possible to improve user satisfaction.

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

[0102] Step 1: In the information collection unit, caregivers and medical professionals register their experiences as documents or audio data. For example, caregivers can register their experiences as text files, and medical professionals can record and register audio data. Audio data can also be converted into text data using voice recognition technology. Step 2: The information analysis unit analyzes the information collected by the information collection unit, assigns appropriate categories and tags, and stores the information. For example, the information collected can be classified into categories such as information about nursing care or information about specific illnesses, and the information can be analyzed using natural language processing technology and assigned appropriate tags. It can also evaluate the reliability of the information and assign tags preferentially to highly reliable information. Step 3: The information storage unit stores the information analyzed by the information analysis unit. For example, the analyzed information can be saved in a database, and the frequency of updates to the information can be analyzed to display the most recent information preferentially. Step 4: The information search unit searches for the necessary information when the user inputs keywords or questions. For example, if the user inputs keywords such as "how to care for dementia," it will search for related information. It can also learn the user's search history and provide optimal search results. Step 5: The information providing unit provides the information searched by the information searching unit to the user. For example, the information providing unit may display the search results as a web page or an app notification, and may analyze the user's emotions and provide feedback in real time to elicit positive emotions.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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 information collection section where caregivers and medical professionals register their experiences as written and audio data; an information analysis unit that analyzes the information collected by the information collection unit, assigns appropriate categories and tags to the information, and stores the information; an information storage unit that stores the information analyzed by the information analysis unit; an information search unit that searches for necessary information by allowing a user to input keywords or questions; an information providing unit that provides the information searched by the information searching unit to a user; A system characterized by:

2. The information collecting unit Automatic translation of the audio data in different languages ​​enables international information gathering 2. The system of claim 1.

3. The information analysis unit The reliability of the information is evaluated, and tags are preferentially assigned to highly reliable information.

2. The system of claim 1.

4. The information search unit Learn the user's search history and provide optimal search results 2. The system of claim 1.

5. The information providing unit Analyzing the user's feedback and automatically improving the accuracy of the information 2. The system of claim 1.

6. The information collecting unit Analyzes subscribers' emotions in real time and provides an interface that elicits positive emotions 2. The system of claim 1.

7. The information analysis unit Analyze the emotional value of the information and prioritize displaying information that evokes positive emotions 2. The system of claim 1.

8. The information search unit Analyze the user's emotions and prioritize displaying search results that evoke positive emotions 2. The system of claim 1.

Images