system

Abstract

The system according to the embodiment aims to provide a wide range of knowledge intuitively based on the touch operation of the user. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives touch operations from a user. The analysis unit analyzes the touch operations received by the reception unit and searches for related information. The provision unit provides the user with the information analyzed by the analysis unit. The customization unit selects and provides specific information based on the user's learning history and interests, based on the information provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has limited the information that digital learning materials can provide, making it difficult to learn intuitively.

[0005] The system according to the embodiment aims to provide a wide range of knowledge intuitively based on the touch operation of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives a touch operation from a user. The analysis unit analyzes the touch operation received by the reception unit and searches for related information. The provision unit provides the user with the information analyzed by the analysis unit. The customization unit selects and provides specific information based on the user's learning history and interests, based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a wide range of knowledge intuitively based on the touch operation of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes a generative AI to provide information based on a user's touch operation. When a user touches digital learning materials, the system inputs the touch operation into a reception unit. The reception unit then inputs the touch operation into a generative AI, which analyzes the touch operation. The analysis unit searches for related information based on the user's touch operation and transmits it to a provision unit. The provision unit presents the analyzed information to the user. For example, if a user touches a specific creature or exhibit, detailed information about the creature or exhibit is provided. Furthermore, a customization unit provides individually customized information based on the user's learning history and interests. This allows users to intuitively learn about creatures and exhibits that are difficult to touch. For example, by prioritizing the presentation of information related to content the user has previously learned or areas of interest, more effective learning is possible. Thus, the system utilizes a generative AI to present accurate information based on the user's touch operation and intuitively provide a wide range of knowledge. This allows the system to instantly provide relevant information based on the user's touch operation and customize the information based on the user's learning history and interests.

[0029] An information provision system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives a user's touch operation. Examples of the user's touch operation include, but are not limited to, single taps, double taps, and swipes. The reception unit receives the touch operation, for example, when the user touches digital learning materials. The reception unit inputs the user's touch operation to a generation AI, which then analyzes the touch operation. The analysis unit uses the generation AI to search for related information based on the user's touch operation. The analysis may be performed by, for example, keyword extraction or context analysis, but is not limited to, examples. For example, the generation AI searches for related information based on the user's touch operation and transmits it to the provision unit. The provision unit provides the analyzed information to the user. The provision may be performed by, for example, a screen display, audio output, or notification, but is not limited to, examples. For example, the provision unit provides the analyzed information to the user by a screen display. The provision unit may also provide the analyzed information by audio output. The provision unit may also provide the analyzed information by notification. The customization unit customizes information based on the user's learning history and interests, based on the information provided by the providing unit. Customization is performed, for example, based on the user's previously viewed materials, study time, learning content, etc., but is not limited to these examples. For example, the customization unit preferentially provides related information based on the content the user has previously learned. The customization unit can also preferentially provide related information based on the user's interests. This allows the information providing system according to the embodiment to instantly provide related information based on the user's touch operation and customize the information based on the user's learning history and interests.

[0030] The reception unit can analyze the user's past touch operation history and select an appropriate reception method. For example, the reception unit prioritizes reception of touch operations that the user performed frequently in the past. The reception unit can also analyze the user's past touch operation patterns and propose an optimal reception method. The reception unit can also select an optimal reception timing, taking into account, for example, the time periods during which the user performed touch operations in the past. By analyzing the past touch operation history, the optimal reception method can be provided to the user. The analysis of the past touch operation history is performed using, for example, log data or statistical analysis. For example, the reception unit stores the user's touch operation history as log data and analyzes it using statistical analysis. The reception unit can also store the user's touch operation history in a database and analyze it using a machine learning algorithm. This allows the reception unit to analyze the user's past touch operation history in detail and select an appropriate reception method.

[0031] When receiving a touch operation, the reception unit can filter the touch operation based on the user's current learning status and areas of interest. For example, the reception unit preferentially receives touch operations related to the area the user is currently studying. The reception unit can also filter related touch operations based on the user's areas of interest. The reception unit can also select an appropriate touch operation according to the user's learning progress. This allows for providing more relevant information by filtering touch operations based on the user's learning status and areas of interest. The learning status can be identified based on criteria such as progress and level of understanding. For example, the reception unit stores the user's learning progress in a database and filters touch operations based on the progress. The reception unit can also evaluate the user's level of understanding based on test results and filter touch operations based on the level of understanding. The area of ​​interest can be identified based on criteria such as past search history and browsing history. For example, the reception unit can analyze the user's past search history and filter touch operations based on the area of ​​interest. The reception unit can also analyze the user's past browsing history and filter touch operations based on the area of ​​interest. This allows the reception unit to filter touch operations based on the user's current learning situation and areas of interest, and provide highly relevant information.

[0032] When receiving a touch operation, the reception unit can prioritize receiving highly relevant operations based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving touch operations related to that area. Furthermore, for example, when the user is on the move, the reception unit can also suggest optimal touch operations based on the user's current location. Furthermore, for example, when the user is in a specific facility, the reception unit can prioritize receiving touch operations related to the facility. This allows for the user to receive highly relevant operations preferentially in consideration of the user's geographical location information, thereby providing more relevant information. The geographical location information can be acquired, for example, using GPS data or a location information service. For example, the reception unit acquires the user's GPS data and identifies the user's current location. Furthermore, the reception unit can also identify the user's current location using a location information service. The highly relevant operations are selected based on criteria such as providing information based on the current location. For example, when the user is in a specific area, the reception unit prioritizes providing information related to that area. Furthermore, when the user is on the move, the reception unit can provide optimal information based on the user's current location. This allows the receiving unit to take into consideration the geographical location information of the user, to preferentially receive highly relevant operations, and to provide highly relevant information.

[0033] The reception unit can analyze the user's social media activity when receiving a touch operation and receive related operations. The reception unit, for example, preferentially receives related touch operations based on information shared by the user on social media. The reception unit can also, for example, analyze the user's social media activity history and suggest optimal touch operations. The reception unit can also, for example, receive related touch operations based on content in which the user has shown interest on social media. In this way, information related to the user can be provided by analyzing the social media activity. The analysis of social media activity is performed based on criteria such as the content of posts, the number of likes, and the number of followers. For example, the reception unit analyzes the content of posts by the user and selects related touch operations. The reception unit can also select related touch operations based on the number of likes by the user. Furthermore, the reception unit can select related touch operations based on the number of followers of the user. In this way, the reception unit can analyze the user's social media activity in detail and receive related operations.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the touch operation. For example, the analysis unit performs a detailed analysis on an important touch operation. Furthermore, the analysis unit can perform a standard analysis on a general touch operation. Furthermore, the analysis unit can perform a concise analysis on a simple touch operation. In this way, by adjusting the level of detail of the analysis based on the importance of the touch operation, more appropriate information can be provided. The importance of the touch operation is evaluated based on criteria such as the frequency of the operation and the impact of the operation. For example, the analysis unit stores the frequency of the user's touch operation in a database and evaluates the importance based on the frequency. Furthermore, the analysis unit can evaluate the impact of the user's touch operation and evaluate the importance based on the impact. In this way, the analysis unit can evaluate the importance of the touch operation in detail and adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of the touch operation during analysis. For example, the analysis unit can apply a biological analysis algorithm to a touch operation related to biology. The analysis unit can also apply a historical analysis algorithm to a touch operation related to history. The analysis unit can also apply a scientific analysis algorithm to a touch operation related to science. By applying different analysis algorithms depending on the category of the touch operation, more appropriate information can be provided. The touch operation categories are classified based on criteria such as the type of operation and the purpose of the operation. For example, the analysis unit can classify the user's touch operation by type and apply an analysis algorithm depending on the category. The analysis unit can also evaluate the purpose of the user's touch operation and apply an analysis algorithm depending on the purpose. This allows the analysis unit to classify the touch operation categories in detail and apply an appropriate analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time when the touch operation was submitted. For example, the analysis unit prioritizes analysis of recently performed touch operations. Furthermore, the analysis unit can also postpone, for example, touch operations performed in the past. Furthermore, the analysis unit can also prioritize analysis of touch operations performed during a specific time period. In this way, by determining the priority of analysis based on the time when the touch operation was submitted, more appropriate information can be provided. The submission time is acquired based on criteria such as the submission date and time or the submission frequency. For example, the analysis unit stores the submission date and time of the user's touch operation in a database and determines the priority based on the submission date and time. Furthermore, the analysis unit can evaluate the frequency of submission of the user's touch operation and determine the priority based on the frequency. In this way, the analysis unit can evaluate the submission time of the touch operation in detail and determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of touch operations. For example, the analysis unit prioritizes analysis of highly relevant touch operations. The analysis unit can also postpone analysis of less relevant touch operations, for example. The analysis unit can also analyze touch operations in the same category together. This allows for adjusting the order of analysis based on the relevance of touch operations, making it possible to provide more appropriate information. The evaluation of relevance is performed based on criteria such as similarity of content and related keywords, for example. For example, the analysis unit analyzes the content of the user's touch operations and evaluates the relevance based on similarity. The analysis unit can also extract keywords related to the user's touch operations and evaluate the relevance based on the keywords. This allows the analysis unit to evaluate the relevance of touch operations in detail and adjust the order of analysis.

[0038] The providing unit can adjust the level of detail to be provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. Furthermore, the providing unit can provide a standard explanation for general information, for example. Furthermore, the providing unit can provide a concise explanation for simple information, for example. By adjusting the level of detail to be provided based on the importance of the information, more appropriate information can be provided. The importance of the information is evaluated based on criteria such as the reliability of the information and the impact of the information. For example, the providing unit evaluates the reliability of the information and evaluates the importance based on the reliability. Furthermore, the providing unit can evaluate the impact of the information and evaluate the importance based on the impact. Thus, the providing unit can evaluate the importance of the information in detail and adjust the level of detail to be provided.

[0039] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply a biological providing algorithm to information about living things. The providing unit can also apply a historical providing algorithm to information about history. The providing unit can also apply a scientific providing algorithm to information about science. In this way, by applying different providing algorithms depending on the category of information, more appropriate information can be provided. Information categories are classified based on criteria such as news, academic papers, and entertainment. For example, the providing unit classifies information by category and applies a providing algorithm depending on the category. The providing unit can also evaluate the content of the information and apply a providing algorithm depending on the content. In this way, the providing unit can classify information categories in detail and apply an appropriate providing algorithm.

[0040] The providing unit can determine the priority of provision based on the time of submission of information when the information is provided. For example, the providing unit can provide information that has been provided recently with priority. The providing unit can also postpone information that has been provided in the past, for example. The providing unit can also provide information that has been provided during a specific time period with priority, for example. In this way, by determining the priority of provision based on the time of submission of information, more appropriate information can be provided. The submission time is acquired based on criteria such as the submission date and time or the submission frequency, for example. For example, the providing unit stores the submission date and time of information in a database and determines the priority based on the submission time. The providing unit can also evaluate the submission frequency of information and determine the priority based on the frequency. In this way, the providing unit can evaluate the submission time of information in detail and determine the priority of provision.

[0041] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. Furthermore, the providing unit can also postpone, for example, information with low relevance. Furthermore, the providing unit can also provide information of the same category together. In this way, by adjusting the order of providing based on the relevance of the information, more appropriate information can be provided. The evaluation of the relevance is performed based on criteria such as similarity of content and related keywords, for example. For example, the providing unit analyzes the content of the information and evaluates the relevance based on the similarity. Furthermore, the providing unit can extract related keywords for the information and evaluate the relevance based on the keywords. In this way, the providing unit can evaluate the relevance of the information in detail and adjust the order of providing the information.

[0042] During customization, the customization unit can analyze the user's past learning history and select the optimal customization method. The customization unit, for example, prioritizes providing related information based on what the user has learned in the past. The customization unit can also analyze the user's learning history and suggest the optimal customization method. The customization unit can also perform customization based on, for example, areas in which the user has shown interest in the past. This makes it possible to provide the optimal customization method by analyzing the user's past learning history. The analysis of the past learning history is performed using, for example, log data or statistical analysis. For example, the customization unit saves the user's learning history as log data and analyzes it using statistical analysis. The customization unit can also save the user's learning history in a database and analyze it using a machine learning algorithm. This makes it possible for the customization unit to analyze the user's past learning history in detail and select the optimal customization method.

[0043] During customization, the customization unit can customize the customization means based on the user's current learning situation. For example, the customization unit can prioritize providing information related to the field the user is currently studying. The customization unit can also perform appropriate customization, for example, according to the user's learning progress. The customization unit can also analyze the user's current learning situation and propose an optimal customization method. This makes it possible to provide more appropriate information by adjusting the customization means based on the user's current learning situation. The current learning situation is identified based on criteria such as progress and level of understanding. For example, the customization unit stores the user's learning progress in a database and performs customization based on the progress. The customization unit can also evaluate the user's level of understanding from test results, etc., and perform customization based on the level of understanding. This allows the customization unit to evaluate the user's current learning situation in detail and adjust the customization means.

[0044] During customization, the customization unit can select an appropriate customization method by taking into account the user's geographical location information. For example, if the user is in a specific area, the customization unit can prioritize providing information related to that area. Furthermore, for example, if the user is traveling, the customization unit can also suggest optimal customization based on the user's current location. Furthermore, for example, if the user is in a specific facility, the customization unit can prioritize providing information related to the facility. This allows for more relevant information to be provided by taking the user's geographical location information into consideration. The geographical location information can be acquired by methods such as GPS data or a location information service. For example, the customization unit can acquire the user's GPS data and identify the user's current location. Furthermore, the customization unit can also identify the user's current location using a location information service. The optimal customization method is selected based on criteria such as customization based on the user's operation pattern. For example, the customization unit can prioritize providing related information based on the user's current location. Furthermore, the customization unit can analyze the user's movement pattern and suggest an optimal customization method. This allows the customization unit to evaluate the user's geographical location information in detail and select an appropriate customization method.

[0045] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit can, for example, suggest relevant customizations based on information shared by the user on social media. The customization unit can also, for example, analyze the user's social media activity history and suggest an optimal customization method. The customization unit can also perform customization based on the content in which the user has shown interest on social media. This makes it possible to provide information relevant to the user by analyzing social media activity. The analysis of social media activity is performed based on criteria such as the content of posts, the number of likes, and the number of followers. For example, the customization unit can analyze the content of posts by the user and suggest relevant customizations. The customization unit can also suggest relevant customizations based on the number of likes by the user. Furthermore, the customization unit can suggest relevant customizations based on the number of followers by the user. This makes it possible for the customization unit to analyze the user's social media activity in detail and suggest customization methods.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit detects the pressure of the user's touch operation and can infer the intention of the operation based on the pressure. For example, a touch operation with strong pressure can be determined to be urgent and an immediate response can be made. A touch operation with light pressure can be determined to be a careful operation by the user and a detailed confirmation can be made. Furthermore, the reception unit can detect changes in pressure and understand the flow of the user's operation. This makes it possible to provide more appropriate information based on the pressure of the touch operation.

[0048] The providing unit can detect the speed of the user's touch operation and adjust the method of providing information based on the speed. For example, it can determine that a fast touch operation indicates a hurry and provide concise information. It can also determine that a slow touch operation indicates a request for detailed information and provide a detailed explanation. It can also detect changes in the speed of the touch operation and provide information according to the flow of the user's operation. This makes it possible to provide more appropriate information based on the speed of the touch operation.

[0049] The reception unit can analyze the frequency of the user's touch operations and determine the priority of the operations based on the frequency. For example, it can determine that frequently performed touch operations are of high importance and receive them first. It can also postpone rarely performed touch operations. It can also detect changes in the frequency of touch operations and adjust the priority according to the flow of the user's operations. This makes it possible to provide more appropriate information based on the frequency of touch operations.

[0050] The providing unit can detect the position of the user's touch operation and adjust the method of providing information based on the position. For example, if the touch operation is performed in the center of the screen, important information can be provided. Also, if the touch operation is performed on the edge of the screen, supplementary information can be provided. Furthermore, the unit can detect changes in the position of the touch operation and provide information according to the flow of the user's operation. This makes it possible to provide more appropriate information based on the position of the touch operation.

[0051] The reception unit can detect the direction of the user's touch operation and estimate the intention of the operation based on the direction. For example, it can determine that an upward swipe is a request to add information and provide related information. It can also determine that a downward swipe is a request to delete information and delete unnecessary information. It can also detect changes in the direction of the touch operation and provide information according to the flow of the user's operation. This makes it possible to provide more appropriate information based on the direction of the touch operation.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reception unit receives the user's touch operation. The user's touch operation includes single taps, double taps, swipes, etc. For example, when the user touches a digital teaching material, the touch operation is received. The reception unit also inputs the user's touch operation into the generation AI, which then analyzes the touch operation. Step 2: The analysis unit uses the generation AI to search for related information based on the user's touch operations. The analysis is performed using methods such as keyword extraction and context analysis. For example, the generation AI searches for related information based on the user's touch operations and sends it to the provision unit. Step 3: The providing unit provides the analyzed information to the user. The information is provided by a method such as a screen display, an audio output, or a notification. For example, the providing unit provides the analyzed information to the user by a screen display. The providing unit can also provide the analyzed information by audio output. Furthermore, the providing unit can also provide the analyzed information by a notification. Step 4: The customization unit customizes the information based on the user's learning history and interests, based on the information provided by the provision unit. The customization is performed based on the user's previously viewed materials, study time, learning content, etc. For example, the customization unit provides related information preferentially based on the content the user has previously learned. The customization unit can also provide related information preferentially based on the user's interests.

[0054] (Example 2) A system according to an embodiment of the present invention utilizes a generative AI to provide information based on a user's touch operation. When a user touches digital learning materials, the system inputs the touch operation into a reception unit. The reception unit then inputs the touch operation into a generative AI, which analyzes the touch operation. The analysis unit searches for related information based on the user's touch operation and transmits it to a provision unit. The provision unit presents the analyzed information to the user. For example, if a user touches a specific creature or exhibit, detailed information about the creature or exhibit is provided. Furthermore, a customization unit provides individually customized information based on the user's learning history and interests. This allows users to intuitively learn about creatures and exhibits that are difficult to touch. For example, by prioritizing the presentation of information related to content the user has previously learned or areas of interest, more effective learning is possible. Thus, the system utilizes a generative AI to present accurate information based on the user's touch operation and intuitively provide a wide range of knowledge. This allows the system to instantly provide relevant information based on the user's touch operation and customize the information based on the user's learning history and interests.

[0055] An information provision system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives a user's touch operation. Examples of the user's touch operation include, but are not limited to, single taps, double taps, and swipes. The reception unit receives the touch operation, for example, when the user touches digital learning materials. The reception unit inputs the user's touch operation to a generation AI, which then analyzes the touch operation. The analysis unit uses the generation AI to search for related information based on the user's touch operation. The analysis may be performed by, for example, keyword extraction or context analysis, but is not limited to, examples. For example, the generation AI searches for related information based on the user's touch operation and transmits it to the provision unit. The provision unit provides the analyzed information to the user. The provision may be performed by, for example, a screen display, audio output, or notification, but is not limited to, examples. For example, the provision unit provides the analyzed information to the user by a screen display. The provision unit may also provide the analyzed information by audio output. The provision unit may also provide the analyzed information by notification. The customization unit customizes information based on the user's learning history and interests, based on the information provided by the providing unit. Customization is performed, for example, based on the user's previously viewed materials, study time, learning content, etc., but is not limited to these examples. For example, the customization unit preferentially provides related information based on the content the user has previously learned. The customization unit can also preferentially provide related information based on the user's interests. This allows the information providing system according to the embodiment to instantly provide related information based on the user's touch operation and customize the information based on the user's learning history and interests.

[0056] The reception unit can estimate the user's emotions and adjust the timing of receiving a touch operation based on the estimated user's emotions. For example, if the user is excited, the reception unit can quickly receive the touch operation and respond immediately. Furthermore, for example, if the user is relaxed, the reception unit can slightly delay the reception of the touch operation, providing a relaxed feel to the operation. Furthermore, for example, if the user is stressed, the reception unit can smoothly receive the touch operation, reducing the stress of the operation. Thus, by adjusting the timing of receiving a touch operation according to the user's emotions, a more appropriate feel to the operation can be provided. The emotion can be estimated using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using biometric technology. This allows the receiving unit to grasp the user's emotions in detail and adjust the timing for receiving the touch operation.

[0057] The reception unit can analyze the user's past touch operation history and select an appropriate reception method. For example, the reception unit prioritizes reception of touch operations that the user performed frequently in the past. The reception unit can also analyze the user's past touch operation patterns and propose an optimal reception method. The reception unit can also select an optimal reception timing, taking into account, for example, the time periods during which the user performed touch operations in the past. By analyzing the past touch operation history, the optimal reception method can be provided to the user. The analysis of the past touch operation history is performed using, for example, log data or statistical analysis. For example, the reception unit stores the user's touch operation history as log data and analyzes it using statistical analysis. The reception unit can also store the user's touch operation history in a database and analyze it using a machine learning algorithm. This allows the reception unit to analyze the user's past touch operation history in detail and select an appropriate reception method.

[0058] When receiving a touch operation, the reception unit can filter the touch operation based on the user's current learning status and areas of interest. For example, the reception unit preferentially receives touch operations related to the area the user is currently studying. The reception unit can also filter related touch operations based on the user's areas of interest. The reception unit can also select an appropriate touch operation according to the user's learning progress. This allows for providing more relevant information by filtering touch operations based on the user's learning status and areas of interest. The learning status can be identified based on criteria such as progress and level of understanding. For example, the reception unit stores the user's learning progress in a database and filters touch operations based on the progress. The reception unit can also evaluate the user's level of understanding based on test results and filter touch operations based on the level of understanding. The area of ​​interest can be identified based on criteria such as past search history and browsing history. For example, the reception unit can analyze the user's past search history and filter touch operations based on the area of ​​interest. The reception unit can also analyze the user's past browsing history and filter touch operations based on the area of ​​interest. This allows the reception unit to filter touch operations based on the user's current learning situation and areas of interest, and provide highly relevant information.

[0059] The reception unit can estimate the user's emotions and determine the priority of touch operations to be received based on the estimated user's emotions. For example, when the user is excited, the reception unit can prioritize important touch operations. Furthermore, for example, when the user is relaxed, the reception unit can equally accept all touch operations. Furthermore, for example, when the user is stressed, the reception unit can prioritize simple touch operations. This enables more appropriate information to be provided by determining the priority of touch operations according to the user's emotions. The estimation of emotions is performed using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using biometric technology. This allows the reception unit to grasp the user's emotions in detail and determine the priority of touch operations.

[0060] When receiving a touch operation, the reception unit can prioritize receiving highly relevant operations based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving touch operations related to that area. Furthermore, for example, when the user is on the move, the reception unit can also suggest optimal touch operations based on the user's current location. Furthermore, for example, when the user is in a specific facility, the reception unit can prioritize receiving touch operations related to the facility. This allows for the user to receive highly relevant operations preferentially in consideration of the user's geographical location information, thereby providing more relevant information. The geographical location information can be acquired, for example, using GPS data or a location information service. For example, the reception unit acquires the user's GPS data and identifies the user's current location. Furthermore, the reception unit can also identify the user's current location using a location information service. The highly relevant operations are selected based on criteria such as providing information based on the current location. For example, when the user is in a specific area, the reception unit prioritizes providing information related to that area. Furthermore, when the user is on the move, the reception unit can provide optimal information based on the user's current location. This allows the receiving unit to take into consideration the geographical location information of the user, to preferentially receive highly relevant operations, and to provide highly relevant information.

[0061] The reception unit can analyze the user's social media activity when receiving a touch operation and receive related operations. The reception unit, for example, preferentially receives related touch operations based on information shared by the user on social media. The reception unit can also, for example, analyze the user's social media activity history and suggest optimal touch operations. The reception unit can also, for example, receive related touch operations based on content in which the user has shown interest on social media. In this way, information related to the user can be provided by analyzing the social media activity. The analysis of social media activity is performed based on criteria such as the content of posts, the number of likes, and the number of followers. For example, the reception unit analyzes the content of posts by the user and selects related touch operations. The reception unit can also select related touch operations based on the number of likes by the user. Furthermore, the reception unit can select related touch operations based on the number of followers of the user. In this way, the reception unit can analyze the user's social media activity in detail and receive related operations.

[0062] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows for more appropriate information to be provided by adjusting the way the analysis is presented depending on the user's emotions. Emotions can be estimated using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using biometric technology. This allows the analysis unit to grasp the user's emotions in detail and adjust the way the analysis is presented.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the touch operation. For example, the analysis unit performs a detailed analysis on an important touch operation. Furthermore, the analysis unit can perform a standard analysis on a general touch operation. Furthermore, the analysis unit can perform a concise analysis on a simple touch operation. In this way, by adjusting the level of detail of the analysis based on the importance of the touch operation, more appropriate information can be provided. The importance of the touch operation is evaluated based on criteria such as the frequency of the operation and the impact of the operation. For example, the analysis unit stores the frequency of the user's touch operation in a database and evaluates the importance based on the frequency. Furthermore, the analysis unit can evaluate the impact of the user's touch operation and evaluate the importance based on the impact. In this way, the analysis unit can evaluate the importance of the touch operation in detail and adjust the level of detail of the analysis.

[0064] The analysis unit can apply different analysis algorithms depending on the category of the touch operation during analysis. For example, the analysis unit can apply a biological analysis algorithm to a touch operation related to biology. The analysis unit can also apply a historical analysis algorithm to a touch operation related to history. The analysis unit can also apply a scientific analysis algorithm to a touch operation related to science. By applying different analysis algorithms depending on the category of the touch operation, more appropriate information can be provided. The touch operation categories are classified based on criteria such as the type of operation and the purpose of the operation. For example, the analysis unit can classify the user's touch operation by type and apply an analysis algorithm depending on the category. The analysis unit can also evaluate the purpose of the user's touch operation and apply an analysis algorithm depending on the purpose. This allows the analysis unit to classify the touch operation categories in detail and apply an appropriate analysis algorithm.

[0065] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is excited, the analysis unit can provide a visually appealing analysis. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using biometric technology. This allows the analysis unit to grasp the user's emotions in detail and adjust the length of the analysis.

[0066] During analysis, the analysis unit can determine the priority of analysis based on the time when the touch operation was submitted. For example, the analysis unit prioritizes analysis of recently performed touch operations. Furthermore, the analysis unit can also postpone, for example, touch operations performed in the past. Furthermore, the analysis unit can also prioritize analysis of touch operations performed during a specific time period. In this way, by determining the priority of analysis based on the time when the touch operation was submitted, more appropriate information can be provided. The submission time is acquired based on criteria such as the submission date and time or the submission frequency. For example, the analysis unit stores the submission date and time of the user's touch operation in a database and determines the priority based on the submission date and time. Furthermore, the analysis unit can evaluate the frequency of submission of the user's touch operation and determine the priority based on the frequency. In this way, the analysis unit can evaluate the submission time of the touch operation in detail and determine the priority of analysis.

[0067] During analysis, the analysis unit can adjust the order of analysis based on the relevance of touch operations. For example, the analysis unit prioritizes analysis of highly relevant touch operations. The analysis unit can also postpone analysis of less relevant touch operations, for example. The analysis unit can also analyze touch operations in the same category together. This allows for adjusting the order of analysis based on the relevance of touch operations, making it possible to provide more appropriate information. The evaluation of relevance is performed based on criteria such as similarity of content and related keywords, for example. For example, the analysis unit analyzes the content of the user's touch operations and evaluates the relevance based on similarity. The analysis unit can also extract keywords related to the user's touch operations and evaluate the relevance based on the keywords. This allows the analysis unit to evaluate the relevance of touch operations in detail and adjust the order of analysis.

[0068] The providing unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can provide detailed information. For example, if the user is in a hurry, the providing unit can provide concise information. For example, if the user is excited, the providing unit can provide visually appealing information. This allows the presentation method to be adjusted according to the user's emotion, thereby providing more appropriate information. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using biometric technology. This allows the providing unit to grasp the user's emotion in detail and adjust the presentation method.

[0069] The providing unit can adjust the level of detail to be provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. Furthermore, the providing unit can provide a standard explanation for general information, for example. Furthermore, the providing unit can provide a concise explanation for simple information, for example. By adjusting the level of detail to be provided based on the importance of the information, more appropriate information can be provided. The importance of the information is evaluated based on criteria such as the reliability of the information and the impact of the information. For example, the providing unit evaluates the reliability of the information and evaluates the importance based on the reliability. Furthermore, the providing unit can evaluate the impact of the information and evaluate the importance based on the impact. Thus, the providing unit can evaluate the importance of the information in detail and adjust the level of detail to be provided.

[0070] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply a biological providing algorithm to information about living things. The providing unit can also apply a historical providing algorithm to information about history. The providing unit can also apply a scientific providing algorithm to information about science. In this way, by applying different providing algorithms depending on the category of information, more appropriate information can be provided. Information categories are classified based on criteria such as news, academic papers, and entertainment. For example, the providing unit classifies information by category and applies a providing algorithm depending on the category. The providing unit can also evaluate the content of the information and apply a providing algorithm depending on the content. In this way, the providing unit can classify information categories in detail and apply an appropriate providing algorithm.

[0071] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, for example, if the user is relaxed, the providing unit can provide detailed information. Furthermore, for example, if the user is excited, the providing unit can provide visually appealing information. This allows the length of information provided to be adjusted according to the user's emotions, thereby providing more appropriate information. The estimation of emotions is performed using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using biometric technology. This allows the providing unit to grasp the user's emotions in detail and adjust the length of the information provided.

[0072] The providing unit can determine the priority of provision based on the time of submission of information when the information is provided. For example, the providing unit can provide information that has been provided recently with priority. The providing unit can also postpone information that has been provided in the past, for example. The providing unit can also provide information that has been provided during a specific time period with priority, for example. In this way, by determining the priority of provision based on the time of submission of information, more appropriate information can be provided. The submission time is acquired based on criteria such as the submission date and time or the submission frequency, for example. For example, the providing unit stores the submission date and time of information in a database and determines the priority based on the submission time. The providing unit can also evaluate the submission frequency of information and determine the priority based on the frequency. In this way, the providing unit can evaluate the submission time of information in detail and determine the priority of provision.

[0073] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. Furthermore, the providing unit can also postpone, for example, information with low relevance. Furthermore, the providing unit can also provide information of the same category together. In this way, by adjusting the order of providing based on the relevance of the information, more appropriate information can be provided. The evaluation of the relevance is performed based on criteria such as similarity of content and related keywords, for example. For example, the providing unit analyzes the content of the information and evaluates the relevance based on the similarity. Furthermore, the providing unit can extract related keywords for the information and evaluate the relevance based on the keywords. In this way, the providing unit can evaluate the relevance of the information in detail and adjust the order of providing the information.

[0074] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user's emotions. For example, the customization unit can provide detailed customization when the user is relaxed. Furthermore, the customization unit can provide concise customization when the user is in a hurry. Furthermore, the customization unit can provide visually appealing customization when the user is excited. This allows the customization method to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the customization unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The customization unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the customization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using biometric technology. This allows the customization unit to grasp the user's emotions in detail and adjust the customization method.

[0075] During customization, the customization unit can analyze the user's past learning history and select the optimal customization method. The customization unit, for example, prioritizes providing related information based on what the user has learned in the past. The customization unit can also analyze the user's learning history and suggest the optimal customization method. The customization unit can also perform customization based on, for example, areas in which the user has shown interest in the past. This makes it possible to provide the optimal customization method by analyzing the user's past learning history. The analysis of the past learning history is performed using, for example, log data or statistical analysis. For example, the customization unit saves the user's learning history as log data and analyzes it using statistical analysis. The customization unit can also save the user's learning history in a database and analyze it using a machine learning algorithm. This makes it possible for the customization unit to analyze the user's past learning history in detail and select the optimal customization method.

[0076] During customization, the customization unit can customize the customization means based on the user's current learning situation. For example, the customization unit can prioritize providing information related to the field the user is currently studying. The customization unit can also perform appropriate customization, for example, according to the user's learning progress. The customization unit can also analyze the user's current learning situation and propose an optimal customization method. This makes it possible to provide more appropriate information by adjusting the customization means based on the user's current learning situation. The current learning situation is identified based on criteria such as progress and level of understanding. For example, the customization unit stores the user's learning progress in a database and performs customization based on the progress. The customization unit can also evaluate the user's level of understanding from test results, etc., and perform customization based on the level of understanding. This allows the customization unit to evaluate the user's current learning situation in detail and adjust the customization means.

[0077] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, if the user is relaxed, the customization unit can prioritize providing detailed customization. Furthermore, for example, if the user is in a hurry, the customization unit can prioritize providing simple customization. Furthermore, for example, if the user is excited, the customization unit can prioritize providing visually appealing customization. By determining the priority of customization according to the user's emotions, more appropriate information can be provided. The estimation of emotions is performed using technologies such as facial expression recognition, voice analysis, and biometrics. For example, the customization unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The customization unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the customization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using biometric technology. This allows the customization unit to grasp the user's emotions in detail and determine the priority of customization.

[0078] During customization, the customization unit can select an appropriate customization method by taking into account the user's geographical location information. For example, if the user is in a specific area, the customization unit can prioritize providing information related to that area. Furthermore, for example, if the user is traveling, the customization unit can also suggest optimal customization based on the user's current location. Furthermore, for example, if the user is in a specific facility, the customization unit can prioritize providing information related to the facility. This allows for more relevant information to be provided by taking the user's geographical location information into consideration. The geographical location information can be acquired by methods such as GPS data or a location information service. For example, the customization unit can acquire the user's GPS data and identify the user's current location. Furthermore, the customization unit can also identify the user's current location using a location information service. The optimal customization method is selected based on criteria such as customization based on the user's operation pattern. For example, the customization unit can prioritize providing related information based on the user's current location. Furthermore, the customization unit can analyze the user's movement pattern and suggest an optimal customization method. This allows the customization unit to evaluate the user's geographical location information in detail and select an appropriate customization method.

[0079] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. The customization unit can, for example, suggest relevant customizations based on information shared by the user on social media. The customization unit can also, for example, analyze the user's social media activity history and suggest an optimal customization method. The customization unit can also perform customization based on the content in which the user has shown interest on social media. This makes it possible to provide information relevant to the user by analyzing social media activity. The analysis of social media activity is performed based on criteria such as the content of posts, the number of likes, and the number of followers. For example, the customization unit can analyze the content of posts by the user and suggest relevant customizations. The customization unit can also suggest relevant customizations based on the number of likes by the user. Furthermore, the customization unit can suggest relevant customizations based on the number of followers by the user. This makes it possible for the customization unit to analyze the user's social media activity in detail and suggest customization methods. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's touch operation using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the touch operation using a generation AI. The provision unit provides the analyzed information to the user using the display 40A or speaker 40B of the smart device 14. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes information based on the user's learning history and interests. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, and customization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's touch operation using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the touch operation using a generation AI. The provision unit provides the analyzed information to the user using the speaker 240 of the smart glasses 214. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes information based on the user's learning history and interests. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, and customization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's touch operation using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the touch operation using a generation AI. The provision unit provides the analyzed information to the user using the speaker 240 of the headset type terminal 314. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes information based on the user's learning history and interests. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a touch operation by the user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the touch operation using a generation AI. The provision unit provides the analyzed information to the user using the speaker 240 of the robot 414. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes information based on the user's learning history and interests.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit detects the pressure of the user's touch operation and can infer the intention of the operation based on the pressure. For example, a touch operation with strong pressure can be determined to be urgent and an immediate response can be made. A touch operation with light pressure can be determined to be a careful operation by the user and a detailed confirmation can be made. Furthermore, the reception unit can detect changes in pressure and understand the flow of the user's operation. This makes it possible to provide more appropriate information based on the pressure of the touch operation.

[0082] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is excited, it can prioritize analyzing information that is interesting. Also, if the user is relaxed, it can prioritize analyzing information that is suitable for learning. Furthermore, if the user is stressed, it can prioritize analyzing information that will help the user relax. In this way, by adjusting the analysis priority according to the user's emotions, it is possible to provide more appropriate information.

[0083] The providing unit can detect the speed of the user's touch operation and adjust the method of providing information based on the speed. For example, it can determine that a fast touch operation indicates a hurry and provide concise information. It can also determine that a slow touch operation indicates a request for detailed information and provide a detailed explanation. It can also detect changes in the speed of the touch operation and provide information according to the flow of the user's operation. This makes it possible to provide more appropriate information based on the speed of the touch operation.

[0084] The customization unit can estimate the user's emotions and adjust the customization content based on the estimated user's emotions. For example, if the user is relaxed, detailed customization can be provided. If the user is in a hurry, simple customization can be provided. Furthermore, if the user is excited, visually appealing customization can be provided. In this way, by adjusting the customization content according to the user's emotions, more appropriate information can be provided.

[0085] The reception unit can analyze the frequency of the user's touch operations and determine the priority of the operations based on the frequency. For example, it can determine that frequently performed touch operations are of high importance and receive them first. It can also postpone rarely performed touch operations. It can also detect changes in the frequency of touch operations and adjust the priority according to the flow of the user's operations. This makes it possible to provide more appropriate information based on the frequency of touch operations.

[0086] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be provided. If the user is in a hurry, a concise analysis can be provided. Furthermore, if the user is excited, a visually appealing analysis can be provided. In this way, by adjusting the depth of the analysis according to the user's emotions, more appropriate information can be provided.

[0087] The providing unit can detect the position of the user's touch operation and adjust the method of providing information based on the position. For example, if the touch operation is performed in the center of the screen, important information can be provided. Also, if the touch operation is performed on the edge of the screen, supplementary information can be provided. Furthermore, the unit can detect changes in the position of the touch operation and provide information according to the flow of the user's operation. This makes it possible to provide more appropriate information based on the position of the touch operation.

[0088] The customization unit can estimate the user's emotions and adjust the frequency of customization based on the estimated user's emotions. For example, if the user is relaxed, the customization unit can provide more frequent customization. If the user is in a hurry, the customization unit can reduce the frequency of customization. Furthermore, if the user is excited, the customization unit can provide more visually appealing customization. Thus, by adjusting the frequency of customization according to the user's emotions, more appropriate information can be provided.

[0089] The reception unit can detect the direction of the user's touch operation and estimate the intention of the operation based on the direction. For example, it can determine that an upward swipe is a request to add information and provide related information. It can also determine that a downward swipe is a request to delete information and delete unnecessary information. It can also detect changes in the direction of the touch operation and provide information according to the flow of the user's operation. This makes it possible to provide more appropriate information based on the direction of the touch operation.

[0090] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis can be performed immediately. If the user is in a hurry, the analysis can be postponed. Furthermore, if the user is excited, a visually appealing analysis can be provided. In this way, by adjusting the timing of the analysis according to the user's emotions, more appropriate information can be provided.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives the user's touch operation. The user's touch operation includes single taps, double taps, swipes, etc. For example, when the user touches a digital teaching material, the touch operation is received. The reception unit also inputs the user's touch operation into the generation AI, which then analyzes the touch operation. Step 2: The analysis unit uses the generation AI to search for related information based on the user's touch operations. The analysis is performed using methods such as keyword extraction and context analysis. For example, the generation AI searches for related information based on the user's touch operations and sends it to the provision unit. Step 3: The providing unit provides the analyzed information to the user. The information is provided by a method such as a screen display, an audio output, or a notification. For example, the providing unit provides the analyzed information to the user by a screen display. The providing unit can also provide the analyzed information by audio output. Furthermore, the providing unit can also provide the analyzed information by a notification. Step 4: The customization unit customizes the information based on the user's learning history and interests, based on the information provided by the provision unit. The customization is performed based on the user's previously viewed materials, study time, learning content, etc. For example, the customization unit provides related information preferentially based on the content the user has previously learned. The customization unit can also provide related information preferentially based on the user's interests.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a touch operation by a user; an analysis unit that analyzes the touch operation received by the reception unit and searches for related information; a providing unit that provides the information analyzed by the analyzing unit to a user; a customization unit that selects and provides specific information based on the information provided by the providing unit and based on the user's learning history and interests. A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of accepting touch operations based on the estimated user emotions. The system of claim 1 .

3. The reception unit Analyze the user's past touch operation history and select the appropriate reception method The system of claim 1 .

4. The reception unit When touch operations are received, filtering is performed to select and provide specific information based on the user's current learning situation and areas of interest. The system of claim 1 .

5. The reception unit Estimate the user's emotions and determine the priority of touch operations to be accepted based on the estimated user emotions. The system of claim 1 .

6. The reception unit When accepting touch operations, it prioritizes operations that are highly relevant based on the user's geographical location information. The system of claim 1 .

7. The reception unit When receiving a touch operation, analyze the user's social media activity and receive related operations. The system of claim 1 .

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

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