System

Abstract

An object of a system according to an embodiment is to display optimal information in real time on the basis of a user's interest or concern.SOLUTION: A system includes a data collection unit, an analysis unit, and a display unit. The data collection unit collects line-of-sight data of a user. The analysis unit analyzes the gaze data collected by the data collection unit, and specifies an interest or concern of the user. The display unit displays information suitable for the user based on the interest or concern specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to find the information they need from a large amount of AR / MR data.

[0005] The system according to the embodiment aims to display optimal information in real time based on the user's interests and concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a display unit. The data collection unit collects gaze data of a user. The analysis unit analyzes the gaze data collected by the data collection unit and identifies the user's interests and concerns. The display unit displays information appropriate for the user based on the interests and concerns identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can display optimal information in real time based on the user's interests and concerns. [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) An information provision system according to an embodiment of the present invention collects and analyzes a user's gaze data and displays optimized information in real time. In the information provision system, a data collection unit collects information about which AR / MR data a user frequently views, and an AI analyzes the collected data to identify the user's interests. Furthermore, a display unit displays information optimized for the user based on the analysis results. For example, the information provision system collects data such as the user's gaze and gaze duration. For example, if a user spends a long time looking at a particular advertisement or signboard in town, that data is collected. Next, the information provision system uses an AI to analyze the collected data. The AI ​​analyzes the user's gaze data and gaze duration to identify the user's interests. For example, if a user frequently views advertisements for a particular brand, it determines that the user has a high interest in that brand. Furthermore, the information provision system displays information optimized for the user based on the analysis results. For example, if a user is interested in a particular brand, information and advertisements related to that brand are displayed. This allows the information provision system to efficiently obtain the information needed by the user from the abundance of AR / MR data available in town. This allows the information provision system to collect and analyze user gaze data and display optimized information in real time. For example, information tailored to the user's interests, such as tourist information or sales information at a shopping mall, can be displayed. This allows users to enjoy a comfortable AR / MR experience without missing any information that is useful to them.

[0029] An information provision system according to an embodiment includes a data collection unit, an analysis unit, and a display unit. The data collection unit collects gaze data of a user. The gaze data includes, but is not limited to, gaze position, gaze movement pattern, and gaze duration. The data collection unit collects the gaze data using, for example, a camera or a sensor mounted on the AR / MR glasses. The data collection unit can also collect the user's gaze data in real time. For example, the data collection unit detects the user's gaze position using a camera and records the gaze movement pattern. The data collection unit can also measure the user's gaze duration using a sensor. The analysis unit uses AI to analyze the gaze data collected by the data collection unit and identify the user's interests. The interests are identified based on, for example, gaze duration and gaze frequency, but are not limited to, for example. For example, the analysis unit performs analysis using an AI model that inputs gaze data and outputs the user's interests. The analysis unit can also analyze the gaze data pattern to identify the user's interests. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past gaze data. The display unit displays information optimized for the user based on the interests and concerns identified by the analysis unit. Suitable information includes, but is not limited to, information based on the user's past behavioral history and current context. For example, the display unit displays information and advertisements related to brands in which the user is interested. The display unit can also filter information and determine display priorities based on the user's interests and concerns. Furthermore, the display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. This allows the information provision system according to the embodiment to collect and analyze the user's gaze data and display optimized information in real time. For example, if a user spends a long time looking at a particular advertisement or signboard in town, the data is collected and analyzed, and information optimized for the user is displayed. This allows the user to efficiently obtain the information they need.

[0030] The data collection unit can collect gaze data using a camera or sensor mounted on the AR / MR glasses. The AR / MR glasses are equipped with, for example, a camera or sensor, and can collect gaze data using these. The data collection unit, for example, detects the position of the user's gaze using a camera mounted on the AR / MR glasses. The data collection unit can also record the user's gaze movement pattern using a sensor. Furthermore, the data collection unit can measure the user's gaze time using a camera or sensor. This makes it possible to collect gaze data using the camera or sensor mounted on the AR / MR glasses. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input gaze data acquired by a camera or sensor to a generation AI and have the generation AI analyze the gaze data.

[0031] The analysis unit can analyze a user's gaze data or gaze time to identify the user's interests. The analysis unit, for example, analyzes the user's gaze data to identify the user's interests. For example, the analysis unit can identify the user's interests based on gaze duration or gaze frequency. The analysis unit can also analyze the user's gaze time to identify the user's interests. For example, the analysis unit performs analysis using an AI model that inputs gaze data and outputs the user's interests. The analysis unit can also analyze patterns of gaze data to identify the user's interests. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past gaze data. In this way, the user's interests can be identified by analyzing the gaze data and gaze time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input gaze data to a generation AI and cause the generation AI to identify the user's interests.

[0032] The display unit can select information based on the user's interests and determine display priorities. The display unit, for example, filters information based on the user's interests and determines display priorities. For example, the display unit selects information based on the user's past behavioral history or current context. The display unit can also determine display priorities based on the importance of information or the user's level of interest. For example, the display unit preferentially displays information and advertisements related to brands in which the user is interested. The display unit can also filter information based on the user's interests and determine display priorities. This allows the optimal information to be provided by filtering information based on the user's interests and determining display priorities. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that selects information based on the user's interests and determines display priorities.

[0033] The display unit can display information or advertisements according to the user's interests. The display unit, for example, displays information and advertisements according to the user's interests. For example, the display unit displays information and advertisements related to brands in which the user is interested. The display unit can also filter information based on the user's interests and determine display priorities. For example, the display unit selects information based on the user's past behavioral history and current context and determines display priorities. This allows information and advertisements according to the user's interests to be displayed, thereby providing useful information to the user. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that selects information based on the user's interests and determines display priorities.

[0034] The data collection unit can anonymize or encrypt data to protect the user's privacy. The data collection unit, for example, anonymizes data to protect the user's privacy. For example, the data collection unit anonymizes the user's personal information when collecting gaze data. The data collection unit can also encrypt data. For example, the data collection unit encrypts the data end-to-end when collecting gaze data. In this way, the anonymization or encryption of data can protect the user's privacy. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can protect the user's privacy when collecting gaze data by using an AI model that anonymizes or encrypts the data.

[0035] The data collection unit can analyze the user's past gaze data and select an appropriate collection method. The data collection unit, for example, analyzes the user's past gaze data and selects the optimal collection method. For example, the data collection unit sets gaze data collection points based on information the user frequently viewed in the past. The data collection unit can also concentrate collection during specific time periods based on the user's past gaze data. Furthermore, the data collection unit can analyze the user's gaze data patterns and suggest efficient collection methods. In this way, the optimal collection method can be selected by analyzing the past gaze data. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past gaze data into a generation AI and have the generation AI select the optimal collection method.

[0036] The data collection unit can perform filtering based on the user's current activity or environment when collecting gaze data. For example, the data collection unit can perform filtering based on the user's current activity or environment when collecting gaze data. For example, when the user is walking, the data collection unit can temporarily stop collecting gaze data to prioritize safety. The data collection unit can also strengthen collection of gaze data and obtain more detailed data when the user is stationary. Furthermore, when the user is in a specific environment (e.g., indoors or outdoors), the data collection unit can perform filtering according to the environment. This enables more appropriate data collection by filtering based on the user's activity and environment. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's current activity and environmental data into the generation AI and have the generation AI perform filtering.

[0037] When collecting gaze data, the data collection unit can select an appropriate collection means depending on the user's input method. For example, when collecting gaze data, the data collection unit selects the optimal collection means depending on the user's input method (voice, text, gesture, etc.). For example, when the user uses voice input, the data collection unit simultaneously collects voice data and gaze data. Also, when the user uses text input, the data collection unit can collect text data and gaze data in conjunction with each other. Furthermore, when the user uses gesture input, the data collection unit can collect gesture data and gaze data in an integrated manner. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input user input method data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting gaze data, the data collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when collecting gaze data, the data collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration. For example, when collecting gaze data, the data collection unit prioritizes collecting gaze data related to a specific tourist spot. Also, when the user is in a shopping mall, the data collection unit can prioritize collecting gaze data related to stores and products. Furthermore, when the user is using public transportation, the data collection unit can prioritize collecting gaze data related to traffic information. In this way, highly relevant data can be collected preferentially by taking the geographical location information into consideration. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The data collection unit can collect related data based on the user's social media activity when collecting gaze data. For example, the data collection unit can analyze the user's social media activity and collect related data when collecting gaze data. For example, the data collection unit can collect gaze data related to locations where the user has checked in on social media. The data collection unit can also analyze the content of the user's social media posts and collect related gaze data. Furthermore, the data collection unit can collect related gaze data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related gaze data.

[0040] The data collection unit can customize the collection method based on the user's past feedback when collecting gaze data. For example, the data collection unit customizes the collection method by reflecting the user's past feedback when collecting gaze data. For example, the data collection unit adjusts the gaze data collection method based on feedback provided by the user in the past. The data collection unit can also set specific data collection points based on the user's past feedback. Furthermore, the data collection unit can customize the frequency and timing of gaze data collection by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the gaze data. The analysis unit adjusts the level of detail of the analysis based on, for example, the priority of the gaze data. For example, the analysis unit performs a detailed analysis on gaze data with high importance. The analysis unit can also perform a simplified analysis on gaze data with low importance. Furthermore, the analysis unit can set the priority of the analysis according to the importance of the gaze data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the priority of the gaze data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the type of gaze data. The analysis unit applies different analysis algorithms depending on, for example, the type of gaze data. For example, the analysis unit applies an algorithm that analyzes advertising effectiveness to gaze data related to advertisements. The analysis unit can also apply an algorithm that analyzes guidance effectiveness to gaze data related to signboards. Furthermore, the analysis unit can apply an algorithm that analyzes purchasing intent to gaze data related to products. In this way, applying different analysis algorithms depending on the type of gaze data enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of gaze data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the gaze data was acquired. The analysis unit determines the priority of analysis based on, for example, the time when the gaze data was acquired. For example, the analysis unit prioritizes the analysis of the most recent gaze data. The analysis unit can also prioritize current gaze data while referring to past gaze data. Furthermore, the analysis unit can set the priority of analysis according to the time when the gaze data was acquired. This enables efficient analysis by determining the priority of analysis based on the time when the gaze data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the gaze data was acquired into the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the gaze data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the gaze data. For example, the analysis unit prioritizes analysis of highly relevant gaze data. The analysis unit can also postpone analysis of less relevant gaze data. Furthermore, the analysis unit can set the order of analysis according to the relevance of the gaze data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the gaze data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the gaze data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's knowledge level. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's knowledge level. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have specialized knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's knowledge level. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's knowledge level. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's knowledge level data into a generation AI and have the generation AI use technical terms in the analysis.

[0047] The display unit can adjust the level of detail of the display based on the priority of the information when displaying the information. The display unit adjusts the level of detail of the display based on, for example, the priority of the information. For example, the display unit displays information of high importance in detail. The display unit can also display information of low importance in a simplified manner. Furthermore, the display unit can set a display priority according to the priority of the information. This enables efficient information display by adjusting the level of detail of the display based on the priority of the information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information priority data to a generation AI and cause the generation AI to adjust the level of detail of the display.

[0048] The display unit can apply different display algorithms depending on the type of information when displaying the information. The display unit applies different display algorithms depending on the type of information, for example. For example, the display unit applies a display algorithm that enhances advertising effectiveness to advertising information. The display unit can also apply a display algorithm that enhances guidance effectiveness to guidance information. Furthermore, the display unit can apply a display algorithm that enhances purchasing motivation to product information. In this way, by applying different display algorithms depending on the type of information, more effective information display is possible. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information type data to a generation AI and cause the generation AI to apply different display algorithms.

[0049] The display unit can improve the accuracy of the display based on the user's past display results when displaying. The display unit, for example, improves the accuracy of the display by referring to the user's past display results. For example, the display unit corrects the current display result based on the user's past display results. The display unit can also extract a specific pattern from the user's past display results and reflect it in the current display. Furthermore, the display unit can optimize the display algorithm by referring to the user's past display results. In this way, the accuracy of the display can be improved by referring to the past display results. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past display result data into a generation AI and cause the generation AI to improve the accuracy of the display.

[0050] The display unit can determine the display priority based on the time when the information was acquired when it was displayed. The display unit determines the display priority based on, for example, the time when the information was acquired. For example, the display unit prioritizes displaying the latest information. The display unit can also prioritize current information while referring to past information. Furthermore, the display unit can set the display priority according to the time when the information was acquired. This enables efficient information display by determining the display priority based on the time when the information was acquired. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information acquisition time data to the generation AI and have the generation AI determine the display priority.

[0051] The display unit can adjust the display order based on the relevance of the information when displaying the information. The display unit adjusts the display order based on, for example, the relevance of the information. For example, the display unit prioritizes display of highly relevant information. The display unit can also postpone display of less relevant information. Furthermore, the display unit can set the display order according to the relevance of the information. This enables efficient information display by adjusting the display order based on the relevance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input relevance data of the information to a generation AI and cause the generation AI to adjust the display order.

[0052] The display unit can adjust the use of technical terms in the display according to the user's knowledge level when displaying information. The display unit can adjust the use of technical terms in the display according to the user's knowledge level, for example. For example, if the user has specialized knowledge, the display unit can provide a display that makes extensive use of technical terms. Furthermore, if the user does not have specialized knowledge, the display unit can also provide a display in simple language. Furthermore, the display unit can adjust the way the display is expressed according to the user's knowledge level. This allows for more easily understandable information display by adjusting the use of technical terms in the display according to the user's knowledge level. Some or all of the above-described processing in the display unit can be performed, for example, using AI or without AI. For example, the display unit can input the user's knowledge level data into a generation AI and have the generation AI execute the use of technical terms in the display.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The data collection unit can collect voice data and gesture data in addition to gaze data of users. For example, by collecting what a user is saying while watching a specific advertisement and analyzing that content, it is possible to identify the correlation between gaze data and voice data. Also, if a user makes a gesture of pointing in a specific direction, the gesture data can be collected and analyzed in combination with gaze data. Furthermore, the data collection unit can collect data such as the user's walking pattern and movement speed, and analyze the data in combination with gaze data. This allows for the collection of a variety of data, including not only gaze data but also voice, gesture, and movement data, enabling more accurate analysis.

[0055] The display unit can dynamically adjust the display position of information based on the user's gaze data. For example, if the user looks at a particular area for a long time, information related to that area can be displayed. Also, if the user moves their gaze, the displayed information can move accordingly. Furthermore, the display unit can dynamically change the display size and color of information based on the user's gaze data. This allows the display of information to be optimized to match the user's gaze, providing a more intuitive and easy-to-use interface.

[0056] The data collection unit can collect environmental data in addition to the user's gaze data. For example, it can collect environmental data such as ambient volume, light intensity, and temperature, and analyze it in combination with the gaze data. The data collection unit can also collect the user's location information and analyze it in combination with the gaze data. Furthermore, the data collection unit can collect the user's device usage history and application operation logs, and analyze them in combination with the gaze data. This allows for the collection of a variety of data, including not only gaze data but also environmental data, location information, and device usage history, enabling more accurate analysis.

[0057] The display unit can customize the information display format based on the user's gaze data. For example, if the user prefers visual information, a display format that makes extensive use of graphs and charts can be provided. If the user prefers text information, detailed explanatory text can be displayed. Furthermore, the display unit can adjust the information display speed based on the user's gaze data. This allows information to be displayed in accordance with the user's preferences, resulting in more effective information provision.

[0058] The display unit can dynamically change the display order of information based on the user's gaze data. For example, if the user keeps their gaze fixed on a particular piece of information, the display unit can prioritize the display of that information. Also, if the user frequently moves their gaze, the display order of the information can be changed to display information in accordance with the movement of the user's gaze. Furthermore, the display unit can adjust the timing of displaying information based on the user's gaze data. This makes it possible to display information in accordance with the user's gaze, thereby providing more effective information.

[0059] The data collection unit can detect changes in the user's interests in real time based on the user's gaze data. For example, if there is a sudden change in the amount of time the user's gaze stays on a screen, it can be determined that the user's interests have changed. Also, if the gaze movement pattern changes, it can be inferred that the user's interests have changed. Furthermore, the data collection unit can compare and analyze the user's gaze data with past gaze data to more accurately detect changes in interests. This makes it possible to provide appropriate information and adjust the interface in response to changes in the user's interests.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection unit collects the user's gaze data. The gaze data includes the gaze position, gaze movement patterns, and gaze duration. The data collection unit collects gaze data using cameras and sensors installed in the AR / MR glasses, and can also collect it in real time. For example, the data collection unit uses a camera to detect the user's gaze position and record the gaze movement patterns, and uses a sensor to measure the user's gaze duration. Step 2: The analysis unit uses AI to analyze the gaze data collected by the data collection unit and identify the user's interests. Interests are identified based on the duration of gaze and the frequency of gaze. The analysis unit inputs gaze data and performs analysis using an AI model that outputs the user's interests. It also analyzes patterns in the gaze data and improves the accuracy of the analysis by referring to the user's past gaze data. Step 3: The display unit displays information optimized for the user based on the interests and concerns identified by the analysis unit. Suitable information includes information based on the user's past behavioral history and current context. For example, it displays information and advertisements related to brands that the user is interested in, and filters the information to determine the display priority. Furthermore, it estimates the user's emotions and adjusts the display method based on the estimated emotions.

[0062] (Example 2) An information provision system according to an embodiment of the present invention collects and analyzes a user's gaze data and displays optimized information in real time. In the information provision system, a data collection unit collects information about which AR / MR data a user frequently views, and an AI analyzes the collected data to identify the user's interests. Furthermore, a display unit displays information optimized for the user based on the analysis results. For example, the information provision system collects data such as the user's gaze and gaze duration. For example, if a user spends a long time looking at a particular advertisement or signboard in town, that data is collected. Next, the information provision system uses an AI to analyze the collected data. The AI ​​analyzes the user's gaze data and gaze duration to identify the user's interests. For example, if a user frequently views advertisements for a particular brand, it determines that the user has a high interest in that brand. Furthermore, the information provision system displays information optimized for the user based on the analysis results. For example, if a user is interested in a particular brand, information and advertisements related to that brand are displayed. This allows the information provision system to efficiently obtain the information needed by the user from the abundance of AR / MR data available in town. This allows the information provision system to collect and analyze user gaze data and display optimized information in real time. For example, information tailored to the user's interests, such as tourist information or sales information at a shopping mall, can be displayed. This allows users to enjoy a comfortable AR / MR experience without missing any information that is useful to them.

[0063] An information provision system according to an embodiment includes a data collection unit, an analysis unit, and a display unit. The data collection unit collects gaze data of a user. The gaze data includes, but is not limited to, gaze position, gaze movement pattern, and gaze duration. The data collection unit collects the gaze data using, for example, a camera or a sensor mounted on the AR / MR glasses. The data collection unit can also collect the user's gaze data in real time. For example, the data collection unit detects the user's gaze position using a camera and records the gaze movement pattern. The data collection unit can also measure the user's gaze duration using a sensor. The analysis unit uses AI to analyze the gaze data collected by the data collection unit and identify the user's interests. The interests are identified based on, for example, gaze duration and gaze frequency, but are not limited to, for example. For example, the analysis unit performs analysis using an AI model that inputs gaze data and outputs the user's interests. The analysis unit can also analyze the gaze data pattern to identify the user's interests. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past gaze data. The display unit displays information optimized for the user based on the interests and concerns identified by the analysis unit. Suitable information includes, but is not limited to, information based on the user's past behavioral history and current context. For example, the display unit displays information and advertisements related to brands in which the user is interested. The display unit can also filter information and determine display priorities based on the user's interests and concerns. Furthermore, the display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. This allows the information provision system according to the embodiment to collect and analyze the user's gaze data and display optimized information in real time. For example, if a user spends a long time looking at a particular advertisement or signboard in town, the data is collected and analyzed, and information optimized for the user is displayed. This allows the user to efficiently obtain the information they need.

[0064] The data collection unit can collect gaze data using a camera or sensor mounted on the AR / MR glasses. The AR / MR glasses are equipped with, for example, a camera or sensor, and can collect gaze data using these. The data collection unit, for example, detects the position of the user's gaze using a camera mounted on the AR / MR glasses. The data collection unit can also record the user's gaze movement pattern using a sensor. Furthermore, the data collection unit can measure the user's gaze time using a camera or sensor. This makes it possible to collect gaze data using the camera or sensor mounted on the AR / MR glasses. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input gaze data acquired by a camera or sensor to a generation AI and have the generation AI analyze the gaze data.

[0065] The analysis unit can analyze a user's gaze data or gaze time to identify the user's interests. The analysis unit, for example, analyzes the user's gaze data to identify the user's interests. For example, the analysis unit can identify the user's interests based on gaze duration or gaze frequency. The analysis unit can also analyze the user's gaze time to identify the user's interests. For example, the analysis unit performs analysis using an AI model that inputs gaze data and outputs the user's interests. The analysis unit can also analyze patterns of gaze data to identify the user's interests. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past gaze data. In this way, the user's interests can be identified by analyzing the gaze data and gaze time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input gaze data to a generation AI and cause the generation AI to identify the user's interests.

[0066] The display unit can select information based on the user's interests and determine display priorities. The display unit, for example, filters information based on the user's interests and determines display priorities. For example, the display unit selects information based on the user's past behavioral history or current context. The display unit can also determine display priorities based on the importance of information or the user's level of interest. For example, the display unit preferentially displays information and advertisements related to brands in which the user is interested. The display unit can also filter information based on the user's interests and determine display priorities. This allows the optimal information to be provided by filtering information based on the user's interests and determining display priorities. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that selects information based on the user's interests and determines display priorities.

[0067] The display unit can display information or advertisements according to the user's interests. The display unit, for example, displays information and advertisements according to the user's interests. For example, the display unit displays information and advertisements related to brands in which the user is interested. The display unit can also filter information based on the user's interests and determine display priorities. For example, the display unit selects information based on the user's past behavioral history and current context and determines display priorities. This allows information and advertisements according to the user's interests to be displayed, thereby providing useful information to the user. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that selects information based on the user's interests and determines display priorities.

[0068] The data collection unit can anonymize or encrypt data to protect the user's privacy. The data collection unit, for example, anonymizes data to protect the user's privacy. For example, the data collection unit anonymizes the user's personal information when collecting gaze data. The data collection unit can also encrypt data. For example, the data collection unit encrypts the data end-to-end when collecting gaze data. In this way, the anonymization or encryption of data can protect the user's privacy. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can protect the user's privacy when collecting gaze data by using an AI model that anonymizes or encrypts the data.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of collecting gaze data based on the estimated user emotions. The data collection unit, for example, estimates the user's emotions and adjusts the timing of collecting gaze data based on the estimated user emotions. For example, when the user is excited, the data collection unit increases the frequency of collecting gaze data and collects detailed data. Furthermore, when the user is relaxed, the data collection unit can reduce the frequency of collecting gaze data and collect the minimum amount of data necessary. Furthermore, when the user is stressed, the data collection unit can temporarily stop collecting gaze data to reduce the burden on the user. This enables more appropriate data collection by adjusting the timing of collecting gaze data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data collection unit can input user emotion data into the generation AI and cause the generation AI to adjust the timing of collecting gaze data.

[0070] The data collection unit can analyze the user's past gaze data and select an appropriate collection method. The data collection unit, for example, analyzes the user's past gaze data and selects the optimal collection method. For example, the data collection unit sets gaze data collection points based on information the user frequently viewed in the past. The data collection unit can also concentrate collection during specific time periods based on the user's past gaze data. Furthermore, the data collection unit can analyze the user's gaze data patterns and suggest efficient collection methods. In this way, the optimal collection method can be selected by analyzing the past gaze data. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input past gaze data into a generation AI and have the generation AI select the optimal collection method.

[0071] The data collection unit can perform filtering based on the user's current activity or environment when collecting gaze data. For example, the data collection unit can perform filtering based on the user's current activity or environment when collecting gaze data. For example, when the user is walking, the data collection unit can temporarily stop collecting gaze data to prioritize safety. The data collection unit can also strengthen collection of gaze data and obtain more detailed data when the user is stationary. Furthermore, when the user is in a specific environment (e.g., indoors or outdoors), the data collection unit can perform filtering according to the environment. This enables more appropriate data collection by filtering based on the user's activity and environment. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's current activity and environmental data into the generation AI and have the generation AI perform filtering.

[0072] When collecting gaze data, the data collection unit can select an appropriate collection means depending on the user's input method. For example, when collecting gaze data, the data collection unit selects the optimal collection means depending on the user's input method (voice, text, gesture, etc.). For example, when the user uses voice input, the data collection unit simultaneously collects voice data and gaze data. Also, when the user uses text input, the data collection unit can collect text data and gaze data in conjunction with each other. Furthermore, when the user uses gesture input, the data collection unit can collect gesture data and gaze data in an integrated manner. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input user input method data to a generation AI and cause the generation AI to select the optimal collection means.

[0073] The data collection unit can estimate the user's emotions and determine the priority of gaze data to be collected based on the estimated user emotions. For example, the data collection unit can estimate the user's emotions and determine the priority of gaze data to be collected based on the estimated user emotions. For example, when the user is excited, the data collection unit prioritizes collection of gaze data and acquires detailed data. Furthermore, when the user is relaxed, the data collection unit can set the priority of gaze data collection to low and collect the minimum amount of data necessary. Furthermore, when the user is stressed, the data collection unit can temporarily stop collection of gaze data to reduce the burden on the user. Thus, by determining the priority of gaze data based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of gaze data.

[0074] When collecting gaze data, the data collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when collecting gaze data, the data collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration. For example, when collecting gaze data, the data collection unit prioritizes collecting gaze data related to a specific tourist spot. Also, when the user is in a shopping mall, the data collection unit can prioritize collecting gaze data related to stores and products. Furthermore, when the user is using public transportation, the data collection unit can prioritize collecting gaze data related to traffic information. In this way, highly relevant data can be collected preferentially by taking the geographical location information into consideration. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0075] The data collection unit can collect related data based on the user's social media activity when collecting gaze data. For example, the data collection unit can analyze the user's social media activity and collect related data when collecting gaze data. For example, the data collection unit can collect gaze data related to locations where the user has checked in on social media. The data collection unit can also analyze the content of the user's social media posts and collect related gaze data. Furthermore, the data collection unit can collect related gaze data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related gaze data.

[0076] The data collection unit can customize the collection method based on the user's past feedback when collecting gaze data. For example, the data collection unit customizes the collection method by reflecting the user's past feedback when collecting gaze data. For example, the data collection unit adjusts the gaze data collection method based on feedback provided by the user in the past. The data collection unit can also set specific data collection points based on the user's past feedback. Furthermore, the data collection unit can customize the frequency and timing of gaze data collection by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the gaze data. The analysis unit adjusts the level of detail of the analysis based on, for example, the priority of the gaze data. For example, the analysis unit performs a detailed analysis on gaze data with high importance. The analysis unit can also perform a simplified analysis on gaze data with low importance. Furthermore, the analysis unit can set the priority of the analysis according to the importance of the gaze data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the priority of the gaze data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the type of gaze data. The analysis unit applies different analysis algorithms depending on, for example, the type of gaze data. For example, the analysis unit applies an algorithm that analyzes advertising effectiveness to gaze data related to advertisements. The analysis unit can also apply an algorithm that analyzes guidance effectiveness to gaze data related to signboards. Furthermore, the analysis unit can apply an algorithm that analyzes purchasing intent to gaze data related to products. In this way, applying different analysis algorithms depending on the type of gaze data enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the type of gaze data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, 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 concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time when the gaze data was acquired. The analysis unit determines the priority of analysis based on, for example, the time when the gaze data was acquired. For example, the analysis unit prioritizes the analysis of the most recent gaze data. The analysis unit can also prioritize current gaze data while referring to past gaze data. Furthermore, the analysis unit can set the priority of analysis according to the time when the gaze data was acquired. This enables efficient analysis by determining the priority of analysis based on the time when the gaze data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the gaze data was acquired into the generation AI and have the generation AI determine the priority of analysis.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the gaze data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the gaze data. For example, the analysis unit prioritizes analysis of highly relevant gaze data. The analysis unit can also postpone analysis of less relevant gaze data. Furthermore, the analysis unit can set the order of analysis according to the relevance of the gaze data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the gaze data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the gaze data to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's knowledge level. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's knowledge level. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have specialized knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's knowledge level. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's knowledge level. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's knowledge level data into a generation AI and have the generation AI use technical terms in the analysis.

[0085] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display method that focuses on the main points. This enables more appropriate information display by adjusting the display method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0086] The display unit can adjust the level of detail of the display based on the priority of the information when displaying the information. The display unit adjusts the level of detail of the display based on, for example, the priority of the information. For example, the display unit displays information of high importance in detail. The display unit can also display information of low importance in a simplified manner. Furthermore, the display unit can set a display priority according to the priority of the information. This enables efficient information display by adjusting the level of detail of the display based on the priority of the information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information priority data to a generation AI and cause the generation AI to adjust the level of detail of the display.

[0087] The display unit can apply different display algorithms depending on the type of information when displaying the information. The display unit applies different display algorithms depending on the type of information, for example. For example, the display unit applies a display algorithm that enhances advertising effectiveness to advertising information. The display unit can also apply a display algorithm that enhances guidance effectiveness to guidance information. Furthermore, the display unit can apply a display algorithm that enhances purchasing motivation to product information. In this way, by applying different display algorithms depending on the type of information, more effective information display is possible. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information type data to a generation AI and cause the generation AI to apply different display algorithms.

[0088] The display unit can improve the accuracy of the display based on the user's past display results when displaying. The display unit, for example, improves the accuracy of the display by referring to the user's past display results. For example, the display unit corrects the current display result based on the user's past display results. The display unit can also extract a specific pattern from the user's past display results and reflect it in the current display. Furthermore, the display unit can optimize the display algorithm by referring to the user's past display results. In this way, the accuracy of the display can be improved by referring to the past display results. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past display result data into a generation AI and cause the generation AI to improve the accuracy of the display.

[0089] The display unit can estimate the user's emotion and adjust the display length based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the display length based on the estimated user's emotion. For example, if the user is in a hurry, the display unit can provide a short, to-the-point display. Furthermore, if the user is relaxed, the display unit can provide a longer display including detailed information. Furthermore, if the user is excited, the display unit can provide a visually stimulating display. This enables more appropriate information display by adjusting the display length based on the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or without an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display length.

[0090] The display unit can determine the display priority based on the time when the information was acquired when it was displayed. The display unit determines the display priority based on, for example, the time when the information was acquired. For example, the display unit prioritizes displaying the latest information. The display unit can also prioritize current information while referring to past information. Furthermore, the display unit can set the display priority according to the time when the information was acquired. This enables efficient information display by determining the display priority based on the time when the information was acquired. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information acquisition time data to the generation AI and have the generation AI determine the display priority.

[0091] The display unit can adjust the display order based on the relevance of the information when displaying the information. The display unit adjusts the display order based on, for example, the relevance of the information. For example, the display unit prioritizes display of highly relevant information. The display unit can also postpone display of less relevant information. Furthermore, the display unit can set the display order according to the relevance of the information. This enables efficient information display by adjusting the display order based on the relevance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input relevance data of the information to a generation AI and cause the generation AI to adjust the display order.

[0092] The display unit can adjust the use of technical terms in the display according to the user's knowledge level when displaying information. The display unit can adjust the use of technical terms in the display according to the user's knowledge level, for example. For example, if the user has specialized knowledge, the display unit can provide a display that makes extensive use of technical terms. Furthermore, if the user does not have specialized knowledge, the display unit can also provide a display in simple language. Furthermore, the display unit can adjust the way the display is expressed according to the user's knowledge level. This allows for more easily understandable information display by adjusting the use of technical terms in the display according to the user's knowledge level. Some or all of the above-described processing in the display unit can be performed, for example, using AI or without AI. For example, the display unit can input the user's knowledge level data into a generation AI and have the generation AI execute the use of technical terms in the display. === Hard Collateral 1-1 === Each of the multiple elements including the data collection unit, analysis unit, and display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit can collect user gaze data using the camera 42 or a sensor of the smart device 14. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12, and AI analyzes the collected gaze data to identify the user's interests and concerns. For example, the display unit can display information optimized based on the analysis results in real time using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the data collection unit, analysis unit, and display unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit can collect user gaze data using the camera 42 or a sensor of the smart glasses 214. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12, and AI analyzes the collected gaze data to identify the user's interests and concerns. For example, the display unit can display information optimized based on the analysis results in real time using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, and display unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the data collection unit can collect user gaze data using the camera 42 or a sensor of the headset-type terminal 314. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12, and AI analyzes the collected gaze data to identify the user's interests and concerns. For example, the display unit can display information optimized based on the analysis results in real time using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit can collect user gaze data using the camera 42 or sensor of the robot 414. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12, and AI analyzes the collected gaze data to identify the user's interests and concerns. For example, the display unit can display information optimized based on the analysis results in real time using the display and speaker 240 of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The data collection unit can collect voice data and gesture data in addition to gaze data of users. For example, by collecting what a user is saying while watching a specific advertisement and analyzing that content, it is possible to identify the correlation between gaze data and voice data. Also, if a user makes a gesture of pointing in a specific direction, the gesture data can be collected and analyzed in combination with gaze data. Furthermore, the data collection unit can collect data such as the user's walking pattern and movement speed, and analyze the data in combination with gaze data. This allows for the collection of a variety of data, including not only gaze data but also voice, gesture, and movement data, enabling more accurate analysis.

[0095] The analysis unit can estimate the user's stress level based on the user's gaze data. For example, if the user's gaze moves frequently, it can be determined that the user is likely feeling stressed. Also, if the gaze stays for a short time, it can be estimated that the user is lacking concentration. Furthermore, the analysis unit can combine and analyze the user's gaze data and heart rate data to more accurately estimate the stress level. This makes it possible to grasp the user's stress level in real time and provide appropriate information and adjust the interface.

[0096] The display unit can dynamically adjust the display position of information based on the user's gaze data. For example, if the user looks at a particular area for a long time, information related to that area can be displayed. Also, if the user moves their gaze, the displayed information can move accordingly. Furthermore, the display unit can dynamically change the display size and color of information based on the user's gaze data. This allows the display of information to be optimized to match the user's gaze, providing a more intuitive and easy-to-use interface.

[0097] The data collection unit can collect environmental data in addition to the user's gaze data. For example, it can collect environmental data such as ambient volume, light intensity, and temperature, and analyze it in combination with the gaze data. The data collection unit can also collect the user's location information and analyze it in combination with the gaze data. Furthermore, the data collection unit can collect the user's device usage history and application operation logs, and analyze them in combination with the gaze data. This allows for the collection of a variety of data, including not only gaze data but also environmental data, location information, and device usage history, enabling more accurate analysis.

[0098] The analysis unit can identify a user's learning style based on the user's gaze data. For example, if the gaze stays on a screen for a long time, it can be determined that the user prefers visual information. Also, if the gaze movement pattern is consistent, it can be estimated that the user tends to process information in an orderly manner. Furthermore, the analysis unit can combine and analyze the user's gaze data and learning outcome data to more accurately identify the user's learning style. This makes it possible to provide information and optimize educational content tailored to the user's learning style.

[0099] The display unit can customize the information display format based on the user's gaze data. For example, if the user prefers visual information, a display format that makes extensive use of graphs and charts can be provided. If the user prefers text information, detailed explanatory text can be displayed. Furthermore, the display unit can adjust the information display speed based on the user's gaze data. This allows information to be displayed in accordance with the user's preferences, resulting in more effective information provision.

[0100] The data collection unit can monitor the user's health condition based on the user's gaze data. For example, if the user's gaze movements slow down, it can be determined that the user may be fatigued. Also, if the gaze movements are irregular, it can be estimated that the user is feeling stressed. Furthermore, the data collection unit can combine and analyze the user's gaze data and heart rate data to more accurately monitor the user's health condition. This makes it possible to grasp the user's health condition in real time and provide appropriate alerts and advice.

[0101] The analysis unit can estimate a user's purchasing intent based on the user's gaze data. For example, if the user's gaze stays on a particular product for a long time, it can be determined that the user is interested in that product. Also, if the frequency of gazes is high, it can be estimated that the user has a strong desire to purchase that product. Furthermore, the analysis unit can combine and analyze the user's gaze data and past purchase history to more accurately estimate purchasing intent. This makes it possible to provide appropriate product information and promotions based on the user's purchasing intent.

[0102] The display unit can dynamically change the display order of information based on the user's gaze data. For example, if the user keeps their gaze fixed on a particular piece of information, the display unit can prioritize the display of that information. Also, if the user frequently moves their gaze, the display order of the information can be changed to display information in accordance with the movement of the user's gaze. Furthermore, the display unit can adjust the timing of displaying information based on the user's gaze data. This makes it possible to display information in accordance with the user's gaze, thereby providing more effective information.

[0103] The data collection unit can detect changes in the user's interests in real time based on the user's gaze data. For example, if there is a sudden change in the amount of time the user's gaze stays on a screen, it can be determined that the user's interests have changed. Also, if the gaze movement pattern changes, it can be inferred that the user's interests have changed. Furthermore, the data collection unit can compare and analyze the user's gaze data with past gaze data to more accurately detect changes in interests. This makes it possible to provide appropriate information and adjust the interface in response to changes in the user's interests.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The data collection unit collects the user's gaze data. The gaze data includes the gaze position, gaze movement patterns, and gaze duration. The data collection unit collects gaze data using cameras and sensors installed in the AR / MR glasses, and can also collect it in real time. For example, the data collection unit uses a camera to detect the user's gaze position and record the gaze movement patterns, and uses a sensor to measure the user's gaze duration. Step 2: The analysis unit uses AI to analyze the gaze data collected by the data collection unit and identify the user's interests. Interests are identified based on the duration of gaze and the frequency of gaze. The analysis unit inputs gaze data and performs analysis using an AI model that outputs the user's interests. It also analyzes patterns in the gaze data and improves the accuracy of the analysis by referring to the user's past gaze data. Step 3: The display unit displays information optimized for the user based on the interests and concerns identified by the analysis unit. Suitable information includes information based on the user's past behavioral history and current context. For example, it displays information and advertisements related to brands that the user is interested in, and filters the information to determine the display priority. Furthermore, it estimates the user's emotions and adjusts the display method based on the estimated emotions.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 data collection unit that collects user gaze data; an analysis unit that analyzes the gaze data collected by the data collection unit and identifies the interests and concerns of the user; a display unit that displays information suitable for the user based on the interests and concerns identified by the analysis unit. A system characterized by:

2. The data collection unit Gathering gaze data using cameras or sensors mounted on AR / MR glasses 2. The system of claim 1.

3. The analysis unit Analyzing user gaze data or gaze duration to identify user interests and concerns 2. The system of claim 1.

4. The display unit Filter information based on user interests and prioritize display 2. The system of claim 1.

5. The display unit Displaying information or advertisements that are relevant to your interests 2. The system of claim 1.

6. The data collection unit Anonymize or encrypt data to protect user privacy 2. The system of claim 1.

7. The data collection unit The system estimates the user's emotions and adjusts the timing of gaze data collection based on the estimated user emotions.

2. The system of claim 1.

8. The data collection unit Analyze users' past gaze data and select the appropriate collection method 2. The system of claim 1.

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