System

Abstract

An object of the system according to the embodiment is to provide personalized information based on a line of sight or position information.SOLUTION: A system includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires a line of sight or position information. The analysis unit analyzes the information acquired by the acquisition unit. The providing unit provides information based on the analysis result obtained 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 do not adequately provide personalized information based on gaze and location information, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized information based on gaze and position information. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires gaze or position information. The analysis unit analyzes the information acquired by the acquisition unit. The provision unit provides information based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized information based on gaze and position information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention incorporates a generative AI in smart glasses, providing a personalized, personalized world where each user's field of view differs even when viewed from the same location. This system acquires gaze and location information, and the generative AI then provides optimal information based on that information. For example, the lifestyle assistant function displays product information and price comparisons in real time while shopping, and provides tourist destination guidance and historical information. It also translates foreign language signs and menus in real time and provides audio information about the surrounding environment to visually impaired users. The business knowledge assistant function displays necessary information during surgery in real time in medical settings, analyzes patient data during diagnosis, and recommends optimal treatments. In the workplace, it navigates the progress of processes and displays recipes to support cooking procedures. It also displays evaluation criteria for items during appraisals, enabling accurate appraisals. This allows the generative AI in smart glasses to analyze the user's gaze and location information and provide personalized information to support daily life and work. For example, if a user is looking at a specific product, it displays detailed information and reviews of that product. At tourist destinations, it also provides information about nearby tourist attractions and restaurants based on the user's location. Furthermore, by providing audio information about the surrounding situation to visually impaired people, it supports them in moving around safely, making life more convenient and comfortable for users and improving work efficiency.

[0029] A smart glasses system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires gaze or location information. The gaze information can be acquired using, for example, gaze tracking technology. The location information can be acquired using, for example, GPS technology. The acquisition unit tracks the user's gaze movement in real time using, for example, an gaze tracking sensor. The acquisition unit can also acquire the user's location information using a GPS module. The acquisition unit can also detect the gaze direction using a camera to acquire the gaze information. The analysis unit analyzes the information acquired by the acquisition unit. The analysis is performed using, for example, a data analysis algorithm. For example, the analysis unit analyzes the gaze information to identify an object the user is focusing on. The analysis unit can also analyze the location information to identify the user's current location. Furthermore, the analysis unit can combine and analyze the gaze information and the location information to identify the user's interests. The provision unit provides information based on the analysis results obtained by the analysis unit. The provided information is, for example, text information or audio information. For example, the provision unit displays product information or price comparisons in real time based on the analysis results. The providing unit can also provide guidance and historical information at tourist spots. Furthermore, the providing unit can translate foreign language signs and menus in real time and provide audio information about the surrounding situation to visually impaired people. This allows the smart glasses system according to the embodiment to provide personalized information by acquiring and analyzing gaze and position information.

[0030] The providing unit can display product information or price comparisons in real time while shopping. The providing unit, for example, displays product information in real time while shopping. For example, the providing unit can display detailed information and reviews of a product that the user is looking at. The providing unit can also display price comparisons in real time. For example, the providing unit can compare and display prices of the same product at different stores. Furthermore, the providing unit can display product inventory status and discount information. For example, the providing unit can display an alert when a product is low in stock and notify the user when discount information is available. In this way, by displaying product information and price comparisons in real time while shopping, it is possible to support the user's purchasing behavior.

[0031] The providing unit can provide guidance or historical information at tourist spots. The providing unit, for example, provides guidance at tourist spots. For example, the providing unit can provide information on surrounding tourist spots and restaurants based on the user's location information. The providing unit can also provide historical information. For example, the providing unit can display the historical background or important events of the tourist spot. Furthermore, the providing unit can also provide map information of the tourist spot. For example, the providing unit can display the user's current location and provide route guidance to surrounding tourist spots. This can improve the user's sightseeing experience by providing guidance and historical information at tourist spots.

[0032] The providing unit can translate signs or menus in a foreign language in real time. The providing unit, for example, translates signs in a foreign language in real time. For example, the providing unit can analyze the characters on a sign photographed by a camera and display the translation result. The providing unit can also translate menus in a foreign language in real time. For example, the providing unit can analyze the characters on a menu and display the translation result. Furthermore, the providing unit can also provide the translation result by voice. For example, the providing unit can read the translation result by voice, thereby accommodating visually impaired people. This can improve user convenience by translating signs and menus in a foreign language in real time.

[0033] The providing unit can communicate the surrounding situation to the visually impaired person by voice. The providing unit, for example, communicates the surrounding situation to the visually impaired person by voice. For example, the providing unit can analyze the surrounding situation captured by a camera and communicate the situation by voice. The providing unit can also communicate the location of an obstacle by voice. For example, the providing unit can detect the location of an obstacle and issue a warning by voice. Furthermore, the providing unit can also communicate the movements of people in the vicinity by voice. For example, the providing unit can detect the movements of people in the vicinity and provide guidance by voice. This makes it possible to support the visually impaired person in moving around safely by communicating the surrounding situation by voice.

[0034] The providing unit can display information required during surgery in real time at the medical site. The providing unit, for example, displays information required during surgery in real time at the medical site. For example, the providing unit can display a patient's vital signs in real time. The providing unit can also display surgical procedures in real time. For example, the providing unit can display the next step according to the progress of the surgery. Furthermore, the providing unit can also display information about medical equipment required during surgery in real time. For example, the providing unit can display how to use the medical equipment and precautions to take. This makes it possible to support medical professionals by displaying information required during surgery in real time at the medical site.

[0035] The providing unit can analyze patient data during diagnosis and propose an appropriate treatment. The providing unit, for example, analyzes patient data during diagnosis and proposes an appropriate treatment. For example, the providing unit can analyze the patient's medical history and test results and propose an optimal treatment. The providing unit can also propose a treatment protocol based on the diagnosis results. For example, the providing unit can display recommended treatment procedures based on the diagnosis results. Furthermore, the providing unit can present treatment options and explain the benefits and risks of each. For example, the providing unit can compare multiple treatments and provide information for selecting the optimal treatment. This makes it possible to improve the quality of medical care by analyzing patient data during diagnosis and proposing the optimal treatment.

[0036] The providing unit can navigate the progress of the work process. The providing unit, for example, navigates the progress of the work process. For example, the providing unit can display the work procedure in real time and navigate the progress. The providing unit can also display the progress of the work. For example, the providing unit can display the progress of the work and the remaining work. Furthermore, the providing unit can display precautions and safety instructions during the work. For example, the providing unit can display risks and precautions during the work and provide information for safely proceeding with the work. In this way, by navigating the progress of the work process, it is possible to improve the efficiency of the work.

[0037] The providing unit can display a recipe while cooking to support the steps. The providing unit, for example, displays a recipe while cooking to support the steps. For example, the providing unit can display a list of ingredients for the dish and cooking steps. The providing unit can also display the next step according to the progress of the cooking. For example, the providing unit can display the next step to be taken according to the progress of the cooking. Furthermore, the providing unit can also display points to note and tips while cooking. For example, the providing unit can display points and tips while cooking and provide information to improve the quality of the cooking. In this way, by displaying a recipe while cooking to support the steps, cooking efficiency can be improved.

[0038] The providing unit displays the evaluation criteria for the object in the appraisal work, enabling accurate appraisals. The providing unit, for example, displays the evaluation criteria for the object in the appraisal work, enabling accurate appraisals. For example, the providing unit can display the evaluation criteria and evaluation items for the object to be appraised. The providing unit can also display the next evaluation item according to the progress of the appraisal. For example, the providing unit can display the next item to be appraised according to the progress of the appraisal. Furthermore, the providing unit can display the appraisal results in real time. For example, the providing unit can display the results of the appraisal in real time and explain the basis for the appraisal. This allows the appraisal criteria for the object to be appraised in the appraisal work, enabling accurate appraisals, thereby improving the accuracy of the appraisal.

[0039] The acquisition unit can analyze the user's past history of gaze and location information and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past history of gaze and location information and selects the optimal acquisition method. For example, the acquisition unit sets acquisition points for gaze and location information based on places the user has frequently visited in the past. The acquisition unit can also analyze the user's past gaze movements and optimize the timing of gaze acquisition. Furthermore, the acquisition unit can prioritize data acquisition in specific areas based on the user's past history of location information. In this way, the optimal acquisition method can be selected by analyzing the user's past history of gaze and location information.

[0040] The acquisition unit can perform filtering based on the user's current activities and areas of interest when acquiring gaze and location information. For example, the acquisition unit performs filtering based on the user's current activities and areas of interest when acquiring gaze and location information. For example, when the user is shopping, the acquisition unit can focus the acquisition of gaze and location information on product shelves and price displays. Furthermore, when the user is sightseeing, the acquisition unit can also focus the acquisition of gaze and location information on tourist spots and information boards. Furthermore, when the user is cooking, the acquisition unit can focus the acquisition of gaze and location information on recipes and cooking utensils. In this way, highly relevant data can be acquired by filtering based on the user's current activities and areas of interest.

[0041] When acquiring gaze and position information, the acquisition unit can select an appropriate acquisition means according to the user's input method. For example, when acquiring gaze and position information, the acquisition unit selects the optimal acquisition means according to the user's input method (voice, text, gesture, etc.). For example, when the user is using voice input, the acquisition unit acquires gaze and position information based on a voice command. Furthermore, when the user is using text input, the acquisition unit can also acquire gaze and position information based on the input content. Furthermore, when the user is using gesture input, the acquisition unit can also acquire gaze and position information based on the gesture movement. This enables efficient data acquisition by selecting the optimal acquisition means according to the user's input method.

[0042] When acquiring gaze and position information, the acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical position information. For example, when acquiring gaze and position information, the acquisition unit prioritizes acquiring highly relevant information in consideration of the user's geographical position information. For example, when the user is in a specific tourist spot, the acquisition unit prioritizes acquiring gaze and position information related to the tourist spot. Furthermore, when the user is in a shopping mall, the acquisition unit can also prioritize acquiring gaze and position information related to stores and products. Furthermore, when the user is in a medical facility, the acquisition unit can also prioritize acquiring gaze and position information related to medical equipment and examination rooms. This enables efficient data collection by preferentially acquiring highly relevant information in consideration of the user's geographical position information.

[0043] The acquisition unit can analyze the user's social media activity when acquiring gaze and location information and acquire related information. For example, the acquisition unit analyzes the user's social media activity when acquiring gaze and location information and acquires related information. For example, the acquisition unit acquires gaze and location information related to a location where the user checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media and acquire related gaze and location information. Furthermore, the acquisition unit can acquire related gaze and location information by referring to the activities of the user's friends on social media. This makes it possible to efficiently acquire related information by analyzing the user's social media activity.

[0044] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring gaze and position information. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring gaze and position information. For example, the acquisition unit adjusts the acquisition method for gaze and position information based on feedback provided by the user in the past. The acquisition unit can also preferentially use a specific acquisition method based on the user's past feedback. Furthermore, the acquisition unit can reflect the user's feedback and optimize the timing of acquiring gaze and position information. In this way, the acquisition method can be customized by reflecting the user's past feedback, enabling efficient data collection.

[0045] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of gaze and position information. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of gaze and position information. For example, the analysis unit performs a detailed analysis when the importance of gaze and position information is high. The analysis unit can also perform a concise analysis when the importance of gaze and position information is low. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis depending on the importance of gaze and position information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of gaze and position information.

[0046] During analysis, the analysis unit can apply different analysis algorithms depending on the category of gaze and location information. The analysis unit applies different analysis algorithms depending on, for example, the category of gaze and location information. For example, if the gaze and location information is related to shopping, the analysis unit applies a product information analysis algorithm. Furthermore, if the gaze and location information is related to tourism, the analysis unit can also apply a tourist information analysis algorithm. Furthermore, if the gaze and location information is related to medicine, the analysis unit can also apply a medical information analysis algorithm. In this way, by applying different analysis algorithms depending on the category of gaze and location information, it is possible to provide appropriate analysis results.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by referring to 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 improves the accuracy of the current analysis 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 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 user's past analysis results.

[0048] During analysis, the analysis unit can determine the priority of analysis based on the time when gaze and position information was acquired. The analysis unit determines the priority of analysis based on, for example, the time when gaze and position information was acquired. For example, the analysis unit prioritizes analysis if the gaze and position information was acquired recently. The analysis unit can also lower the priority of analysis if the gaze and position information was acquired a long time ago. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the time when gaze and position information was acquired. This enables efficient analysis by determining the priority of analysis based on the time when gaze and position information was acquired.

[0049] During analysis, the analysis unit can adjust the order of analysis based on the relevance of gaze and position information. The analysis unit adjusts the order of analysis based on, for example, the relevance of gaze and position information. For example, the analysis unit prioritizes analysis when the relevance of gaze and position information is high. The analysis unit can also postpone the order of analysis when the relevance of gaze and position information is low. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of gaze and position information. This enables efficient analysis by adjusting the order of analysis based on the relevance of gaze and position information.

[0050] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that make extensive use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, appropriate analysis results can be provided.

[0051] The providing unit can adjust the level of detail of the information to be provided based on the user's current activity and interests when providing information. For example, the providing unit adjusts the level of detail of the information to be provided based on the user's current activity and interests when providing information. For example, when the user is shopping, the providing unit can provide detailed information about products. Furthermore, when the user is sightseeing, the providing unit can also provide detailed information about tourist spots. Furthermore, when the user is cooking, the providing unit can provide detailed information about recipes. In this way, by adjusting the level of detail of the information to be provided based on the user's current activity and interests, it is possible to provide appropriate information.

[0052] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision history. The providing unit improves the accuracy of the information provided by referring to, for example, the user's past information provision history. For example, the providing unit improves the accuracy of the current information provided based on the user's past information provision history. The providing unit can also extract specific patterns from the user's past information provision history and reflect them in the information provided. Furthermore, the providing unit can also optimize the information providing algorithm by referring to the user's past information provision history. In this way, the accuracy of the information provided can be improved by referring to the user's past information provision history.

[0053] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, the providing unit improves the information providing method by reflecting user feedback when providing information. For example, when a user provides feedback on the provided information, the providing unit improves the information providing method based on the feedback. The providing unit can also analyze the user feedback and optimize the content and format of the information to be provided. Furthermore, the providing unit can reflect the user feedback and adjust the timing and frequency of information provision. In this way, the providing method can be improved and appropriate information can be provided by reflecting the user feedback.

[0054] The providing unit can provide appropriate information by taking into consideration the geographical location information of the user when providing information. For example, the providing unit provides appropriate information by taking into consideration the geographical location information of the user when providing information. For example, when the user is in a specific tourist spot, the providing unit can provide information related to the tourist spot preferentially. Furthermore, when the user is in a shopping mall, the providing unit can also provide information related to stores and products preferentially. Furthermore, when the user is in a medical facility, the providing unit can also provide information related to medical equipment and examination rooms preferentially. This enables efficient information provision by providing optimal information by taking into consideration the geographical location information of the user.

[0055] The providing unit can provide related information by analyzing the user's social media activity when providing information. For example, the providing unit can provide related information by analyzing the user's social media activity when providing information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be efficiently provided by analyzing the user's social media activity.

[0056] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit customizes the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit adjusts the information providing method based on the user's past feedback. The providing unit can also preferentially use a specific information providing method based on the user's past feedback. Furthermore, the providing unit can also reflect the user's feedback and optimize the timing and format of information provision. In this way, the providing method can be customized by reflecting the user's past feedback, enabling efficient information provision.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The acquisition unit acquires the user's biometric information, and the analysis unit analyzes the biometric information to monitor the user's health condition. For example, the acquisition unit acquires biometric information such as heart rate, blood pressure, and body temperature using a sensor. The analysis unit analyzes the acquired biometric information and can send an alert to the provision unit if an abnormality is detected. Furthermore, the provision unit can provide health management advice or information recommending a visit to a medical institution based on the analysis results. This makes it possible to monitor the user's health condition in real time and support early health management.

[0059] The analysis unit can learn the user's past behavioral patterns and predict future behavior. For example, the analysis unit can predict the place the user is likely to visit next based on the places the user has visited and the activities he or she has performed in the past. The analysis unit can also predict the product the user is likely to purchase next based on the user's past purchase history. Furthermore, the analysis unit can predict the next planned activity based on the user's past schedule. This makes it possible to provide more personalized information by predicting the user's future behavior.

[0060] The acquisition unit acquires environmental sounds around the user, and the analysis unit analyzes the environmental sounds to provide a voice assistant suited to the user's activities. For example, the acquisition unit acquires the ambient noise level using a sensor. The analysis unit can analyze the acquired environmental sounds and set the volume of the voice assistant low in quiet environments and high in noisy environments. Furthermore, the provision unit can provide a voice assistant function suited to the user's activities based on the analysis results. This makes it possible to provide a voice assistant suited to the user's surrounding environment.

[0061] The analysis unit can analyze a user's past search history and provide relevant information preferentially. For example, the analysis unit can display relevant information preferentially based on keywords the user has searched for in the past. The analysis unit can also extract topics of interest from the user's past search history and provide related news and articles. Furthermore, the analysis unit can predict and suggest keywords that the user is likely to search for next based on the user's past search history. This makes it possible to provide highly relevant information by utilizing the user's past search history.

[0062] The acquisition unit acquires the user's exercise data, and the analysis unit analyzes the exercise data to provide a fitness plan. For example, the acquisition unit acquires exercise data such as the user's number of steps, heart rate, and calories burned using a sensor. The analysis unit analyzes the acquired exercise data and can create an optimal fitness plan based on the user's exercise habits and goals. Furthermore, the provision unit can provide exercise advice and training programs based on the analysis results. This makes it possible to utilize the user's exercise data to provide an effective fitness plan.

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

[0064] Step 1: The acquisition unit acquires gaze or position information. Gaze information is acquired using gaze tracking technology, and position information is acquired using GPS technology. The acquisition unit acquires the user's gaze movement and position information in real time using an gaze tracking sensor, GPS module, and camera. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. It uses a data analysis algorithm to analyze the gaze information and location information to identify the object the user is looking at and the user's current location. It can also analyze the gaze information and location information in combination to identify the user's interests. Step 3: The provider provides information based on the analysis results obtained by the analyzer. The provided information may be text or audio information, such as product information, price comparisons, tourist information, historical information, translations of foreign language signs and menus, and audio guidance for the visually impaired.

[0065] (Example 2) A system according to an embodiment of the present invention incorporates a generative AI in smart glasses, providing a personalized, personalized world where each user's field of view differs even when viewed from the same location. This system acquires gaze and location information, and the generative AI then provides optimal information based on that information. For example, the lifestyle assistant function displays product information and price comparisons in real time while shopping, and provides tourist destination guidance and historical information. It also translates foreign language signs and menus in real time and provides audio information about the surrounding environment to visually impaired users. The business knowledge assistant function displays necessary information during surgery in real time in medical settings, analyzes patient data during diagnosis, and recommends optimal treatments. In the workplace, it navigates the progress of processes and displays recipes to support cooking procedures. It also displays evaluation criteria for items during appraisals, enabling accurate appraisals. This allows the generative AI in smart glasses to analyze the user's gaze and location information and provide personalized information to support daily life and work. For example, if a user is looking at a specific product, it displays detailed information and reviews of that product. At tourist destinations, it also provides information about nearby tourist attractions and restaurants based on the user's location. Furthermore, by providing audio information about the surrounding situation to visually impaired people, it supports them in moving around safely, making life more convenient and comfortable for users and improving work efficiency.

[0066] A smart glasses system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires gaze or location information. The gaze information can be acquired using, for example, gaze tracking technology. The location information can be acquired using, for example, GPS technology. The acquisition unit tracks the user's gaze movement in real time using, for example, an gaze tracking sensor. The acquisition unit can also acquire the user's location information using a GPS module. The acquisition unit can also detect the gaze direction using a camera to acquire the gaze information. The analysis unit analyzes the information acquired by the acquisition unit. The analysis is performed using, for example, a data analysis algorithm. For example, the analysis unit analyzes the gaze information to identify an object the user is focusing on. The analysis unit can also analyze the location information to identify the user's current location. Furthermore, the analysis unit can combine and analyze the gaze information and the location information to identify the user's interests. The provision unit provides information based on the analysis results obtained by the analysis unit. The provided information is, for example, text information or audio information. For example, the provision unit displays product information or price comparisons in real time based on the analysis results. The providing unit can also provide guidance and historical information at tourist spots. Furthermore, the providing unit can translate foreign language signs and menus in real time and provide audio information about the surrounding situation to visually impaired people. This allows the smart glasses system according to the embodiment to provide personalized information by acquiring and analyzing gaze and position information.

[0067] The providing unit can display product information or price comparisons in real time while shopping. The providing unit, for example, displays product information in real time while shopping. For example, the providing unit can display detailed information and reviews of a product that the user is looking at. The providing unit can also display price comparisons in real time. For example, the providing unit can compare and display prices of the same product at different stores. Furthermore, the providing unit can display product inventory status and discount information. For example, the providing unit can display an alert when a product is low in stock and notify the user when discount information is available. In this way, by displaying product information and price comparisons in real time while shopping, it is possible to support the user's purchasing behavior.

[0068] The providing unit can provide guidance or historical information at tourist spots. The providing unit, for example, provides guidance at tourist spots. For example, the providing unit can provide information on surrounding tourist spots and restaurants based on the user's location information. The providing unit can also provide historical information. For example, the providing unit can display the historical background or important events of the tourist spot. Furthermore, the providing unit can also provide map information of the tourist spot. For example, the providing unit can display the user's current location and provide route guidance to surrounding tourist spots. This can improve the user's sightseeing experience by providing guidance and historical information at tourist spots.

[0069] The providing unit can translate signs or menus in a foreign language in real time. The providing unit, for example, translates signs in a foreign language in real time. For example, the providing unit can analyze the characters on a sign photographed by a camera and display the translation result. The providing unit can also translate menus in a foreign language in real time. For example, the providing unit can analyze the characters on a menu and display the translation result. Furthermore, the providing unit can also provide the translation result by voice. For example, the providing unit can read the translation result by voice, thereby accommodating visually impaired people. This can improve user convenience by translating signs and menus in a foreign language in real time.

[0070] The providing unit can communicate the surrounding situation to the visually impaired person by voice. The providing unit, for example, communicates the surrounding situation to the visually impaired person by voice. For example, the providing unit can analyze the surrounding situation captured by a camera and communicate the situation by voice. The providing unit can also communicate the location of an obstacle by voice. For example, the providing unit can detect the location of an obstacle and issue a warning by voice. Furthermore, the providing unit can also communicate the movements of people in the vicinity by voice. For example, the providing unit can detect the movements of people in the vicinity and provide guidance by voice. This makes it possible to support the visually impaired person in moving around safely by communicating the surrounding situation by voice.

[0071] The providing unit can display information required during surgery in real time at the medical site. The providing unit, for example, displays information required during surgery in real time at the medical site. For example, the providing unit can display a patient's vital signs in real time. The providing unit can also display surgical procedures in real time. For example, the providing unit can display the next step according to the progress of the surgery. Furthermore, the providing unit can also display information about medical equipment required during surgery in real time. For example, the providing unit can display how to use the medical equipment and precautions to take. This makes it possible to support medical professionals by displaying information required during surgery in real time at the medical site.

[0072] The providing unit can analyze patient data during diagnosis and propose an appropriate treatment. The providing unit, for example, analyzes patient data during diagnosis and proposes an appropriate treatment. For example, the providing unit can analyze the patient's medical history and test results and propose an optimal treatment. The providing unit can also propose a treatment protocol based on the diagnosis results. For example, the providing unit can display recommended treatment procedures based on the diagnosis results. Furthermore, the providing unit can present treatment options and explain the benefits and risks of each. For example, the providing unit can compare multiple treatments and provide information for selecting the optimal treatment. This makes it possible to improve the quality of medical care by analyzing patient data during diagnosis and proposing the optimal treatment.

[0073] The providing unit can navigate the progress of the work process. The providing unit, for example, navigates the progress of the work process. For example, the providing unit can display the work procedure in real time and navigate the progress. The providing unit can also display the progress of the work. For example, the providing unit can display the progress of the work and the remaining work. Furthermore, the providing unit can display precautions and safety instructions during the work. For example, the providing unit can display risks and precautions during the work and provide information for safely proceeding with the work. In this way, by navigating the progress of the work process, it is possible to improve the efficiency of the work.

[0074] The providing unit can display a recipe while cooking to support the steps. The providing unit, for example, displays a recipe while cooking to support the steps. For example, the providing unit can display a list of ingredients for the dish and cooking steps. The providing unit can also display the next step according to the progress of the cooking. For example, the providing unit can display the next step to be taken according to the progress of the cooking. Furthermore, the providing unit can also display points to note and tips while cooking. For example, the providing unit can display points and tips while cooking and provide information to improve the quality of the cooking. In this way, by displaying a recipe while cooking to support the steps, cooking efficiency can be improved.

[0075] The providing unit displays the evaluation criteria for the object in the appraisal work, enabling accurate appraisals. The providing unit, for example, displays the evaluation criteria for the object in the appraisal work, enabling accurate appraisals. For example, the providing unit can display the evaluation criteria and evaluation items for the object to be appraised. The providing unit can also display the next evaluation item according to the progress of the appraisal. For example, the providing unit can display the next item to be appraised according to the progress of the appraisal. Furthermore, the providing unit can display the appraisal results in real time. For example, the providing unit can display the results of the appraisal in real time and explain the basis for the appraisal. This allows the appraisal criteria for the object to be appraised in the appraisal work, enabling accurate appraisals, thereby improving the accuracy of the appraisal.

[0076] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring gaze and position information based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the timing of acquiring gaze and position information. For example, when the user is excited, the acquisition unit increases the frequency of acquiring gaze and position information to collect detailed data. Furthermore, when the user is relaxed, the acquisition unit can reduce the frequency of acquiring gaze and position information to collect the minimum amount of data necessary. Furthermore, when the user is stressed, the acquisition unit can adjust the timing of acquiring gaze and position information to reduce the burden on the user. In this way, by adjusting the timing of acquiring gaze and position information based on the user's emotions, more appropriate data can be collected. Emotion estimation is realized 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.

[0077] The acquisition unit can analyze the user's past history of gaze and location information and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past history of gaze and location information and selects the optimal acquisition method. For example, the acquisition unit sets acquisition points for gaze and location information based on places the user has frequently visited in the past. The acquisition unit can also analyze the user's past gaze movements and optimize the timing of gaze acquisition. Furthermore, the acquisition unit can prioritize data acquisition in specific areas based on the user's past history of location information. In this way, the optimal acquisition method can be selected by analyzing the user's past history of gaze and location information.

[0078] The acquisition unit can perform filtering based on the user's current activities and areas of interest when acquiring gaze and location information. For example, the acquisition unit performs filtering based on the user's current activities and areas of interest when acquiring gaze and location information. For example, when the user is shopping, the acquisition unit can focus the acquisition of gaze and location information on product shelves and price displays. Furthermore, when the user is sightseeing, the acquisition unit can also focus the acquisition of gaze and location information on tourist spots and information boards. Furthermore, when the user is cooking, the acquisition unit can focus the acquisition of gaze and location information on recipes and cooking utensils. In this way, highly relevant data can be acquired by filtering based on the user's current activities and areas of interest.

[0079] When acquiring gaze and position information, the acquisition unit can select an appropriate acquisition means according to the user's input method. For example, when acquiring gaze and position information, the acquisition unit selects the optimal acquisition means according to the user's input method (voice, text, gesture, etc.). For example, when the user is using voice input, the acquisition unit acquires gaze and position information based on a voice command. Furthermore, when the user is using text input, the acquisition unit can also acquire gaze and position information based on the input content. Furthermore, when the user is using gesture input, the acquisition unit can also acquire gaze and position information based on the gesture movement. This enables efficient data acquisition by selecting the optimal acquisition means according to the user's input method.

[0080] The acquisition unit can estimate the user's emotions and determine the priority of gaze and location information to be acquired based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of gaze and location information. For example, when the user is excited, the acquisition unit prioritizes acquisition of gaze and location information to collect detailed data. Furthermore, when the user is relaxed, the acquisition unit can set acquisition of gaze and location information to a low priority and collect the minimum amount of data necessary. Furthermore, when the user is stressed, the acquisition unit can set acquisition of gaze and location information to a medium priority to reduce the burden on the user. Thus, by determining the priority of gaze and location information based on the user's emotions, important data can be acquired preferentially. Emotion estimation is realized 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.

[0081] When acquiring gaze and position information, the acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical position information. For example, when acquiring gaze and position information, the acquisition unit prioritizes acquiring highly relevant information in consideration of the user's geographical position information. For example, when the user is in a specific tourist spot, the acquisition unit prioritizes acquiring gaze and position information related to the tourist spot. Furthermore, when the user is in a shopping mall, the acquisition unit can also prioritize acquiring gaze and position information related to stores and products. Furthermore, when the user is in a medical facility, the acquisition unit can also prioritize acquiring gaze and position information related to medical equipment and examination rooms. This enables efficient data collection by preferentially acquiring highly relevant information in consideration of the user's geographical position information.

[0082] The acquisition unit can analyze the user's social media activity when acquiring gaze and location information and acquire related information. For example, the acquisition unit analyzes the user's social media activity when acquiring gaze and location information and acquires related information. For example, the acquisition unit acquires gaze and location information related to a location where the user checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media and acquire related gaze and location information. Furthermore, the acquisition unit can acquire related gaze and location information by referring to the activities of the user's friends on social media. This makes it possible to efficiently acquire related information by analyzing the user's social media activity.

[0083] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring gaze and position information. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring gaze and position information. For example, the acquisition unit adjusts the acquisition method for gaze and position information based on feedback provided by the user in the past. The acquisition unit can also preferentially use a specific acquisition method based on the user's past feedback. Furthermore, the acquisition unit can reflect the user's feedback and optimize the timing of acquiring gaze and position information. In this way, the acquisition method can be customized by reflecting the user's past feedback, enabling efficient data collection.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results with visually stimulating effects when the user is excited. In this way, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide analysis results that are suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of gaze and position information. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of gaze and position information. For example, the analysis unit performs a detailed analysis when the importance of gaze and position information is high. The analysis unit can also perform a concise analysis when the importance of gaze and position information is low. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis depending on the importance of gaze and position information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of gaze and position information.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of gaze and location information. The analysis unit applies different analysis algorithms depending on, for example, the category of gaze and location information. For example, if the gaze and location information is related to shopping, the analysis unit applies a product information analysis algorithm. Furthermore, if the gaze and location information is related to tourism, the analysis unit can also apply a tourist information analysis algorithm. Furthermore, if the gaze and location information is related to medicine, the analysis unit can also apply a medical information analysis algorithm. In this way, by applying different analysis algorithms depending on the category of gaze and location information, it is possible to provide appropriate analysis results.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to 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 improves the accuracy of the current analysis 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 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 user's past analysis results.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Alternatively, 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 an analysis result with a visually stimulating effect. In this way, by adjusting the length of the analysis based on the user's emotions, it is possible to provide an analysis result that is suitable for the user. Emotion estimation is realized 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.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the time when gaze and position information was acquired. The analysis unit determines the priority of analysis based on, for example, the time when gaze and position information was acquired. For example, the analysis unit prioritizes analysis if the gaze and position information was acquired recently. The analysis unit can also lower the priority of analysis if the gaze and position information was acquired a long time ago. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the time when gaze and position information was acquired. This enables efficient analysis by determining the priority of analysis based on the time when gaze and position information was acquired.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of gaze and position information. The analysis unit adjusts the order of analysis based on, for example, the relevance of gaze and position information. For example, the analysis unit prioritizes analysis when the relevance of gaze and position information is high. The analysis unit can also postpone the order of analysis when the relevance of gaze and position information is low. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of gaze and position information. This enables efficient analysis by adjusting the order of analysis based on the relevance of gaze and position information.

[0091] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that make extensive use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, appropriate analysis results can be provided.

[0092] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the method of providing information. For example, when the user is relaxed, the providing unit can provide detailed information. When the user is in a hurry, the providing unit can also provide concise information that focuses on the main points. Furthermore, when the user is excited, the providing unit can also provide information with a visually stimulating effect. In this way, by adjusting the method of providing information based on the user's emotions, it is possible to provide information that is appropriate for the user. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The providing unit can adjust the level of detail of the information to be provided based on the user's current activity and interests when providing information. For example, the providing unit adjusts the level of detail of the information to be provided based on the user's current activity and interests when providing information. For example, when the user is shopping, the providing unit can provide detailed information about products. Furthermore, when the user is sightseeing, the providing unit can also provide detailed information about tourist spots. Furthermore, when the user is cooking, the providing unit can provide detailed information about recipes. In this way, by adjusting the level of detail of the information to be provided based on the user's current activity and interests, it is possible to provide appropriate information.

[0094] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision history. The providing unit improves the accuracy of the information provided by referring to, for example, the user's past information provision history. For example, the providing unit improves the accuracy of the current information provided based on the user's past information provision history. The providing unit can also extract specific patterns from the user's past information provision history and reflect them in the information provided. Furthermore, the providing unit can also optimize the information providing algorithm by referring to the user's past information provision history. In this way, the accuracy of the information provided can be improved by referring to the user's past information provision history.

[0095] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, the providing unit improves the information providing method by reflecting user feedback when providing information. For example, when a user provides feedback on the provided information, the providing unit improves the information providing method based on the feedback. The providing unit can also analyze the user feedback and optimize the content and format of the information to be provided. Furthermore, the providing unit can reflect the user feedback and adjust the timing and frequency of information provision. In this way, the providing method can be improved and appropriate information can be provided by reflecting the user feedback.

[0096] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information to be provided. For example, if the user is excited, the providing unit can prioritize providing visually stimulating information. Furthermore, if the user is relaxed, the providing unit can also prioritize providing detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise information that covers the main points. In this way, by determining the priority of information to be provided based on the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The providing unit can provide appropriate information by taking into consideration the geographical location information of the user when providing information. For example, the providing unit provides appropriate information by taking into consideration the geographical location information of the user when providing information. For example, when the user is in a specific tourist spot, the providing unit can provide information related to the tourist spot preferentially. Furthermore, when the user is in a shopping mall, the providing unit can also provide information related to stores and products preferentially. Furthermore, when the user is in a medical facility, the providing unit can also provide information related to medical equipment and examination rooms preferentially. This enables efficient information provision by providing optimal information by taking into consideration the geographical location information of the user.

[0098] The providing unit can provide related information by analyzing the user's social media activity when providing information. For example, the providing unit can provide related information by analyzing the user's social media activity when providing information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be efficiently provided by analyzing the user's social media activity.

[0099] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit customizes the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit adjusts the information providing method based on the user's past feedback. The providing unit can also preferentially use a specific information providing method based on the user's past feedback. Furthermore, the providing unit can also reflect the user's feedback and optimize the timing and format of information provision. In this way, the providing method can be customized by reflecting the user's past feedback, enabling efficient information provision. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, and provision 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 acquisition unit acquires gaze information using the camera 42 or an eye-tracking sensor of the smart device 14, and acquires location information using a GPS module. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired gaze information and location information. The provision unit is realized by the control unit 46A of the smart device 14 and displays product information and tourist information in real time based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, analysis unit, and provision 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 acquisition unit acquires gaze information using the camera 42 or an eye tracking sensor of the smart glasses 214, and acquires location information using a GPS module. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired gaze information and location information. The provision unit is realized by the control unit 46A of the smart glasses 214 and displays product information and tourist information in real time based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires gaze information using the camera 42 or an eye-tracking sensor of the headset terminal 314, and acquires location information using a GPS module. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired gaze information and location information. The provision unit is realized by the control unit 46A of the headset terminal 314, and displays product information and tourist information in real time based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires gaze information using the camera 42 or an eye-tracking sensor of the robot 414, and acquires location information using a GPS module. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired gaze information and location information. The provision unit is realized by the control unit 46A of the robot 414, and displays product information and tourist information in real time based on the analysis results.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The acquisition unit acquires the user's biometric information, and the analysis unit analyzes the biometric information to monitor the user's health condition. For example, the acquisition unit acquires biometric information such as heart rate, blood pressure, and body temperature using a sensor. The analysis unit analyzes the acquired biometric information and can send an alert to the provision unit if an abnormality is detected. Furthermore, the provision unit can provide health management advice or information recommending a visit to a medical institution based on the analysis results. This makes it possible to monitor the user's health condition in real time and support early health management.

[0102] The providing unit can estimate the user's emotions and provide music and images with a relaxing effect based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can play music with a relaxing effect. Also, if the user is tired, the providing unit can display images with a refreshing effect. Furthermore, if the user is excited, the providing unit can provide a meditation guide to calm the user. In this way, by providing content with a relaxing effect according to the user's emotions, it is possible to support the user's mental health.

[0103] The analysis unit can learn the user's past behavioral patterns and predict future behavior. For example, the analysis unit can predict the place the user is likely to visit next based on the places the user has visited and the activities he or she has performed in the past. The analysis unit can also predict the product the user is likely to purchase next based on the user's past purchase history. Furthermore, the analysis unit can predict the next planned activity based on the user's past schedule. This makes it possible to provide more personalized information by predicting the user's future behavior.

[0104] The providing unit can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, if the user is sad, the providing unit can display an encouraging message. If the user is happy, the providing unit can also display a congratulatory message. Furthermore, if the user is angry, the providing unit can also provide advice on how to stay calm. In this way, the user's emotions can be supported by providing appropriate communication according to the user's emotions.

[0105] The acquisition unit acquires environmental sounds around the user, and the analysis unit analyzes the environmental sounds to provide a voice assistant suited to the user's activities. For example, the acquisition unit acquires the ambient noise level using a sensor. The analysis unit can analyze the acquired environmental sounds and set the volume of the voice assistant low in quiet environments and high in noisy environments. Furthermore, the provision unit can provide a voice assistant function suited to the user's activities based on the analysis results. This makes it possible to provide a voice assistant suited to the user's surrounding environment.

[0106] The providing unit can estimate the user's emotions and provide entertainment content based on the estimated emotions. For example, if the user is bored, the providing unit can suggest an interesting movie or game. If the user is sad, the providing unit can suggest a comedy movie to brighten the mood. Furthermore, if the user wants to relax, the providing unit can provide music or videos that have a relaxing effect. In this way, the user's mood can be improved by providing entertainment content that corresponds to the user's emotions.

[0107] The analysis unit can analyze a user's past search history and provide relevant information preferentially. For example, the analysis unit can display relevant information preferentially based on keywords the user has searched for in the past. The analysis unit can also extract topics of interest from the user's past search history and provide related news and articles. Furthermore, the analysis unit can predict and suggest keywords that the user is likely to search for next based on the user's past search history. This makes it possible to provide highly relevant information by utilizing the user's past search history.

[0108] The providing unit can estimate the user's emotions and provide learning content based on the estimated emotions. For example, if the user is concentrating, the providing unit can provide learning content with a high level of difficulty. If the user is tired, the providing unit can also provide content that allows the user to learn while relaxing. Furthermore, if the user is excited, the providing unit can also provide engaging, interactive learning content. This can improve learning effectiveness by providing learning content that corresponds to the user's emotions.

[0109] The acquisition unit acquires the user's exercise data, and the analysis unit analyzes the exercise data to provide a fitness plan. For example, the acquisition unit acquires exercise data such as the user's number of steps, heart rate, and calories burned using a sensor. The analysis unit analyzes the acquired exercise data and can create an optimal fitness plan based on the user's exercise habits and goals. Furthermore, the provision unit can provide exercise advice and training programs based on the analysis results. This makes it possible to utilize the user's exercise data to provide an effective fitness plan.

[0110] The providing unit can estimate the user's emotions and provide personalized news based on the estimated emotions. For example, if the user is excited, the providing unit can provide positive news preferentially. Also, if the user is relaxed, the providing unit can provide detailed news articles. Furthermore, if the user is feeling stressed, the providing unit can provide news and articles that have a relaxing effect. In this way, by providing personalized news according to the user's emotions, it is possible to support the user's information gathering.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The acquisition unit acquires gaze or position information. Gaze information is acquired using gaze tracking technology, and position information is acquired using GPS technology. The acquisition unit acquires the user's gaze movement and position information in real time using an gaze tracking sensor, GPS module, and camera. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. It uses a data analysis algorithm to analyze the gaze information and location information to identify the object the user is looking at and the user's current location. It can also analyze the gaze information and location information in combination to identify the user's interests. Step 3: The provider provides information based on the analysis results obtained by the analyzer. The provided information may be text or audio information, such as product information, price comparisons, tourist information, historical information, translations of foreign language signs and menus, and audio guidance for the visually impaired.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires gaze or position information; an analysis unit that analyzes the information acquired by the acquisition unit; a providing unit that provides information based on the analysis result obtained by the analyzing unit. A system characterized by:

2. The providing unit Display real-time product information or price comparisons while shopping 2. The system of claim 1.

3. The providing unit Providing tourist guides or historical information 2. The system of claim 1.

4. The providing unit Translate foreign language signs or menus in real time 2. The system of claim 1.

5. The providing unit Communicating surrounding situations to the visually impaired through audio 2. The system of claim 1.

6. The providing unit Displaying necessary information in real time during surgery in the medical field 2. The system of claim 1.

7. The providing unit Analyzing patient data during diagnosis and suggesting appropriate treatment 2. The system of claim 1.

8. The providing unit Navigate the progress of your work 2. The system of claim 1.

Images