System

Abstract

An object of a system according to an embodiment is to acquire a life log using smart glasses and to provide highly convenient information to a user.SOLUTION: A system includes a video acquisition unit, a current location specification unit, an information provision unit, a call correspondence unit, and a summary video creation unit. The image acquisition unit acquires a life log as an image using smart glasses. The current location identification unit identifies the current location based on the video acquired by the video acquisition unit. An information providing part provides nearby profitable coupon information and event information on the basis of the present location specified by the present location specifying part. In response to a call from the user, the call handling unit provides route guidance, provides a train timetable, and handles the call as a conversation partner. The summary video creation unit creates today's summary video from the video acquired at the end of the day.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to centrally perform tasks such as acquiring life logs, determining current locations, and providing information, and that user convenience has not been sufficiently improved.

[0005] The system according to the embodiment aims to acquire a life log using smart glasses and provide highly convenient information to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a video acquisition unit, a current location determination unit, an information provision unit, a call response unit, and a summary video creation unit. The video acquisition unit acquires a life log as video using smart glasses. The current location determination unit determines the current location based on the video acquired by the video acquisition unit. The information provision unit provides nearby discount coupon information and event information based on the current location determined by the current location determination unit. The call response unit responds to calls from users by providing route guidance, train timetables, and acting as a conversation partner. The summary video creation unit creates a summary video of the day from the video acquired at the end of the day. [Effects of the Invention]

[0007] The system according to the embodiment can acquire a life log using smart glasses and provide highly convenient information to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A life log acquisition system according to an embodiment of the present invention is a system that acquires a life log as video using smart glasses, analyzes the video using a generation AI, and provides various information. This makes the user's daily life more convenient and enjoyable.

[0029] A life log acquisition system according to an embodiment includes a video acquisition unit, a current location determination unit, an information provision unit, a call response unit, and a summary video creation unit. The video acquisition unit acquires a life log as video using smart glasses. For example, the video captures various scenes from the user's daily life, such as scenes from the user's commute, conversations with friends, and shopping. The video acquisition unit can record high-resolution video using a camera built into the smart glasses. The video acquisition unit can also upload the video data to the cloud in real time. The current location determination unit determines the user's current location based on the video acquired by the video acquisition unit. For example, the current location determination unit can analyze buildings, scenery, signs, and the like in the video and compare them with GPS information to determine the user's current location with high accuracy. The current location determination unit can also extract feature points in the video using video analysis technology and estimate location information. The current location determination unit can also determine the user's current location using Wi-Fi signals or Bluetooth beacons. The information provision unit provides nearby discount coupon information and event information based on the current location determined by the current location determination unit. For example, it can provide information such as "Here's a 10% off coupon for a nearby cafe" or "There's a music festival happening nearby." The information provider can also provide personalized information based on the user's preferences and past behavioral history. Furthermore, the information provider can provide information updated in real time. The call response unit responds to user requests by providing route guidance, train timetables, and conversational support. For example, if the user requests, "Tell me the way to the station," the call response unit will guide the user to the optimal route from their current location to the station. If the user requests, "What time is the next train?", the call response unit will display the train timetable. Furthermore, the unit can respond to requests such as, "Can I talk to you for a moment?" and engage in conversation with the user. The summary video creator creates a summary video of the day from the videos acquired at the end of the day. For example, it can select memorable scenes for the user, such as fun conversations with friends, beautiful scenery, or special events, and create a summary video centered around those scenes.The summary video creation unit can also use generation AI to analyze the video, determine the excitement level, and focus on the summary. Furthermore, the summary video creation unit can also add effects and music to the video using video editing technology. This allows the life log acquisition system according to the embodiment to make the user's daily life more convenient and enjoyable. For example, users can quickly get directions when they get lost, or participate in nearby events without missing them. Furthermore, by looking back on the events of the day at the end of the day, users can feel a sense of fulfillment.

[0030] The video acquisition unit simultaneously records the user's biometric information using a sensor built into the smart glasses and links it to the video. For example, the video acquisition unit uses a heart rate sensor built into the smart glasses to record the user's heart rate in real time and link it to the video data. For example, the video acquisition unit records the user's heart rate when exercising or relaxing along with the video. The video acquisition unit also uses a body temperature sensor built into the smart glasses to record the user's body temperature in real time and link it to the video data. For example, the video acquisition unit records the user's body temperature when the user is out or indoors along with the video. Furthermore, the video acquisition unit also uses a blood pressure sensor built into the smart glasses to record the user's blood pressure in real time and link it to the video data. For example, the video acquisition unit records the user's blood pressure when the user is feeling stressed along with the video. In this way, by linking the user's biometric information with the video, a more detailed life log can be obtained.

[0031] The video acquisition unit uses gaze tracking technology to identify the object the user is looking at and focuses on recording that part. The video acquisition unit, for example, is equipped with an gaze tracking sensor in smart glasses and identifies the object the user is looking at in real time. For example, it records a sign or product the user is looking at in the center of the video. The video acquisition unit also uses gaze tracking technology to analyze the user's gaze movement and identify the object the user is looking at. For example, it records an object in the direction the user is looking in the video. Furthermore, the video acquisition unit uses gaze tracking technology to analyze the duration of the user's gaze and focuses on recording the object the user is looking at for the longest time. For example, it records a landscape or person the user is looking at for a long time in the video. In this way, by focusing on recording the object the user is looking at, more important scenes can be captured.

[0032] The video acquisition unit adds a voice recognition function and simultaneously records the content of the user's conversation as text. The video acquisition unit, for example, uses a microphone built into the smart glasses to recognize the user's conversation in real time and record it as text data. For example, the content of a conversation with a friend is converted into text. The video acquisition unit also simultaneously records the content of the user's conversation as text using voice recognition technology. For example, deep learning-based voice recognition technology is used to perform highly accurate text conversion. Furthermore, the video acquisition unit uses the voice recognition function to save the content of the user's conversation as text data. For example, the content of the conversation can be searched or referenced later. In this way, by recording the content of the user's conversation as text, it can be easily searched or referenced later.

[0033] The video acquisition unit simultaneously records surrounding environmental sounds while capturing video and synchronizes the video and audio, resulting in a more realistic recording. The video acquisition unit, for example, uses a microphone built into the smart glasses to record surrounding environmental sounds in real time and synchronizes them with the video data. For example, it records sounds of the hustle and bustle of a city or natural sounds. The video acquisition unit also uses a dedicated microphone to record environmental sounds and acquire high-quality audio data. For example, it clearly records the sound of the wind or birdsong. Furthermore, the video acquisition unit uses an algorithm to synchronize the video and audio, accurately matching the video and audio. For example, it compares the video timestamp with the audio timestamp. This synchronizes the video and audio, enabling a more realistic recording.

[0034] The current location determination unit determines the current location using surrounding Wi-Fi signals and Bluetooth beacons in addition to video analysis, improving accuracy. The current location determination unit determines the current location using, for example, surrounding Wi-Fi signals in addition to video analysis. For example, the current location is determined with high accuracy based on location information from Wi-Fi access points. The current location determination unit also determines the current location using Bluetooth beacons. For example, it measures the signal strength of Bluetooth beacons and performs location triangulation. Furthermore, the current location determination unit combines Wi-Fi signals and Bluetooth beacons to improve the accuracy of determining the current location. For example, it integrates data from both Wi-Fi signals and Bluetooth beacons to provide more accurate location information. As a result, the accuracy of determining the current location is improved by using Wi-Fi signals and Bluetooth beacons.

[0035] The current location determination unit analyzes the clothing and behavior of people in the video and provides information related to specific events and seasons. The current location determination unit, for example, analyzes the clothing of people in the video and provides information related to the season. For example, it detects winter coats and summer T-shirts and suggests event information according to the season. The current location determination unit also analyzes the behavior of people in the video and provides information related to specific events. For example, it analyzes the behavior of participants in a festival or sporting event and provides event information. Furthermore, the current location determination unit uses video analysis technology to analyze people's clothing and behavior in real time and provide related information. For example, it analyzes the movements of people in the video and understands the status of an event. In this way, by analyzing people's clothing and behavior, it is possible to provide information related to seasons and events.

[0036] In addition to identifying the current location, the current location identification unit analyzes past movement history and learns the user's behavior patterns to predict future behavior. In addition to identifying the current location, the current location identification unit, for example, analyzes past movement history and learns the user's behavior patterns. For example, it analyzes the user's commuting route and how they spend their weekends. The current location identification unit also uses a machine learning algorithm to learn the user's behavior patterns and predict future behavior. For example, it predicts the places the user is likely to visit next. Furthermore, the current location identification unit combines past movement history and current location information to predict future behavior. For example, it predicts the places the user will visit during a specific time period. In this way, future behavior can be predicted by analyzing past movement history.

[0037] The current location determination unit reflects the user's past preferences and ratings in the information it proposes, thereby providing more personalized information. The current location determination unit, for example, builds a system that personalizes the information it proposes based on the user's past preferences and ratings. For example, it prioritizes suggesting information about restaurants that have been highly rated in the past. The current location determination unit also analyzes the user's behavioral history and provides information that matches the user's preferences. For example, it suggests information based on places the user frequently visits and services the user uses. Furthermore, the current location determination unit provides personalized information based on the user's rating data. For example, it suggests information about events and activities that the user has highly rated. In this way, more personalized information can be provided by reflecting the user's past preferences and ratings.

[0038] The call response unit customizes responses to calls based on the user's past behavioral history and preferences. The call response unit, for example, analyzes the user's past behavioral history and builds a system that customizes responses to calls. For example, the response content is adjusted based on places the user has visited in the past and services the user has used. The call response unit also customizes the response content based on the user's preferences. For example, it provides information about restaurants and activities that the user likes. Furthermore, the call response unit combines the user's behavioral history and preferences to provide a personalized response. For example, it provides information about places and services that the user has given high ratings to in the past. This enables more personalized responses by customizing responses based on the user's past behavioral history and preferences.

[0039] The call response unit provides appropriate information in response to a call, taking into account the user's current activity status (e.g., walking, driving). The call response unit, for example, analyzes the user's current activity status in real time and builds a system that adjusts the response to the call. For example, if the user is walking, it provides a concise response. The call response unit also analyzes the user's current activity status using an acceleration sensor or GPS data. For example, if the user is driving, it provides a response that takes safety into consideration. Furthermore, the call response unit provides appropriate information according to the user's activity status. For example, if the user is exercising, it provides information related to exercise. This allows for a more appropriate response by providing information taking into account the user's current activity status.

[0040] The call response unit enables responses to calls in multiple languages, and also supports international users. The call response unit, for example, builds a system that enables responses to calls in multiple languages. For example, responses are made in languages ​​such as English, Japanese, and French. The call response unit also uses language recognition technology to automatically determine the user's language and respond in an appropriate language. For example, if the user speaks in English, the response is made in English. Furthermore, the call response unit performs translation in real time using a translation algorithm for supporting multiple languages. For example, the content spoken by the user is translated into another language and responded to. This allows responses in multiple languages, making it possible to support international users.

[0041] In addition to video analysis, the summary video creation unit analyzes the user's biometric information and focuses on summarizing scenes that show heightened emotions. In addition to video analysis, the summary video creation unit, for example, analyzes the user's heart rate data to identify scenes that show heightened emotions. For example, the moment when the heart rate suddenly increases is reflected in the video. The summary video creation unit also analyzes body temperature data to identify scenes that show heightened emotions. For example, the moment when the body temperature increases is reflected in the video. Furthermore, the summary video creation unit focuses on summarizing scenes that show heightened emotions based on the user's biometric information. For example, it analyzes changes in heart rate and body temperature to select scenes that show heightened emotions. In this way, by analyzing the user's biometric information, it is possible to focus on summarizing scenes that show heightened emotions.

[0042] The summary video creation unit reflects the user's preferences and past ratings in the editing of the video to create a more personalized summary video. The summary video creation unit, for example, builds a system that customizes the edited content of the video based on the user's preferences. For example, it incorporates music and effects that the user likes into the video. The summary video creation unit also adjusts the edited content of the video based on the user's past ratings. For example, it prioritizes editing scenes that the user has given high ratings. Furthermore, the summary video creation unit creates a personalized summary video that reflects the user's preferences and past ratings. For example, it edits the video based on the user's preferred style or theme. In this way, a more personalized summary video can be created by reflecting the user's preferences and past ratings.

[0043] The summary video creation unit adds the user's voice memos and text memos to the summary video of the day, allowing for more detailed reflection. The summary video creation unit, for example, builds a system in which the user records the events of the day as voice memos and incorporates the voice data into the summary video. For example, the user records their thoughts about the day in audio. The summary video creation unit also adds content recorded by the user as text memos to the summary video. For example, notes written by the user are displayed in the video. Furthermore, the summary video creation unit uses voice recognition technology to convert the user's voice memos into text data and incorporates it into the video. For example, what the user said is displayed as text. This allows for more detailed reflection by adding the user's voice memos and text memos.

[0044] The summary video creation unit automatically posts the summary video to the user's social media account to promote sharing. The summary video creation unit, for example, builds a system that automatically posts the summary video to the user's social media account. For example, the video is automatically posted to Facebook or Instagram. The summary video creation unit also automatically posts the video using a social media API. For example, the video is posted to an account set by the user. Furthermore, the summary video creation unit provides a function to customize the posted content, allowing the user to add a description and tags to the video. For example, the user can add a comment to the video. In this way, the summary video can be automatically posted to social media, promoting sharing.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The life log acquisition system can also be equipped with a health management unit that monitors the user's health status. For example, sensors built into the smart glasses can be used to record the user's biometric information, such as heart rate, blood pressure, and body temperature, in real time, and an alert can be issued if an abnormality is detected. The health management unit can also analyze the user's exercise volume and sleep patterns and provide advice on maintaining health. For example, if it detects a lack of exercise, it can suggest appropriate exercise. Furthermore, the health management unit can record the user's diet and provide advice on nutritional balance. This allows for comprehensive management of the user's health status and supports health maintenance.

[0047] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0048] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0049] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0050] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0051] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0052] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0053] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0054] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0055] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The video acquisition unit uses the smart glasses to acquire a life log as video. For example, it captures various scenes from the user's daily life, such as the scenery during their commute, conversations with friends, and shopping. The video acquisition unit can also record high-resolution video using the camera built into the smart glasses. Furthermore, the video acquisition unit can also upload the video data to the cloud in real time. Step 2: The current location determination unit determines the current location based on the video acquired by the video acquisition unit. For example, it analyzes buildings, scenery, signs, etc. shown in the video and compares them with GPS information to determine the user's current location with high accuracy. The current location determination unit can also use video analysis technology to extract feature points in the video and estimate location information. Furthermore, the current location determination unit can also determine the current location using Wi-Fi signals and Bluetooth beacons. Step 3: The information providing unit provides nearby discount coupon information and event information based on the current location identified by the current location identifying unit. For example, it provides information such as "There is a 10% off coupon that can be used at a nearby cafe" or "There is a music festival being held nearby." The information providing unit can also provide personalized information based on the user's preferences and past behavioral history. Furthermore, the information providing unit can provide information that is updated in real time. Step 4: The call response unit responds to calls from the user by providing route guidance, train timetables, and acting as a conversation partner. For example, if the user says, "Tell me the way to the station," the call response unit will guide the user to the optimal route from the current location to the station. If the user asks, "What time is the next train?", the unit will display the train timetable. Furthermore, the unit can respond to calls such as, "Can you please listen to me for a moment?" and can engage in conversation with the user. Step 5: At the end of the day, the summary video creation unit creates a summary video of the day from the footage acquired. For example, it selects scenes that made an impression on the user, such as fun conversations with friends, beautiful scenery, or special events, and creates a video that focuses on those scenes. The summary video creation unit can also use generative AI to analyze the video, determine which scenes are most exciting, and focus on those scenes to summarize. Furthermore, the summary video creation unit can use video editing technology to add effects and music to the video.

[0058] (Example 2) A life log acquisition system according to an embodiment of the present invention is a system that acquires a life log as video using smart glasses, analyzes the video using a generation AI, and provides various information. This makes the user's daily life more convenient and enjoyable.

[0059] A life log acquisition system according to an embodiment includes a video acquisition unit, a current location determination unit, an information provision unit, a call response unit, and a summary video creation unit. The video acquisition unit acquires a life log as video using smart glasses. For example, the video captures various scenes from the user's daily life, such as scenes from the user's commute, conversations with friends, and shopping. The video acquisition unit can record high-resolution video using a camera built into the smart glasses. The video acquisition unit can also upload the video data to the cloud in real time. The current location determination unit determines the user's current location based on the video acquired by the video acquisition unit. For example, the current location determination unit can analyze buildings, scenery, signs, and the like in the video and compare them with GPS information to determine the user's current location with high accuracy. The current location determination unit can also extract feature points in the video using video analysis technology and estimate location information. The current location determination unit can also determine the user's current location using Wi-Fi signals or Bluetooth beacons. The information provision unit provides nearby discount coupon information and event information based on the current location determined by the current location determination unit. For example, it can provide information such as "Here's a 10% off coupon for a nearby cafe" or "There's a music festival happening nearby." The information provider can also provide personalized information based on the user's preferences and past behavioral history. Furthermore, the information provider can provide information updated in real time. The call response unit responds to user requests by providing route guidance, train timetables, and conversational support. For example, if the user requests, "Tell me the way to the station," the call response unit will guide the user to the optimal route from their current location to the station. If the user requests, "What time is the next train?", the call response unit will display the train timetable. Furthermore, the unit can respond to requests such as, "Can I talk to you for a moment?" and engage in conversation with the user. The summary video creator creates a summary video of the day from the videos acquired at the end of the day. For example, it can select memorable scenes for the user, such as fun conversations with friends, beautiful scenery, or special events, and create a summary video centered around those scenes.The summary video creation unit can also use generation AI to analyze the video, determine the excitement level, and focus on the summary. Furthermore, the summary video creation unit can also add effects and music to the video using video editing technology. This allows the life log acquisition system according to the embodiment to make the user's daily life more convenient and enjoyable. For example, users can quickly get directions when they get lost, or participate in nearby events without missing them. Furthermore, by looking back on the events of the day at the end of the day, users can feel a sense of fulfillment.

[0060] The video acquisition unit simultaneously records the user's biometric information using a sensor built into the smart glasses and links it to the video. For example, the video acquisition unit uses a heart rate sensor built into the smart glasses to record the user's heart rate in real time and link it to the video data. For example, the video acquisition unit records the user's heart rate when exercising or relaxing along with the video. The video acquisition unit also uses a body temperature sensor built into the smart glasses to record the user's body temperature in real time and link it to the video data. For example, the video acquisition unit records the user's body temperature when the user is out or indoors along with the video. Furthermore, the video acquisition unit also uses a blood pressure sensor built into the smart glasses to record the user's blood pressure in real time and link it to the video data. For example, the video acquisition unit records the user's blood pressure when the user is feeling stressed along with the video. In this way, by linking the user's biometric information with the video, a more detailed life log can be obtained.

[0061] The video acquisition unit uses gaze tracking technology to identify the object the user is looking at and focuses on recording that part. The video acquisition unit, for example, is equipped with an gaze tracking sensor in smart glasses and identifies the object the user is looking at in real time. For example, it records a sign or product the user is looking at in the center of the video. The video acquisition unit also uses gaze tracking technology to analyze the user's gaze movement and identify the object the user is looking at. For example, it records an object in the direction the user is looking in the video. Furthermore, the video acquisition unit uses gaze tracking technology to analyze the duration of the user's gaze and focuses on recording the object the user is looking at for the longest time. For example, it records a landscape or person the user is looking at for a long time in the video. In this way, by focusing on recording the object the user is looking at, more important scenes can be captured.

[0062] The video acquisition unit uses an emotion estimation function to analyze the user's emotional state in real time and automatically highlight moments when emotions are heightened. The video acquisition unit, for example, uses a camera and microphone built into the smart glasses to analyze the user's facial expressions and tone of voice and estimate the emotional state in real time. For example, it detects smiling faces and excited voices. The video acquisition unit also uses an emotion estimation algorithm to analyze the user's emotional state in real time. For example, it estimates the user's emotions using facial expression recognition technology. Furthermore, the video acquisition unit uses the emotion estimation function to automatically highlight moments when the user's emotions are heightened. For example, it highlights moments when the user feels joy or excitement in the video. This makes it possible to highlight important scenes by highlighting moments when the user's emotions are heightened.

[0063] The video acquisition unit adds a voice recognition function and simultaneously records the content of the user's conversation as text. The video acquisition unit, for example, uses a microphone built into the smart glasses to recognize the user's conversation in real time and record it as text data. For example, the content of a conversation with a friend is converted into text. The video acquisition unit also simultaneously records the content of the user's conversation as text using voice recognition technology. For example, deep learning-based voice recognition technology is used to perform highly accurate text conversion. Furthermore, the video acquisition unit uses the voice recognition function to save the content of the user's conversation as text data. For example, the content of the conversation can be searched or referenced later. In this way, by recording the content of the user's conversation as text, it can be easily searched or referenced later.

[0064] The video acquisition unit simultaneously records surrounding environmental sounds while capturing video and synchronizes the video and audio, resulting in a more realistic recording. The video acquisition unit, for example, uses a microphone built into the smart glasses to record surrounding environmental sounds in real time and synchronizes them with the video data. For example, it records sounds of the hustle and bustle of a city or natural sounds. The video acquisition unit also uses a dedicated microphone to record environmental sounds and acquire high-quality audio data. For example, it clearly records the sound of the wind or birdsong. Furthermore, the video acquisition unit uses an algorithm to synchronize the video and audio, accurately matching the video and audio. For example, it compares the video timestamp with the audio timestamp. This synchronizes the video and audio, enabling a more realistic recording.

[0065] The video acquisition unit uses the emotion estimation function to map locations where the user felt a particular emotion on a map, creating an emotion map. For example, the video acquisition unit uses the emotion estimation function to identify locations where the user felt a particular emotion in real time and map them on a map. For example, it displays locations where the user felt joy on the map. The video acquisition unit also uses an emotion estimation algorithm to analyze the user's emotional state and create an emotion map. For example, it uses facial expression recognition technology to estimate the user's emotions and map them on the map. Furthermore, the video acquisition unit provides a dedicated application for creating the emotion map, allowing the user to visually grasp changes in emotions. For example, it displays color-coded emotion markers on the map. By mapping the user's emotions on the map, changes in emotions can be visually grasped.

[0066] The current location determination unit determines the current location using surrounding Wi-Fi signals and Bluetooth beacons in addition to video analysis, improving accuracy. The current location determination unit determines the current location using, for example, surrounding Wi-Fi signals in addition to video analysis. For example, the current location is determined with high accuracy based on location information from Wi-Fi access points. The current location determination unit also determines the current location using Bluetooth beacons. For example, it measures the signal strength of Bluetooth beacons and performs location triangulation. Furthermore, the current location determination unit combines Wi-Fi signals and Bluetooth beacons to improve the accuracy of determining the current location. For example, it integrates data from both Wi-Fi signals and Bluetooth beacons to provide more accurate location information. As a result, the accuracy of determining the current location is improved by using Wi-Fi signals and Bluetooth beacons.

[0067] The current location determination unit analyzes the clothing and behavior of people in the video and provides information related to specific events and seasons. The current location determination unit, for example, analyzes the clothing of people in the video and provides information related to the season. For example, it detects winter coats and summer T-shirts and suggests event information according to the season. The current location determination unit also analyzes the behavior of people in the video and provides information related to specific events. For example, it analyzes the behavior of participants in a festival or sporting event and provides event information. Furthermore, the current location determination unit uses video analysis technology to analyze people's clothing and behavior in real time and provide related information. For example, it analyzes the movements of people in the video and understands the status of an event. In this way, by analyzing people's clothing and behavior, it is possible to provide information related to seasons and events.

[0068] The current location identification unit uses the emotion estimation function to preferentially suggest places and events that the user is likely to be interested in. For example, the current location identification unit uses the emotion estimation function to identify places that the user is likely to be interested in and preferentially suggest them. For example, it suggests revisiting places that the user felt positive about in the past. The current location identification unit also uses an emotion estimation algorithm to analyze the user's emotional state and suggest events that the user is likely to be interested in. For example, it preferentially provides information about events in which the user has shown interest. Furthermore, the current location identification unit makes personalized suggestions based on the user's emotion data. For example, it suggests places and events that the user is likely to be interested in based on the user's preferences and past behavioral history. This makes it possible to suggest places and events that the user is likely to be interested in based on the user's emotions.

[0069] In addition to identifying the current location, the current location identification unit analyzes past movement history and learns the user's behavior patterns to predict future behavior. In addition to identifying the current location, the current location identification unit, for example, analyzes past movement history and learns the user's behavior patterns. For example, it analyzes the user's commuting route and how they spend their weekends. The current location identification unit also uses a machine learning algorithm to learn the user's behavior patterns and predict future behavior. For example, it predicts the places the user is likely to visit next. Furthermore, the current location identification unit combines past movement history and current location information to predict future behavior. For example, it predicts the places the user will visit during a specific time period. In this way, future behavior can be predicted by analyzing past movement history.

[0070] The current location determination unit reflects the user's past preferences and ratings in the information it proposes, thereby providing more personalized information. The current location determination unit, for example, builds a system that personalizes the information it proposes based on the user's past preferences and ratings. For example, it prioritizes suggesting information about restaurants that have been highly rated in the past. The current location determination unit also analyzes the user's behavioral history and provides information that matches the user's preferences. For example, it suggests information based on places the user frequently visits and services the user uses. Furthermore, the current location determination unit provides personalized information based on the user's rating data. For example, it suggests information about events and activities that the user has highly rated. In this way, more personalized information can be provided by reflecting the user's past preferences and ratings.

[0071] The current location identification unit uses the emotion estimation function to suggest revisiting places where the user felt positive emotions in the past. For example, the current location identification unit uses the emotion estimation function to identify places where the user felt positive emotions in the past and suggest revisiting them. For example, the current location identification unit suggests revisiting places where the user has fond memories. The current location identification unit also uses an emotion estimation algorithm to analyze the user's emotional state and identify places where the user felt positive emotions. For example, it uses facial expression recognition technology to estimate the user's emotions and identify places where the user felt positive emotions in the past. Furthermore, the current location identification unit suggests places to revisit based on the user's emotion data. For example, it suggests places where the user felt particularly positive emotions among places they have visited in the past. In this way, by suggesting revisiting places where the user felt positive emotions in the past, user satisfaction is improved.

[0072] The call response unit analyzes the tone and speed of the user's voice and provides an appropriate response according to the user's emotional state. The call response unit, for example, analyzes the tone of the user's voice and builds a system that provides a response according to the user's emotional state. For example, if the user is excited, the call response unit responds in a calm tone. The call response unit also uses voice waveform analysis technology to analyze the tone and speed of the user's voice. For example, it analyzes the pitch and speed of the voice to estimate the user's emotional state. Furthermore, the call response unit uses an emotion estimation algorithm to analyze the user's emotional state in real time and provide an appropriate response. For example, if the user is feeling stressed, the call response unit makes suggestions to help them relax. In this way, by analyzing the tone and speed of the user's voice, an appropriate response according to the user's emotional state is possible.

[0073] The call response unit customizes responses to calls based on the user's past behavioral history and preferences. The call response unit, for example, analyzes the user's past behavioral history and builds a system that customizes responses to calls. For example, the response content is adjusted based on places the user has visited in the past and services the user has used. The call response unit also customizes the response content based on the user's preferences. For example, it provides information about restaurants and activities that the user likes. Furthermore, the call response unit combines the user's behavioral history and preferences to provide a personalized response. For example, it provides information about places and services that the user has given high ratings to in the past. This enables more personalized responses by customizing responses based on the user's past behavioral history and preferences.

[0074] The prompt response unit uses the emotion estimation function to make suggestions to help the user relax when they are feeling stressed. The prompt response unit, for example, uses the emotion estimation function to build a system that makes suggestions to help the user relax when they are feeling stressed. For example, it suggests relaxing music or a meditation app. The prompt response unit also uses an emotion estimation algorithm to analyze the user's emotional state in real time and make suggestions to help the user relax when they are feeling stressed. For example, it suggests places or activities where the user can relax. Furthermore, the prompt response unit estimates the user's stress level based on the user's biometric information and makes suggestions to help the user relax. For example, it analyzes changes in heart rate and body temperature to estimate the stress level. As a result, it is possible to make suggestions to help the user relax when they are feeling stressed, thereby reducing the user's stress.

[0075] The call response unit provides appropriate information in response to a call, taking into account the user's current activity status (e.g., walking, driving). The call response unit, for example, analyzes the user's current activity status in real time and builds a system that adjusts the response to the call. For example, if the user is walking, it provides a concise response. The call response unit also analyzes the user's current activity status using an acceleration sensor or GPS data. For example, if the user is driving, it provides a response that takes safety into consideration. Furthermore, the call response unit provides appropriate information according to the user's activity status. For example, if the user is exercising, it provides information related to exercise. This allows for a more appropriate response by providing information taking into account the user's current activity status.

[0076] The call response unit enables responses to calls in multiple languages, and also supports international users. The call response unit, for example, builds a system that enables responses to calls in multiple languages. For example, responses are made in languages ​​such as English, Japanese, and French. The call response unit also uses language recognition technology to automatically determine the user's language and respond in an appropriate language. For example, if the user speaks in English, the response is made in English. Furthermore, the call response unit performs translation in real time using a translation algorithm for supporting multiple languages. For example, the content spoken by the user is translated into another language and responded to. This allows responses in multiple languages, making it possible to support international users.

[0077] The call response unit uses the emotion estimation function to suggest participation in online communities or events when the user feels lonely. The call response unit, for example, uses the emotion estimation function to build a system that suggests participation in online communities or events when the user feels lonely. For example, it suggests online forums or chat groups that the user is interested in. The call response unit also uses an emotion estimation algorithm to analyze the user's emotional state in real time and suggests participation in communities or events when the user feels lonely. For example, it provides information about local events that the user can participate in. Furthermore, the call response unit makes suggestions to reduce feelings of loneliness based on the user's emotion data. For example, it suggests activities or social events that the user is interested in. In this way, the user's feelings of loneliness can be reduced by suggesting participation in online communities or events when the user feels lonely.

[0078] In addition to video analysis, the summary video creation unit analyzes the user's biometric information and focuses on summarizing scenes that show heightened emotions. In addition to video analysis, the summary video creation unit, for example, analyzes the user's heart rate data to identify scenes that show heightened emotions. For example, the moment when the heart rate suddenly increases is reflected in the video. The summary video creation unit also analyzes body temperature data to identify scenes that show heightened emotions. For example, the moment when the body temperature increases is reflected in the video. Furthermore, the summary video creation unit focuses on summarizing scenes that show heightened emotions based on the user's biometric information. For example, it analyzes changes in heart rate and body temperature to select scenes that show heightened emotions. In this way, by analyzing the user's biometric information, it is possible to focus on summarizing scenes that show heightened emotions.

[0079] The summary video creation unit reflects the user's preferences and past ratings in the editing of the video to create a more personalized summary video. The summary video creation unit, for example, builds a system that customizes the edited content of the video based on the user's preferences. For example, it incorporates music and effects that the user likes into the video. The summary video creation unit also adjusts the edited content of the video based on the user's past ratings. For example, it prioritizes editing scenes that the user has given high ratings. Furthermore, the summary video creation unit creates a personalized summary video that reflects the user's preferences and past ratings. For example, it edits the video based on the user's preferred style or theme. In this way, a more personalized summary video can be created by reflecting the user's preferences and past ratings.

[0080] The summary video creation unit uses an emotion estimation function to compile scenes that the user felt most positively. For example, the summary video creation unit uses the emotion estimation function to identify scenes that the user felt most positively and incorporate them into the summary video. For example, the summary video creation unit reflects moments when the user smiles in the video. The summary video creation unit also uses an emotion estimation algorithm to analyze the user's emotional state and select scenes that the user felt positively. For example, it uses facial expression recognition technology to estimate the user's emotions and identify scenes that the user felt positively. Furthermore, the summary video creation unit compiles scenes that the user felt positively based on the user's emotion data. For example, it highlights moments that the user particularly enjoyed in the video. This allows the creation of a summary video that is more satisfying by compiles scenes that the user felt most positively.

[0081] The summary video creation unit adds the user's voice memos and text memos to the summary video of the day, allowing for more detailed reflection. The summary video creation unit, for example, builds a system in which the user records the events of the day as voice memos and incorporates the voice data into the summary video. For example, the user records their thoughts about the day in audio. The summary video creation unit also adds content recorded by the user as text memos to the summary video. For example, notes written by the user are displayed in the video. Furthermore, the summary video creation unit uses voice recognition technology to convert the user's voice memos into text data and incorporates it into the video. For example, what the user said is displayed as text. This allows for more detailed reflection by adding the user's voice memos and text memos.

[0082] The summary video creation unit automatically posts the summary video to the user's social media account to promote sharing. The summary video creation unit, for example, builds a system that automatically posts the summary video to the user's social media account. For example, the video is automatically posted to Facebook or Instagram. The summary video creation unit also automatically posts the video using a social media API. For example, the video is posted to an account set by the user. Furthermore, the summary video creation unit provides a function to customize the posted content, allowing the user to add a description and tags to the video. For example, the user can add a comment to the video. In this way, the summary video can be automatically posted to social media, promoting sharing.

[0083] The summary video creation unit uses an emotion estimation function to highlight scenes that particularly moved the user and provide information and suggestions related to those scenes. The summary video creation unit, for example, uses the emotion estimation function to identify scenes that particularly moved the user and incorporate them as highlights in the summary video. For example, it highlights moments that moved the user in the video. The summary video creation unit also uses an emotion estimation algorithm to analyze the user's emotional state and select scenes that moved the user. For example, it uses facial expression recognition technology to estimate the user's emotions and identify moving scenes. Furthermore, the summary video creation unit provides information and suggestions related to the moving scenes. For example, it provides information about places and events that moved the user. In this way, by highlighting scenes that particularly moved the user and providing information and suggestions related to those scenes, user satisfaction is improved.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The life log acquisition system can also be equipped with a health management unit that monitors the user's health status. For example, sensors built into the smart glasses can be used to record the user's biometric information, such as heart rate, blood pressure, and body temperature, in real time, and an alert can be issued if an abnormality is detected. The health management unit can also analyze the user's exercise volume and sleep patterns and provide advice on maintaining health. For example, if it detects a lack of exercise, it can suggest appropriate exercise. Furthermore, the health management unit can record the user's diet and provide advice on nutritional balance. This allows for comprehensive management of the user's health status and supports health maintenance.

[0086] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0087] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0088] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0089] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0090] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0091] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0092] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0093] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0094] The video acquisition unit can also learn the user's visual preferences and automatically apply video filters according to the preferences. For example, it can learn the user's preferred color tones and effects and apply them to the video. The video acquisition unit can also analyze the user's past video viewing history and suggest video filters that match the user's preferences. For example, it can select a filter based on the style of video the user frequently watches. Furthermore, the video acquisition unit can adjust the filter according to the user's real-time environment. For example, it can adjust the brightness when outdoors and the color temperature when indoors. This makes it possible to provide video that matches the user's visual preferences.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The video acquisition unit uses the smart glasses to acquire a life log as video. For example, it captures various scenes from the user's daily life, such as the scenery during their commute, conversations with friends, and shopping. The video acquisition unit can also record high-resolution video using the camera built into the smart glasses. Furthermore, the video acquisition unit can also upload the video data to the cloud in real time. Step 2: The current location determination unit determines the current location based on the video acquired by the video acquisition unit. For example, it analyzes buildings, scenery, signs, etc. shown in the video and compares them with GPS information to determine the user's current location with high accuracy. The current location determination unit can also use video analysis technology to extract feature points in the video and estimate location information. Furthermore, the current location determination unit can also determine the current location using Wi-Fi signals and Bluetooth beacons. Step 3: The information providing unit provides nearby discount coupon information and event information based on the current location identified by the current location identifying unit. For example, it provides information such as "There is a 10% off coupon that can be used at a nearby cafe" or "There is a music festival being held nearby." The information providing unit can also provide personalized information based on the user's preferences and past behavioral history. Furthermore, the information providing unit can provide information that is updated in real time. Step 4: The call response unit responds to calls from the user by providing route guidance, train timetables, and acting as a conversation partner. For example, if the user says, "Tell me the way to the station," the call response unit will guide the user to the optimal route from the current location to the station. If the user asks, "What time is the next train?", the unit will display the train timetable. Furthermore, the unit can respond to calls such as, "Can you please listen to me for a moment?" and can engage in conversation with the user. Step 5: At the end of the day, the summary video creation unit creates a summary video of the day from the footage acquired. For example, it selects scenes that made an impression on the user, such as fun conversations with friends, beautiful scenery, or special events, and creates a video that focuses on those scenes. The summary video creation unit can also use generative AI to analyze the video, determine which scenes are most exciting, and focus on those scenes to summarize. Furthermore, the summary video creation unit can use video editing technology to add effects and music to the video.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 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 image acquisition unit that acquires a life log as an image using smart glasses; a current location determination unit that determines a current location based on the image acquired by the image acquisition unit; an information providing unit that provides nearby advantageous coupon information and event information based on the current location identified by the current location identifying unit; a call response unit that responds to calls from a user by providing route guidance, train timetables, and acting as a conversation partner; and a summary video creation unit that creates a summary video of the day from the video acquired at the end of the day. A system characterized by:

2. The image acquisition unit Using eye-tracking technology, the object the user is looking at is identified and that part is recorded in a focused manner.

2. The system of claim 1.

3. The current location identification unit In addition to video analysis, it also uses surrounding Wi-Fi signals and Bluetooth beacons to determine the location and improve accuracy.

2. The system of claim 1.

4. The call response unit Analyzing the tone and rate of the user's voice and responding appropriately according to the user's emotional state 2. The system of claim 1.

5. The summary video creation unit In addition to video analysis, the system analyzes the user's biometric information and prioritizes scenes that show heightened emotions.

2. The system of claim 1.

6. The image acquisition unit Using emotion estimation function, the emotional state of the user is analyzed in real time and moments of heightened emotion are automatically highlighted.

2. The system of claim 1.

7. The current location identification unit Using emotion estimation function, the system prioritizes suggestions of places and events that the user may be interested in.

2. The system of claim 1.

8. The call response unit Using the emotion estimation function, if the user is feeling stressed, suggestions to help them relax are made.

2. The system of claim 1.

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