system

Abstract

We provide the system. [Solution] A means of obtaining activity information from users, A means of analyzing acquired activity information to identify important events, A means for automatically generating videos with narration using natural language generation technology based on identified events, A means of providing the generated video to the user via communication means, A means of securely managing user information through encryption, A means to enable local governments to utilize information collected to improve the overall safety and resident satisfaction of the city, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

[0006] "Activity data" refers to data related to a user's daily activities, such as biometric information, behavioral information, and environmental information.

[0007] "Important events" are those that stand out as particularly noteworthy occurrences or situations identified from user activity data.

[0008] A "narrated video" is a video file that combines visual content with audio commentary, serving as a means of conveying information to users both visually and aurally.

[0009] "Natural language generation technology" refers to the technology used by computers to generate human language, and specifically the process of creating text using AI.

[0010] "Privacy protection" refers to taking security measures to protect users' personal information and activity data from unauthorized access and misuse. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention relates to a system that automatically records a user's daily activities, analyzes that data, and generates videos with narration. This system is realized by acquiring activity data from wearable devices and smart home sensor terminals and transmitting that data to a server.

[0033] First, the device continuously collects activity data such as the user's location, heart rate, and walking data. The collected data is periodically transferred to a server via the internet. The server integrates the received data and analyzes it using AI. This identifies important events and the user's emotional state. To identify important events, a pre-configured algorithm is used to extract data that deviates from normal patterns. Based on these analysis results, narration is created using natural language generation technology.

[0034] Next, the server selects particularly noteworthy scenes from the user's day and generates a video with narration. The finished video is then adjusted to maintain quality and sent to the user's device. The user can watch the video presented on their device and reflect on their day. They can also share this video with family and friends.

[0035] Furthermore, user activity data must be managed securely. The server protects privacy by encrypting data and strictly controlling access. This allows users to use the system with peace of mind. For example, if a user enjoys sports with friends one day, a video reflecting the user's fulfilling experience can be generated from their heart rate, location information, and conversation content. In this way, important moments in daily life can be easily reviewed, providing a more enriching experience.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The device continuously collects activity data from the user's wearable devices and smart home sensors. This includes heart rate, location information, and voice data.

[0039] Step 2:

[0040] The device sends the collected activity data to the server via the internet at regular intervals. The transmission is encrypted for data security.

[0041] Step 3:

[0042] The server integrates the received activity data, removing duplicates and imputing missing values ​​to maintain consistency. This creates a daily activity timeline.

[0043] Step 4:

[0044] The server uses AI algorithms to analyze the integrated data and identify important events and emotional shifts. For example, it can detect special activities from sudden increases in heart rate or changes in movement patterns.

[0045] Step 5:

[0046] The server generates narration content using natural language generation technology based on the analysis results. This narration is adjusted according to the user's emotions and activities.

[0047] Step 6:

[0048] The server combines selected key scenes and narration to generate a video. Video editing techniques are then used to create a visually appealing format.

[0049] Step 7:

[0050] The server sends the completed video with narration to the user's device. The video data is optimized taking into account the user's network conditions.

[0051] Step 8:

[0052] The device notifies the user that a video has been sent and prompts them to watch it. The user can then play the video and reflect on their day.

[0053] Step 9:

[0054] The server maintains privacy by encrypting and storing user activity data and implementing access controls. As a result, user data is managed securely.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In modern society, users are expected to enrich their lives by meticulously recording their daily activities and reviewing those records. However, manually recording and analyzing daily activities to identify important moments is laborious and difficult for the average consumer. Furthermore, protecting the privacy of collected data is a crucial issue.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for acquiring information based on user activity, means for identifying unique events using the acquired information, and means for automatically generating videos with commentary using language generation technology based on the identified events. This allows users to easily acquire videos to review their daily activities, easily share important moments, and ensure data security.

[0060] A "user" refers to an individual who uses the system to acquire their own activity data and enjoys videos generated based on that data.

[0061] "Activity-based information" refers to information that includes biometric data and movement history data, such as the user's daily location information, heart rate, and exercise level.

[0062] "Identifying unusual events" refers to the process of detecting unusual patterns or significant events from acquired activity data.

[0063] "Language generation technology" refers to technology that uses artificial intelligence models to automatically generate sentences and explanations in natural language based on data.

[0064] "Videos with commentary" refers to video content that includes narration or textual information about a unique event.

[0065] "Secretively managing" means protecting users' personal information and activity data from third parties by encrypting and restricting access to store it safely.

[0066] This invention is a system that acquires and analyzes user activity data and automatically generates videos with narration. This allows users to easily obtain videos reflecting on their day while ensuring the confidentiality of their data.

[0067] The device uses sensors to record the user's daily activities. Specifically, it can use commonly available wearable devices or smartphones to acquire the user's movement routes and biometric information. For example, by utilizing smartphones equipped with location services and biometric data sensors, it is possible to collect very detailed activity data.

[0068] The server integrates activity data sent from the terminals and analyzes it using AI technology. Software such as Python and deep learning libraries can be used for the analysis. This process applies algorithms to identify unique events and the user's emotional state.

[0069] Based on the analysis results, the server uses a generative AI model to create narration. This model generates natural language output consistent with the user's data. The generated narration is then used to create a video, which is then transformed into a video with commentary. This video generation is then finished using video editing software (e.g., a common video editing tool) to create highly engaging content that appeals to both sight and sound.

[0070] Ultimately, users receive the completed video through their devices, allowing them to vividly relive their daily experiences. Furthermore, for security reasons, the server encrypts user information and manages data under strict access control.

[0071] As a concrete example, by using data on tourist destinations visited by a user on their day off and providing a prompt to the AI ​​model such as, "Generate a video with pleasant music and emotionally rich narration based on yesterday's activity data," it becomes possible to recreate the wonderful experiences of that day as a video.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The device collects data about the user's daily activities. Specifically, it uses wearable devices and smartphones to acquire biometric information such as location, steps taken, and heart rate. This data becomes input data and is stored in real time. As output, the device prepares to send this activity data to a server.

[0075] Step 2:

[0076] The device periodically sends the collected activity data to the server via the internet. For security reasons, the data is encrypted using the SSL / TLS protocol. The input is the data collected in step 1, and the output is a message indicating that the data has been successfully sent to the server.

[0077] Step 3:

[0078] The server integrates the received activity data and centralizes the information acquired from each sensor. This input is the raw data received from the terminal. The server converts the data into an analyzable format and preprocesses it to prepare the output for the next analysis step.

[0079] Step 4:

[0080] The server uses AI technology to analyze the organized data. During the analysis, it identifies unique events and emotional changes from user activity patterns. The input here is pre-processed data, and the output is a list of unique events and metadata. This data is obtained by performing anomaly detection using algorithms.

[0081] Step 5:

[0082] The server uses a generative AI model to create narration based on identified events. The input is the event data extracted in step 4. The AI ​​is given text as a prompt, such as "Generate a video with emotionally rich narration and pleasant music based on yesterday's activity data," and the model outputs an explanation in natural language.

[0083] Step 6:

[0084] The server uses video editing software to generate a video incorporating narration. The input data consists of the narration generated in step 5 and images / videos of the user's activities. The output is a video with commentary. In this process, the server edits the video to be visually appealing and incorporates appropriate music and text.

[0085] Step 7:

[0086] The server properly compresses and encrypts the completed video and sends it to the user's device. The output is a viewable video file, and the input is the product generated in step 6. By receiving this, the user can review their activities.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In modern society, improving safety and convenience through the use of personal activity data is considered important. However, collecting and analyzing personal activity information presents many challenges in terms of privacy protection and information security management. Furthermore, there is a need for methods to effectively utilize information obtained from daily activities to improve the quality of life for residents. This invention was proposed to solve these problems.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for acquiring activity information from users, means for analyzing the acquired activity information to identify important events, means for automatically generating narrated videos using natural language generation technology based on the identified events, means for providing the generated videos to users via communication means, means for securely managing user information through encryption, and means for enabling local governments to utilize the collected information to improve the safety and resident satisfaction of the entire city. This makes it possible to extract useful information from individual activity data and improve safety and quality of life while protecting privacy.

[0092] A "user" is an entity that utilizes the system and provides information about their personal activities.

[0093] "Activity information" refers to a collection of digital information, including location data, biometric data, and environmental data related to a user's daily activities.

[0094] "Means" refers to technical methods, devices, or programs used to realize the various functions that constitute a system.

[0095] A "server" is a computer system on a network that receives information from users, processes it, and provides necessary functions.

[0096] An "event" refers to a user's actions or circumstances identified as important from the analyzed activity data.

[0097] "Natural language generation technology" is a technology that uses machine learning and AI to enable computers to generate text using human language.

[0098] "Narrated videos" are audiovisual content that includes audio commentary based on the user's activity information.

[0099] "Communication methods" refer to technologies such as internet connectivity and wireless communication used to transmit generated videos and information to users.

[0100] "Encryption" is a technology that maintains the confidentiality of information by obfuscating digital information based on a specific algorithm.

[0101] A "local government" is an administrative organization that constitutes a local public body and works to improve the safety and satisfaction of its residents.

[0102] "Overall safety and resident satisfaction in a town" refers to the sense of public security in a particular area and the satisfaction residents derive from living in that area.

[0103] The embodiment of this invention is a system that collects user activity information, analyzes that data to extract important events, and creates a video with narration using natural language generation technology. The system mainly consists of the following components.

[0104] First, the system utilizes wearable devices or smartphones worn by the user as terminals. These terminals have the capability to collect user activity information, such as location, heart rate, and environmental data, in real time from sensors. The collected data is encrypted and then transmitted to a server via the internet. This encryption is for the purpose of protecting privacy.

[0105] The server centrally manages the received activity information and applies advanced algorithms to identify important events. This process utilizes machine learning models and AI technologies to detect anomalies and noteworthy events from the collected data patterns.

[0106] Next, the server uses a generative AI model to create narration in natural language in order to record the user's experience as a video. Based on this generated narration, video processing software is used to automatically generate a video with narration.

[0107] The generated videos are delivered to the user's device using communication technology. During this process, the video quality is adjusted to ensure a comfortable viewing experience for the user. Through these videos, users can reflect on daily events, and videos containing safety information and local information provided by local governments are also offered. This contributes to improving the quality of life for residents and enhancing local safety.

[0108] For example, a user's activities when visiting a park with their family on a holiday are recorded and played back via a generated video. This video includes narration describing their enjoyment, and external weather information is integrated to help the user vividly recall the memories of that day.

[0109] An example of a prompt for a generative AI model would be: "Create a narration recreating a memory of going to the park with family today. Example: We arrived at the park at 10:00 AM and started strolling around 10 minutes later."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device acquires user activity information in real time. It receives location information, heart rate, and environmental data from sensors in wearable devices and smartphones as input. This data undergoes initial data format conversion on the device, is encrypted to enhance security, and then transmitted to the server via the internet.

[0113] Step 2:

[0114] The server receives encrypted data sent from the terminal. It decrypts the input data and imports it into a centralized database. In this process, the data is sorted chronologically, missing values ​​and outliers are cleaned, and the data is formatted into an analyzable format.

[0115] Step 3:

[0116] The server uses an analysis engine to identify significant events. The input here is organized activity data. Machine learning algorithms are used to identify anomalies or noteworthy events that deviate from normal patterns. The output of this process is a list of identified events.

[0117] Step 4:

[0118] The server generates narration using a generative AI model based on identified events. The input consists of a list of events and related data. The AI ​​model uses prompts to generate natural language narration and outputs the result in text format.

[0119] Step 5:

[0120] The server generates a video with narration based on the generated narration. The input here is the narration text and associated visual data. Appropriate video scenes are selected using video processing software, and the narration is converted into speech using speech synthesis technology and integrated into the video. The output is the completed video data.

[0121] Step 6:

[0122] The server transfers the generated video to the user's terminal using communication technology. The input here is the completed video. The video file is converted into a streaming format accessible to the user, and distribution is performed by notifying the user application.

[0123] Step 7:

[0124] Users watch videos they receive notifications for on their devices. In this step, video playback software provides the best possible viewing experience tailored to the user. Users can also review their daily experiences and obtain safety and local information.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system that incorporates an emotion engine that automatically records a user's daily activities and recognizes the user's emotions from that data. This system provides users with a means to reflect on their daily lives in a more enriching way by offering them videos with narration.

[0127] First, the device acquires activity data from wearable devices worn by the user or smart home sensors installed in the home. This data includes heart rate, location information, and voice data. The device then transmits the acquired data to a server via the internet. This allows the server to monitor the user's activity in real time.

[0128] Next, the server analyzes the received activity data using an emotion engine to determine the user's emotional state. The emotion engine identifies emotions from, for example, changes in voice tone and heart rate, and based on this, identifies specific emotional events as important events. In this process, it analyzes various emotions such as happiness, sadness, and surprise.

[0129] The server then automatically generates a video with narration, using the identified emotional event as the core of the narration. The narration is created using natural language generation technology, corresponding to the user's emotional state. For example, adding narration that reflects the excitement the user felt when attending a music concert makes the video more emotionally engaging.

[0130] Finally, users can view the generated video on their device. They can also use the video sharing function on their device to share the emotional moment with friends and family. Furthermore, the server uses encryption technology to protect user data and manages it securely. This ensures that users can use the system with peace of mind in a private environment. For example, the excitement of a sporting event a user participated in can be expressed as an emotional climax in the video, allowing users to record their memorable experiences on video.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device acquires user activity data from wearable devices and smart home sensors. This data includes heart rate, location information, and voice data.

[0134] Step 2:

[0135] The device sends the acquired activity data to the server at regular intervals. The transmitted data is encrypted for security purposes.

[0136] Step 3:

[0137] The server integrates the received activity data and performs preprocessing to maintain data integrity. This organizes the data in a timeline format.

[0138] Step 4:

[0139] The server uses an emotion engine to analyze data and identify the user's emotional state. For example, it uses voice tone analysis and heart rate variability to determine whether the user is happy or stressed.

[0140] Step 5:

[0141] The server selects important events based on the emotional events identified by the emotion engine. This selection takes into account the rise and fall of emotions during the user's activities.

[0142] Step 6:

[0143] The server uses natural language generation technology to create narration corresponding to the selected event. The narration is adjusted to match the user's emotions.

[0144] Step 7:

[0145] The server combines narration with selected scenes to generate a personalized narrated video for the user. The video is then visually edited professionally.

[0146] Step 8:

[0147] The server sends the generated video to the user's device. The video size and format are optimized to match the device's specifications.

[0148] Step 9:

[0149] The device notifies the user when a video arrives and makes it easy to access. The user can then watch the video and enjoy scenes that reflect their emotions.

[0150] Step 10:

[0151] The server securely manages user activity data and generated videos by storing them in an encrypted state in a database and implementing access control as needed. This process ensures user privacy.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] In modern times, there is a challenge in that there are limited means to accurately record and reflect on people's emotions in their daily lives. In particular, there are no systems available that can grasp a user's individual emotional state in real time and enable rich recollection based on that information.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for acquiring activity information from a user, means for analyzing the acquired activity information to identify an emotional state, and means for automatically generating a video with narration based on the identified emotional state. This enables real-time analysis of emotional states and the provision of compelling content to users based on that information.

[0157] "Activity information" refers to information that users generate in their daily lives, including physiological, geographical, and audio data.

[0158] "Emotional state" refers to data that indicates the emotional state of a user, obtained by analyzing activity information.

[0159] A "narrated video" is video content that incorporates audio commentary provided based on the user's emotional state.

[0160] "Personal information" refers to unique data about a user that requires privacy protection.

[0161] "Natural language generation technology" is a technology in which computer programs generate natural language that humans can understand based on text data.

[0162] This invention is a system that automatically records a user's daily activities, analyzes the user's emotions in real time, and generates a video with narration that reflects the results. This system utilizes a combination of multiple hardware and software components.

[0163] The device acquires activity information using wearable devices and smart home sensing equipment. Specifically, heart rate monitors, GPS location acquisition devices, and voice detection devices are used for data collection. This data is transmitted to the device via Bluetooth or Wi-Fi.

[0164] The device transmits the collected activity information to a server via the internet. This process uses protocols such as HTTP, and data encryption is employed for security purposes.

[0165] The server performs sentiment analysis based on the received activity information. This involves applying speech recognition software based on tone analysis of speech, and statistical methods for heart rate variability analysis. Sentiment analysis uses sentiment analysis libraries for speech data, as well as programming languages ​​such as Python.

[0166] The analyzed emotional state is used in creating narrated videos through natural language generation technology using a generative AI model. Specifically, the generative AI is given prompts such as, "Generate narration that reflects the exhilaration a user feels when enjoying upbeat music," and then generates the narration text.

[0167] The generated narration is incorporated into the video content and output as the final video. This allows for the creation of memorable videos based on the user's specific emotional events.

[0168] Users view videos created by this system through a dedicated application. Video sharing is done using social networking services and messaging functions, and users' personal information is protected by standard encryption technologies such as TLS.

[0169] This allows users to reflect on their emotional state and emotionally relive important moments in their daily lives.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The device acquires activity information from wearable devices and smart home sensors worn by the user. Specifically, it receives heart rate, location information, and audio data via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local memory. Physical sensor data is taken in as input, and each data point is captured in real time based on this data. The output provides instantaneous physiological and environmental information of the user.

[0173] Step 2:

[0174] The terminal sends the activity information obtained in Step 1 to the server using the SSL / TLS encryption protocol. Specifically, the terminal securely uploads the data over the internet using HTTPS. At this time, the input is the physiological and environmental information generated earlier, and the output is the data received by the server in a format that can be parsed.

[0175] Step 3:

[0176] The server receives the transmitted data and performs emotion analysis. Based on previous functions, it analyzes tone from the audio data and identifies the user's emotional state from heart rate variability. Specifically, it applies an emotion analysis algorithm and analyzes the input sensor data to label emotions. The output generates the user's emotional state (e.g., happiness, surprise) numerically or categorically.

[0177] Step 4:

[0178] The server generates narration text using a generative AI model based on the results of the emotion analysis. Specifically, the emotion data from step 3 is input to the generative AI as a prompt, giving instructions such as, "Generate narration that reflects the exhilaration the user feels when enjoying upbeat music." After data processing based on this prompt, the narration text is output using natural language generation technology.

[0179] Step 5:

[0180] The server uses the generated narration to create a video with narration using automated video editing software. Specifically, it incorporates the narration and related visual effects into a video template and creates a video file after editing is complete. Here, the input is the narration text and user activity visual data, and the output is a viewable video file.

[0181] Step 6:

[0182] The user receives and watches the completed narrated video on their device. The device plays this video using its default media player app. Specifically, the user presses the video playback button and can enjoy the emotional narration playing through the video and audio. The input is the completed video, and the output is the user's emotional experience through their sight and hearing.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] In today's living environment, there is a need for systems that record individual daily activities and efficiently recognize users' emotions. Furthermore, there is a need for technology that generates and provides videos that reflect users' emotional states so that they can comfortably reflect on their own experiences. However, current technologies often suffer from low accuracy in emotion recognition and insufficient personalization of generated content, and solving these problems is the objective of this invention.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for acquiring activity information from the user, means for analyzing the acquired activity information to identify important events and analyze the emotional state, and means for automatically generating a video with narration added based on the identified events and providing it to the user. This enables personalized feedback that corresponds to the user's emotional state, allowing for a richer reflection on daily activity experiences.

[0188] A "user" is an individual who uses this system or is the subject whose activities are recorded.

[0189] "Activity information" refers to data about the user's daily activities, including heart rate, location information, and voice tone.

[0190] "Important events" are moments or events that are particularly noteworthy from an emotional perspective, as analyzed from user activity data.

[0191] "Emotional state" refers to the emotional response or mental state a user exhibits when performing a particular activity.

[0192] "Methods for automatically generating videos" refers to a function that automatically creates visual content with narration based on identified important events and corresponding emotional states.

[0193] "Feedback" refers to information provided to users that encourages them to re-evaluate and emotionally respond to their own activities and feelings.

[0194] In order to implement this invention, it is essential that the user wears or installs a wearable device or smart home sensor to collect activity information. This allows the device to acquire activity information in real time and transmit it to the server.

[0195] The server uses Python and related libraries (e.g., OpenCV, Librosa) to run the emotion engine and analyze the acquired data. It processes heart rate, location information, and voice data to identify the user's emotional state.

[0196] Based on identified key events, the server generates a video with narration using video processing tools such as FFmpeg. This video is customized for each user, with narration added to take into account the user's emotional state.

[0197] The generated videos are transferred to the device, allowing users to emotionally reflect on their daily activities by watching them. The videos incorporate visually appealing and emotionally engaging elements, enriching the user's life experience.

[0198] Furthermore, the servers encrypt and securely manage data to protect user privacy. This allows users to use the system with peace of mind.

[0199] For example, a user could record a picnic in a park during a holiday, capturing moments of interaction with their pet. The video could include clips of moments that brought a smile to the user's face, allowing them to emotionally relive those memories.

[0200] Examples of prompt statements to input into a generative AI model include the following:

[0201] "Based on the following data, please generate a video with narration that resonates with the user's emotions: Time: 14:30, Heart rate: 72, Voice tone: Lively, Event: Family picnic in the park."

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The device acquires activity information from wearable devices worn by the user and smart home sensors installed in the home. Inputs include heart rate, location information, and voice data, which are stored in a temporary database.

[0205] Step 2:

[0206] The device transmits the acquired activity information to the server via the internet. The input is the data collected in step 1, and the output arrives at the server as data packets. The data is appropriately formatted and prepared for transmission.

[0207] Step 3:

[0208] The server analyzes the received activity information using an emotion engine. The input is the data packets sent in step 2, and the emotional state is output by performing data processing, including voice tone analysis and changes in heart rate.

[0209] Step 4:

[0210] The server identifies significant events based on the identified emotional states. The input is the emotional states analyzed in step 3, and the output is organized as a list of significant events. This list includes events of particularly high emotional interest.

[0211] Step 5:

[0212] The server automatically generates narrated videos based on identified events. The input consists of the list from step 4 and the user's emotional state, and the video generation software generates the output video clip. The video is accompanied by narration created by a speech generation engine.

[0213] Step 6:

[0214] The server transfers the generated video to the terminal. The input is the video generated in step 5, and the output is a video file format playable on the terminal. The user can watch this to reflect on their daily life.

[0215] Step 7:

[0216] The server encrypts and securely stores all activity information and generated content to ensure user privacy. Inputs consist of all data that needs to be saved, and outputs are stored in secure storage as encrypted data.

[0217] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0218] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 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.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention relates to a system that automatically records a user's daily activities, analyzes that data, and generates videos with narration. This system is realized by acquiring activity data from wearable devices and smart home sensor terminals and transmitting that data to a server.

[0234] First, the device continuously collects activity data such as the user's location, heart rate, and walking data. The collected data is periodically transferred to a server via the internet. The server integrates the received data and analyzes it using AI. This identifies important events and the user's emotional state. To identify important events, a pre-configured algorithm is used to extract data that deviates from normal patterns. Based on these analysis results, narration is created using natural language generation technology.

[0235] Next, the server selects particularly noteworthy scenes from the user's day and generates a video with narration. The finished video is then adjusted to maintain quality and sent to the user's device. The user can watch the video presented on their device and reflect on their day. They can also share this video with family and friends.

[0236] Furthermore, user activity data must be managed securely. The server protects privacy by encrypting data and strictly controlling access. This allows users to use the system with peace of mind. For example, if a user enjoys sports with friends one day, a video reflecting the user's fulfilling experience can be generated from their heart rate, location information, and conversation content. In this way, important moments in daily life can be easily reviewed, providing a more enriching experience.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The device continuously collects activity data from the user's wearable devices and smart home sensors. This includes heart rate, location information, and voice data.

[0240] Step 2:

[0241] The device sends the collected activity data to the server via the internet at regular intervals. The transmission is encrypted for data security.

[0242] Step 3:

[0243] The server integrates the received activity data, removing duplicates and imputing missing values ​​to maintain consistency. This creates a daily activity timeline.

[0244] Step 4:

[0245] The server uses AI algorithms to analyze the integrated data and identify important events and emotional shifts. For example, it can detect special activities from sudden increases in heart rate or changes in movement patterns.

[0246] Step 5:

[0247] The server generates narration content using natural language generation technology based on the analysis results. This narration is adjusted according to the user's emotions and activities.

[0248] Step 6:

[0249] The server combines selected key scenes and narration to generate a video. Video editing techniques are then used to create a visually appealing format.

[0250] Step 7:

[0251] The server sends the completed video with narration to the user's device. The video data is optimized taking into account the user's network conditions.

[0252] Step 8:

[0253] The device notifies the user that a video has been sent and prompts them to watch it. The user can then play the video and reflect on their day.

[0254] Step 9:

[0255] The server maintains privacy by encrypting and storing user activity data and implementing access controls. As a result, user data is managed securely.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In modern society, users are expected to enrich their lives by meticulously recording their daily activities and reviewing those records. However, manually recording and analyzing daily activities to identify important moments is laborious and difficult for the average consumer. Furthermore, protecting the privacy of collected data is a crucial issue.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for acquiring information based on user activity, means for identifying unique events using the acquired information, and means for automatically generating videos with commentary using language generation technology based on the identified events. This allows users to easily acquire videos to review their daily activities, easily share important moments, and ensure data security.

[0261] A "user" refers to an individual who uses the system to acquire their own activity data and enjoys videos generated based on that data.

[0262] "Activity-based information" refers to information that includes biometric data and movement history data, such as the user's daily location information, heart rate, and exercise level.

[0263] "Identifying unusual events" refers to the process of detecting unusual patterns or significant events from acquired activity data.

[0264] "Language generation technology" refers to technology that uses artificial intelligence models to automatically generate sentences and explanations in natural language based on data.

[0265] "Videos with commentary" refers to video content that includes narration or textual information about a unique event.

[0266] "Secretively managing" means protecting users' personal information and activity data from third parties by encrypting and restricting access to store it safely.

[0267] This invention is a system that acquires and analyzes user activity data and automatically generates videos with narration. This allows users to easily obtain videos reflecting on their day while ensuring the confidentiality of their data.

[0268] The device uses sensors to record the user's daily activities. Specifically, it can use commonly available wearable devices or smartphones to acquire the user's movement routes and biometric information. For example, by utilizing smartphones equipped with location services and biometric data sensors, it is possible to collect very detailed activity data.

[0269] The server integrates activity data sent from the terminals and analyzes it using AI technology. Software such as Python and deep learning libraries can be used for the analysis. This process applies algorithms to identify unique events and the user's emotional state.

[0270] Based on the analysis results, the server uses a generative AI model to create narration. This model generates natural language output consistent with the user's data. The generated narration is then used to create a video, which is then transformed into a video with commentary. This video generation is then finished using video editing software (e.g., a common video editing tool) to create highly engaging content that appeals to both sight and sound.

[0271] Ultimately, users receive the completed video through their devices, allowing them to vividly relive their daily experiences. Furthermore, for security reasons, the server encrypts user information and manages data under strict access control.

[0272] As a concrete example, by using data on tourist destinations visited by a user on their day off and providing a prompt to the AI ​​model such as, "Generate a video with pleasant music and emotionally rich narration based on yesterday's activity data," it becomes possible to recreate the wonderful experiences of that day as a video.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The device collects data about the user's daily activities. Specifically, it uses wearable devices and smartphones to acquire biometric information such as location, steps taken, and heart rate. This data becomes input data and is stored in real time. As output, the device prepares to send this activity data to a server.

[0276] Step 2:

[0277] The device periodically sends the collected activity data to the server via the internet. For security reasons, the data is encrypted using the SSL / TLS protocol. The input is the data collected in step 1, and the output is a message indicating that the data has been successfully sent to the server.

[0278] Step 3:

[0279] The server integrates the received activity data and unifies the information obtained from each sensor. This input is the raw data received from the terminal. The server converts the data into an analyzable format and performs preprocessing to prepare the output for the next analysis step.

[0280] Step 4:

[0281] Based on the organized data, the server performs analysis using AI technology. During the analysis, specific events and emotional changes are identified from the user's activity patterns. The input here is the preprocessed data, and the output is a list of specific events and metadata. This data is obtained by performing anomaly detection using an algorithm.

[0282] Step 5:

[0283] The server uses the generated AI model to create a narration based on the identified events. This input is the event data extracted in Step 4. Text such as "Generate a video with a rich and emotional narration along with pleasant music based on yesterday's activity data" is provided to the AI as a prompt, and the model outputs an explanation in natural language.

[0284] Step 6:

[0285] The server uses video editing software to generate a video incorporating the narration. The input data is the narration generated in Step 5 and the user's activity images / videos. The output is a video with an explanation. In this process, the server edits visually appealing videos and incorporates appropriate music and text into the video.

[0286] Step 7:

[0287] The server properly compresses and encrypts the completed video and sends it to the user's device. The output is a viewable video file, and the input is the product generated in step 6. By receiving this, the user can review their activities.

[0288] (Application Example 1)

[0289] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0290] In modern society, improving safety and convenience through the use of personal activity data is considered important. However, collecting and analyzing personal activity information presents many challenges in terms of privacy protection and information security management. Furthermore, there is a need for methods to effectively utilize information obtained from daily activities to improve the quality of life for residents. This invention was proposed to solve these problems.

[0291] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes means for acquiring activity information from users, means for analyzing the acquired activity information to identify important events, means for automatically generating narrated videos using natural language generation technology based on the identified events, means for providing the generated videos to users via communication means, means for securely managing user information through encryption, and means for enabling local governments to utilize the collected information to improve the safety and resident satisfaction of the entire city. This makes it possible to extract useful information from individual activity data and improve safety and quality of life while protecting privacy.

[0293] A "user" is an entity that utilizes the system and provides information about their personal activities.

[0294] "Activity information" refers to a collection of digital information, including location data, biometric data, and environmental data related to a user's daily activities.

[0295] "Means" refers to technical methods, devices, or programs used to realize the various functions that constitute a system.

[0296] A "server" is a computer system on a network that receives information from users, processes it, and provides necessary functions.

[0297] An "event" refers to a user's actions or circumstances identified as important from the analyzed activity data.

[0298] "Natural language generation technology" is a technology that uses machine learning and AI to enable computers to generate text using human language.

[0299] "Narrated videos" are audiovisual content that includes audio commentary based on the user's activity information.

[0300] "Communication methods" refer to technologies such as internet connectivity and wireless communication used to transmit generated videos and information to users.

[0301] "Encryption" is a technology that maintains the confidentiality of information by obfuscating digital information based on a specific algorithm.

[0302] A "local government" is an administrative organization that constitutes a local public body and works to improve the safety and satisfaction of its residents.

[0303] "Overall safety and resident satisfaction in a town" refers to the sense of public security in a particular area and the satisfaction residents derive from living in that area.

[0304] The embodiments for implementing this invention are a system that collects users' activity information, analyzes the data to extract important events, and creates a narrated video using natural language generation technology. The system mainly consists of the following components.

[0305] First, as the terminal, a wearable device or a smartphone worn by the user is used. This terminal has the function of collecting users' activity information such as location information, heart rate, and environmental data from sensors in real time. The collected data is encrypted and then sent to the server via the Internet. This encryption is for the purpose of protecting privacy.

[0306] The server centrally manages the received activity information and applies advanced algorithms to identify important events. In this process, machine learning models and AI technologies are used to detect anomalies and notable events from the collected data patterns.

[0307] Next, the server creates a narration in natural language using a generation AI model in order to record the user's experience as a video. Based on this generated narration, video processing software is used to automatically generate a narrated video.

[0308] The generated video is provided to the user's terminal using communication technology. In this process, the quality of the video is adjusted to ensure that the user can comfortably view the video on the terminal. Through this video, the user can review daily events, and videos containing safety information provided by the local government and information about the region are also provided. This contributes to improving the quality of residents' lives and the safety of the region.

[0309] As a specific example, the actions of a user when visiting a park with family on a holiday are recorded and played back via the generated video. In this video, the enjoyable moments are explained in narration, and by integrating external weather information, support is provided for the user to vividly recall the memories of that day.

[0310] An example of a prompt for a generative AI model would be: "Create a narration recreating a memory of going to the park with family today. Example: We arrived at the park at 10:00 AM and started strolling around 10 minutes later."

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] The device acquires user activity information in real time. It receives location information, heart rate, and environmental data from sensors in wearable devices and smartphones as input. This data undergoes initial data format conversion on the device, is encrypted to enhance security, and then transmitted to the server via the internet.

[0314] Step 2:

[0315] The server receives encrypted data sent from the terminal. It decrypts the input data and imports it into a centralized database. In this process, the data is sorted chronologically, missing values ​​and outliers are cleaned, and the data is formatted into an analyzable format.

[0316] Step 3:

[0317] The server uses an analysis engine to identify significant events. The input here is organized activity data. Machine learning algorithms are used to identify anomalies or noteworthy events that deviate from normal patterns. The output of this process is a list of identified events.

[0318] Step 4:

[0319] The server generates narration using a generative AI model based on identified events. The input consists of a list of events and related data. The AI ​​model uses prompts to generate natural language narration and outputs the result in text format.

[0320] Step 5:

[0321] The server generates a video with narration based on the generated narration. The input here is the narration text and associated visual data. Appropriate video scenes are selected using video processing software, and the narration is converted into speech using speech synthesis technology and integrated into the video. The output is the completed video data.

[0322] Step 6:

[0323] The server transfers the generated video to the user's terminal using communication technology. The input here is the completed video. The video file is converted into a streaming format accessible to the user, and distribution is performed by notifying the user application.

[0324] Step 7:

[0325] Users watch videos they receive notifications for on their devices. In this step, video playback software provides the best possible viewing experience tailored to the user. Users can also review their daily experiences and obtain safety and local information.

[0326] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0327] This invention is a system that incorporates an emotion engine that automatically records a user's daily activities and recognizes the user's emotions from that data. This system provides users with a means to reflect on their daily lives in a more enriching way by offering them videos with narration.

[0328] First, the device acquires activity data from wearable devices worn by the user or smart home sensors installed in the home. This data includes heart rate, location information, and voice data. The device then transmits the acquired data to a server via the internet. This allows the server to monitor the user's activity in real time.

[0329] Next, the server analyzes the received activity data using an emotion engine to determine the user's emotional state. The emotion engine identifies emotions from, for example, changes in voice tone and heart rate, and based on this, identifies specific emotional events as important events. In this process, it analyzes various emotions such as happiness, sadness, and surprise.

[0330] The server then automatically generates a video with narration, using the identified emotional event as the core of the narration. The narration is created using natural language generation technology, corresponding to the user's emotional state. For example, adding narration that reflects the excitement the user felt when attending a music concert makes the video more emotionally engaging.

[0331] Finally, users can view the generated video on their device. They can also use the video sharing function on their device to share the emotional moment with friends and family. Furthermore, the server uses encryption technology to protect user data and manages it securely. This ensures that users can use the system with peace of mind in a private environment. For example, the excitement of a sporting event a user participated in can be expressed as an emotional climax in the video, allowing users to record their memorable experiences on video.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The device acquires user activity data from wearable devices and smart home sensors. This data includes heart rate, location information, and voice data.

[0335] Step 2:

[0336] The device sends the acquired activity data to the server at regular intervals. The transmitted data is encrypted for security purposes.

[0337] Step 3:

[0338] The server integrates the received activity data and performs preprocessing to maintain data integrity. This organizes the data in a timeline format.

[0339] Step 4:

[0340] The server uses an emotion engine to analyze data and identify the user's emotional state. For example, it uses voice tone analysis and heart rate variability to determine whether the user is happy or stressed.

[0341] Step 5:

[0342] The server selects important events based on the emotional events identified by the emotion engine. This selection takes into account the rise and fall of emotions during the user's activities.

[0343] Step 6:

[0344] The server uses natural language generation technology to create narration corresponding to the selected event. The narration is adjusted to match the user's emotions.

[0345] Step 7:

[0346] The server combines narration with selected scenes to generate a personalized narrated video for the user. The video is then visually edited professionally.

[0347] Step 8:

[0348] The server sends the generated video to the user's device. The video size and format are optimized to match the device's specifications.

[0349] Step 9:

[0350] The device notifies the user when a video arrives and makes it easy to access. The user can then watch the video and enjoy scenes that reflect their emotions.

[0351] Step 10:

[0352] The server securely manages user activity data and generated videos by storing them in an encrypted state in a database and implementing access control as needed. This process ensures user privacy.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] In modern times, there is a challenge in that there are limited means to accurately record and reflect on people's emotions in their daily lives. In particular, there are no systems available that can grasp a user's individual emotional state in real time and enable rich recollection based on that information.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes means for acquiring activity information from a user, means for analyzing the acquired activity information to identify an emotional state, and means for automatically generating a video with narration based on the identified emotional state. This enables real-time analysis of emotional states and the provision of compelling content to users based on that information.

[0358] "Activity information" refers to information that users generate in their daily lives, including physiological, geographical, and audio data.

[0359] "Emotional state" refers to data that indicates the emotional state of a user, obtained by analyzing activity information.

[0360] A "narrated video" is video content that incorporates audio commentary provided based on the user's emotional state.

[0361] "Personal information" refers to unique data about a user that requires privacy protection.

[0362] "Natural language generation technology" is a technology in which computer programs generate natural language that humans can understand based on text data.

[0363] This invention is a system that automatically records a user's daily activities, analyzes the user's emotions in real time, and generates a video with narration that reflects the results. This system utilizes a combination of multiple hardware and software components.

[0364] The device acquires activity information using wearable devices and smart home sensing equipment. Specifically, heart rate monitors, GPS location acquisition devices, and voice detection devices are used for data collection. This data is transmitted to the device via Bluetooth or Wi-Fi.

[0365] The device transmits the collected activity information to a server via the internet. This process uses protocols such as HTTP, and data encryption is employed for security purposes.

[0366] The server performs sentiment analysis based on the received activity information. This involves applying speech recognition software based on tone analysis of speech, and statistical methods for heart rate variability analysis. Sentiment analysis uses sentiment analysis libraries for speech data, as well as programming languages ​​such as Python.

[0367] The analyzed emotional state is used in creating narrated videos through natural language generation technology using a generative AI model. Specifically, the generative AI is given prompts such as, "Generate narration that reflects the exhilaration a user feels when enjoying upbeat music," and then generates the narration text.

[0368] The generated narration is incorporated into the video content and output as the final video. This allows for the creation of memorable videos based on the user's specific emotional events.

[0369] Users view videos created by this system through a dedicated application. Video sharing is done using social networking services and messaging functions, and users' personal information is protected by standard encryption technologies such as TLS.

[0370] This allows users to reflect on their emotional state and emotionally relive important moments in their daily lives.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The device acquires activity information from wearable devices and smart home sensors worn by the user. Specifically, it receives heart rate, location information, and audio data via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local memory. Physical sensor data is taken in as input, and each data point is captured in real time based on this data. The output provides instantaneous physiological and environmental information of the user.

[0374] Step 2:

[0375] The terminal sends the activity information obtained in Step 1 to the server using the SSL / TLS encryption protocol. Specifically, the terminal securely uploads the data over the internet using HTTPS. At this time, the input is the physiological and environmental information generated earlier, and the output is the data received by the server in a format that can be parsed.

[0376] Step 3:

[0377] The server receives the transmitted data and performs emotion analysis. Based on previous functions, it analyzes tone from the audio data and identifies the user's emotional state from heart rate variability. Specifically, it applies an emotion analysis algorithm and analyzes the input sensor data to label emotions. The output generates the user's emotional state (e.g., happiness, surprise) numerically or categorically.

[0378] Step 4:

[0379] The server generates narration text using a generative AI model based on the results of the emotion analysis. Specifically, the emotion data from step 3 is input to the generative AI as a prompt, giving instructions such as, "Generate narration that reflects the exhilaration the user feels when enjoying upbeat music." After data processing based on this prompt, the narration text is output using natural language generation technology.

[0380] Step 5:

[0381] The server uses the generated narration to create a video with narration using automated video editing software. Specifically, it incorporates the narration and related visual effects into a video template and creates a video file after editing is complete. Here, the input is the narration text and user activity visual data, and the output is a viewable video file.

[0382] Step 6:

[0383] The user receives and watches the completed narrated video on their device. The device plays this video using its default media player app. Specifically, the user presses the video playback button and can enjoy the emotional narration playing through the video and audio. The input is the completed video, and the output is the user's emotional experience through their sight and hearing.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0386] In today's living environment, there is a need for systems that record individual daily activities and efficiently recognize users' emotions. Furthermore, there is a need for technology that generates and provides videos that reflect users' emotional states so that they can comfortably reflect on their own experiences. However, current technologies often suffer from low accuracy in emotion recognition and insufficient personalization of generated content, and solving these problems is the objective of this invention.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0388] In this invention, the server includes means for acquiring activity information from the user, means for analyzing the acquired activity information to identify important events and analyze the emotional state, and means for automatically generating a video with narration added based on the identified events and providing it to the user. This enables personalized feedback that corresponds to the user's emotional state, allowing for a richer reflection on daily activity experiences.

[0389] A "user" is an individual who uses this system or is the subject whose activities are recorded.

[0390] "Activity information" refers to data about the user's daily activities, including heart rate, location information, and voice tone.

[0391] "Important events" are moments or events that are particularly noteworthy from an emotional perspective, as analyzed from user activity data.

[0392] "Emotional state" refers to the emotional response or mental state a user exhibits when performing a particular activity.

[0393] "Methods for automatically generating videos" refers to a function that automatically creates visual content with narration based on identified important events and corresponding emotional states.

[0394] "Feedback" refers to information provided to users that encourages them to re-evaluate and emotionally respond to their own activities and feelings.

[0395] In order to implement this invention, it is essential that the user wears or installs a wearable device or smart home sensor to collect activity information. This allows the device to acquire activity information in real time and transmit it to the server.

[0396] The server uses Python and related libraries (e.g., OpenCV, Librosa) to run the emotion engine and analyze the acquired data. It processes heart rate, location information, and voice data to identify the user's emotional state.

[0397] Based on identified key events, the server generates a video with narration using video processing tools such as FFmpeg. This video is customized for each user, with narration added to take into account the user's emotional state.

[0398] The generated videos are transferred to the device, allowing users to emotionally reflect on their daily activities by watching them. The videos incorporate visually appealing and emotionally engaging elements, enriching the user's life experience.

[0399] Furthermore, the servers encrypt and securely manage data to protect user privacy. This allows users to use the system with peace of mind.

[0400] For example, a user could record a picnic in a park during a holiday, capturing moments of interaction with their pet. The video could include clips of moments that brought a smile to the user's face, allowing them to emotionally relive those memories.

[0401] Examples of prompt statements to input into a generative AI model include the following:

[0402] "Based on the following data, please generate a video with narration that resonates with the user's emotions: Time: 14:30, Heart rate: 72, Voice tone: Lively, Event: Family picnic in the park."

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The device acquires activity information from wearable devices worn by the user and smart home sensors installed in the home. Inputs include heart rate, location information, and voice data, which are stored in a temporary database.

[0406] Step 2:

[0407] The device transmits the acquired activity information to the server via the internet. The input is the data collected in step 1, and the output arrives at the server as data packets. The data is appropriately formatted and prepared for transmission.

[0408] Step 3:

[0409] The server analyzes the received activity information using an emotion engine. The input is the data packets sent in step 2, and the emotional state is output by performing data processing, including voice tone analysis and changes in heart rate.

[0410] Step 4:

[0411] The server identifies significant events based on the identified emotional states. The input is the emotional states analyzed in step 3, and the output is organized as a list of significant events. This list includes events of particularly high emotional interest.

[0412] Step 5:

[0413] The server automatically generates narrated videos based on identified events. The input consists of the list from step 4 and the user's emotional state, and the video generation software generates the output video clip. The video is accompanied by narration created by a speech generation engine.

[0414] Step 6:

[0415] The server transfers the generated video to the terminal. The input is the video generated in step 5, and the output is a video file format playable on the terminal. The user can watch this to reflect on their daily life.

[0416] Step 7:

[0417] The server encrypts and securely stores all activity information and generated content to ensure user privacy. Inputs consist of all data that needs to be saved, and outputs are stored in secure storage as encrypted data.

[0418] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0419] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0420] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0424] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0425] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0426] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0427] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0428] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0429] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0430] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0431] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0432] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0433] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0434] This invention relates to a system that automatically records a user's daily activities, analyzes that data, and generates videos with narration. This system is realized by acquiring activity data from wearable devices and smart home sensor terminals and transmitting that data to a server.

[0435] First, the device continuously collects activity data such as the user's location, heart rate, and walking data. The collected data is periodically transferred to a server via the internet. The server integrates the received data and analyzes it using AI. This identifies important events and the user's emotional state. To identify important events, a pre-configured algorithm is used to extract data that deviates from normal patterns. Based on these analysis results, narration is created using natural language generation technology.

[0436] Next, the server selects particularly noteworthy scenes from the user's day and generates a video with narration. The finished video is then adjusted to maintain quality and sent to the user's device. The user can watch the video presented on their device and reflect on their day. They can also share this video with family and friends.

[0437] Furthermore, user activity data must be managed securely. The server protects privacy by encrypting data and strictly controlling access. This allows users to use the system with peace of mind. For example, if a user enjoys sports with friends one day, a video reflecting the user's fulfilling experience can be generated from their heart rate, location information, and conversation content. In this way, important moments in daily life can be easily reviewed, providing a more enriching experience.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The device continuously collects activity data from the user's wearable devices and smart home sensors. This includes heart rate, location information, and voice data.

[0441] Step 2:

[0442] The device sends the collected activity data to the server via the internet at regular intervals. The transmission is encrypted for data security.

[0443] Step 3:

[0444] The server integrates the received activity data, removing duplicates and imputing missing values ​​to maintain consistency. This creates a daily activity timeline.

[0445] Step 4:

[0446] The server uses AI algorithms to analyze the integrated data and identify important events and emotional shifts. For example, it can detect special activities from sudden increases in heart rate or changes in movement patterns.

[0447] Step 5:

[0448] The server generates narration content using natural language generation technology based on the analysis results. This narration is adjusted according to the user's emotions and activities.

[0449] Step 6:

[0450] The server combines selected key scenes and narration to generate a video. Video editing techniques are then used to create a visually appealing format.

[0451] Step 7:

[0452] The server sends the completed video with narration to the user's device. The video data is optimized taking into account the user's network conditions.

[0453] Step 8:

[0454] The device notifies the user that a video has been sent and prompts them to watch it. The user can then play the video and reflect on their day.

[0455] Step 9:

[0456] The server maintains privacy by encrypting and storing user activity data and implementing access controls. As a result, user data is managed securely.

[0457] (Example 1)

[0458] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] In modern society, users are expected to enrich their lives by meticulously recording their daily activities and reviewing those records. However, manually recording and analyzing daily activities to identify important moments is laborious and difficult for the average consumer. Furthermore, protecting the privacy of collected data is a crucial issue.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes means for acquiring information based on user activity, means for identifying unique events using the acquired information, and means for automatically generating videos with commentary using language generation technology based on the identified events. This allows users to easily acquire videos to review their daily activities, easily share important moments, and ensure data security.

[0462] A "user" refers to an individual who uses the system to acquire their own activity data and enjoys videos generated based on that data.

[0463] "Activity-based information" refers to information that includes biometric data and movement history data, such as the user's daily location information, heart rate, and exercise level.

[0464] "Identifying unusual events" refers to the process of detecting unusual patterns or significant events from acquired activity data.

[0465] "Language generation technology" refers to technology that uses artificial intelligence models to automatically generate sentences and explanations in natural language based on data.

[0466] "Videos with commentary" refers to video content that includes narration or textual information about a unique event.

[0467] "Secretively managing" means protecting users' personal information and activity data from third parties by encrypting and restricting access to store it safely.

[0468] This invention is a system that acquires and analyzes user activity data and automatically generates videos with narration. This allows users to easily obtain videos reflecting on their day while ensuring the confidentiality of their data.

[0469] The device uses sensors to record the user's daily activities. Specifically, it can use commonly available wearable devices or smartphones to acquire the user's movement routes and biometric information. For example, by utilizing smartphones equipped with location services and biometric data sensors, it is possible to collect very detailed activity data.

[0470] The server integrates activity data sent from the terminals and analyzes it using AI technology. Software such as Python and deep learning libraries can be used for the analysis. This process applies algorithms to identify unique events and the user's emotional state.

[0471] Based on the analysis results, the server uses a generative AI model to create narration. This model generates natural language output consistent with the user's data. The generated narration is then used to create a video, which is then transformed into a video with commentary. This video generation is then finished using video editing software (e.g., a common video editing tool) to create highly engaging content that appeals to both sight and sound.

[0472] Ultimately, users receive the completed video through their devices, allowing them to vividly relive their daily experiences. Furthermore, for security reasons, the server encrypts user information and manages data under strict access control.

[0473] As a concrete example, by using data on tourist destinations visited by a user on their day off and providing a prompt to the AI ​​model such as, "Generate a video with pleasant music and emotionally rich narration based on yesterday's activity data," it becomes possible to recreate the wonderful experiences of that day as a video.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The device collects data about the user's daily activities. Specifically, it uses wearable devices and smartphones to acquire biometric information such as location, steps taken, and heart rate. This data becomes input data and is stored in real time. As output, the device prepares to send this activity data to a server.

[0477] Step 2:

[0478] The device periodically sends the collected activity data to the server via the internet. For security reasons, the data is encrypted using the SSL / TLS protocol. The input is the data collected in step 1, and the output is a message indicating that the data has been successfully sent to the server.

[0479] Step 3:

[0480] The server integrates the received activity data and centralizes the information acquired from each sensor. This input is the raw data received from the terminal. The server converts the data into an analyzable format and preprocesses it to prepare the output for the next analysis step.

[0481] Step 4:

[0482] The server uses AI technology to analyze the organized data. During the analysis, it identifies unique events and emotional changes from user activity patterns. The input here is pre-processed data, and the output is a list of unique events and metadata. This data is obtained by performing anomaly detection using algorithms.

[0483] Step 5:

[0484] The server uses a generative AI model to create narration based on identified events. The input is the event data extracted in step 4. The AI ​​is given text as a prompt, such as "Generate a video with emotionally rich narration and pleasant music based on yesterday's activity data," and the model outputs an explanation in natural language.

[0485] Step 6:

[0486] The server uses video editing software to generate a video incorporating narration. The input data consists of the narration generated in step 5 and images / videos of the user's activities. The output is a video with commentary. In this process, the server edits the video to be visually appealing and incorporates appropriate music and text.

[0487] Step 7:

[0488] The server properly compresses and encrypts the completed video and sends it to the user's device. The output is a viewable video file, and the input is the product generated in step 6. By receiving this, the user can review their activities.

[0489] (Application Example 1)

[0490] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] In modern society, improving safety and convenience through the use of personal activity data is considered important. However, collecting and analyzing personal activity information presents many challenges in terms of privacy protection and information security management. Furthermore, there is a need for methods to effectively utilize information obtained from daily activities to improve the quality of life for residents. This invention was proposed to solve these problems.

[0492] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0493] In this invention, the server includes means for acquiring activity information from users, means for analyzing the acquired activity information to identify important events, means for automatically generating narrated videos using natural language generation technology based on the identified events, means for providing the generated videos to users via communication means, means for securely managing user information through encryption, and means for enabling local governments to utilize the collected information to improve the safety and resident satisfaction of the entire city. This makes it possible to extract useful information from individual activity data and improve safety and quality of life while protecting privacy.

[0494] A "user" is an entity that utilizes the system and provides information about their personal activities.

[0495] "Activity information" refers to a collection of digital information, including location data, biometric data, and environmental data related to a user's daily activities.

[0496] "Means" refers to technical methods, devices, or programs used to realize the various functions that constitute a system.

[0497] A "server" is a computer system on a network that receives information from users, processes it, and provides necessary functions.

[0498] An "event" refers to a user's actions or circumstances identified as important from the analyzed activity data.

[0499] "Natural language generation technology" is a technology that uses machine learning and AI to enable computers to generate text using human language.

[0500] "Narrated videos" are audiovisual content that includes audio commentary based on the user's activity information.

[0501] "Communication methods" refer to technologies such as internet connectivity and wireless communication used to transmit generated videos and information to users.

[0502] "Encryption" is a technology that maintains the confidentiality of information by obfuscating digital information based on a specific algorithm.

[0503] A "local government" is an administrative organization that constitutes a local public body and works to improve the safety and satisfaction of its residents.

[0504] "Overall safety and resident satisfaction in a town" refers to the sense of public security in a particular area and the satisfaction residents derive from living in that area.

[0505] The embodiment of this invention is a system that collects user activity information, analyzes that data to extract important events, and creates a video with narration using natural language generation technology. The system mainly consists of the following components.

[0506] First, the system utilizes wearable devices or smartphones worn by the user as terminals. These terminals have the capability to collect user activity information, such as location, heart rate, and environmental data, in real time from sensors. The collected data is encrypted and then transmitted to a server via the internet. This encryption is for the purpose of protecting privacy.

[0507] The server centrally manages the received activity information and applies advanced algorithms to identify important events. This process utilizes machine learning models and AI technologies to detect anomalies and noteworthy events from the collected data patterns.

[0508] Next, the server uses a generative AI model to create narration in natural language in order to record the user's experience as a video. Based on this generated narration, video processing software is used to automatically generate a video with narration.

[0509] The generated videos are delivered to the user's device using communication technology. During this process, the video quality is adjusted to ensure a comfortable viewing experience for the user. Through these videos, users can reflect on daily events, and videos containing safety information and local information provided by local governments are also offered. This contributes to improving the quality of life for residents and enhancing local safety.

[0510] For example, a user's activities when visiting a park with their family on a holiday are recorded and played back via a generated video. This video includes narration describing their enjoyment, and external weather information is integrated to help the user vividly recall the memories of that day.

[0511] An example of a prompt for a generative AI model would be: "Create a narration recreating a memory of going to the park with family today. Example: We arrived at the park at 10:00 AM and started strolling around 10 minutes later."

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The device acquires user activity information in real time. It receives location information, heart rate, and environmental data from sensors in wearable devices and smartphones as input. This data undergoes initial data format conversion on the device, is encrypted to enhance security, and then transmitted to the server via the internet.

[0515] Step 2:

[0516] The server receives encrypted data sent from the terminal. It decrypts the input data and imports it into a centralized database. In this process, the data is sorted chronologically, missing values ​​and outliers are cleaned, and the data is formatted into an analyzable format.

[0517] Step 3:

[0518] The server uses an analysis engine to identify significant events. The input here is organized activity data. Machine learning algorithms are used to identify anomalies or noteworthy events that deviate from normal patterns. The output of this process is a list of identified events.

[0519] Step 4:

[0520] The server generates narration using a generative AI model based on identified events. The input consists of a list of events and related data. The AI ​​model uses prompts to generate natural language narration and outputs the result in text format.

[0521] Step 5:

[0522] The server generates a video with narration based on the generated narration. The input here is the narration text and associated visual data. Appropriate video scenes are selected using video processing software, and the narration is converted into speech using speech synthesis technology and integrated into the video. The output is the completed video data.

[0523] Step 6:

[0524] The server transfers the generated video to the user's terminal using communication technology. The input here is the completed video. The video file is converted into a streaming format accessible to the user, and distribution is performed by notifying the user application.

[0525] Step 7:

[0526] Users watch videos they receive notifications for on their devices. In this step, video playback software provides the best possible viewing experience tailored to the user. Users can also review their daily experiences and obtain safety and local information.

[0527] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0528] This invention is a system that incorporates an emotion engine that automatically records a user's daily activities and recognizes the user's emotions from that data. This system provides users with a means to reflect on their daily lives in a more enriching way by offering them videos with narration.

[0529] First, the device acquires activity data from wearable devices worn by the user or smart home sensors installed in the home. This data includes heart rate, location information, and voice data. The device then transmits the acquired data to a server via the internet. This allows the server to monitor the user's activity in real time.

[0530] Next, the server analyzes the received activity data using an emotion engine to determine the user's emotional state. The emotion engine identifies emotions from, for example, changes in voice tone and heart rate, and based on this, identifies specific emotional events as important events. In this process, it analyzes various emotions such as happiness, sadness, and surprise.

[0531] The server then automatically generates a video with narration, using the identified emotional event as the core of the narration. The narration is created using natural language generation technology, corresponding to the user's emotional state. For example, adding narration that reflects the excitement the user felt when attending a music concert makes the video more emotionally engaging.

[0532] Finally, users can view the generated video on their device. They can also use the video sharing function on their device to share the emotional moment with friends and family. Furthermore, the server uses encryption technology to protect user data and manages it securely. This ensures that users can use the system with peace of mind in a private environment. For example, the excitement of a sporting event a user participated in can be expressed as an emotional climax in the video, allowing users to record their memorable experiences on video.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] The device acquires user activity data from wearable devices and smart home sensors. This data includes heart rate, location information, and voice data.

[0536] Step 2:

[0537] The device sends the acquired activity data to the server at regular intervals. The transmitted data is encrypted for security purposes.

[0538] Step 3:

[0539] The server integrates the received activity data and performs preprocessing to maintain data integrity. This organizes the data in a timeline format.

[0540] Step 4:

[0541] The server uses an emotion engine to analyze data and identify the user's emotional state. For example, it uses voice tone analysis and heart rate variability to determine whether the user is happy or stressed.

[0542] Step 5:

[0543] The server selects important events based on the emotional events identified by the emotion engine. This selection takes into account the rise and fall of emotions during the user's activities.

[0544] Step 6:

[0545] The server uses natural language generation technology to create narration corresponding to the selected event. The narration is adjusted to match the user's emotions.

[0546] Step 7:

[0547] The server combines narration with selected scenes to generate a personalized narrated video for the user. The video is then visually edited professionally.

[0548] Step 8:

[0549] The server sends the generated video to the user's device. The video size and format are optimized to match the device's specifications.

[0550] Step 9:

[0551] The device notifies the user when a video arrives and makes it easy to access. The user can then watch the video and enjoy scenes that reflect their emotions.

[0552] Step 10:

[0553] The server securely manages user activity data and generated videos by storing them in an encrypted state in a database and implementing access control as needed. This process ensures user privacy.

[0554] (Example 2)

[0555] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0556] In modern times, there is a challenge in that there are limited means to accurately record and reflect on people's emotions in their daily lives. In particular, there are no systems available that can grasp a user's individual emotional state in real time and enable rich recollection based on that information.

[0557] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0558] In this invention, the server includes means for acquiring activity information from a user, means for analyzing the acquired activity information to identify an emotional state, and means for automatically generating a video with narration based on the identified emotional state. This enables real-time analysis of emotional states and the provision of compelling content to users based on that information.

[0559] "Activity information" refers to information that users generate in their daily lives, including physiological, geographical, and audio data.

[0560] "Emotional state" refers to data that indicates the emotional state of a user, obtained by analyzing activity information.

[0561] A "narrated video" is video content that incorporates audio commentary provided based on the user's emotional state.

[0562] "Personal information" refers to unique data about a user that requires privacy protection.

[0563] "Natural language generation technology" is a technology in which computer programs generate natural language that humans can understand based on text data.

[0564] This invention is a system that automatically records a user's daily activities, analyzes the user's emotions in real time, and generates a video with narration that reflects the results. This system utilizes a combination of multiple hardware and software components.

[0565] The device acquires activity information using wearable devices and smart home sensing equipment. Specifically, heart rate monitors, GPS location acquisition devices, and voice detection devices are used for data collection. This data is transmitted to the device via Bluetooth or Wi-Fi.

[0566] The device transmits the collected activity information to a server via the internet. This process uses protocols such as HTTP, and data encryption is employed for security purposes.

[0567] The server performs sentiment analysis based on the received activity information. This involves applying speech recognition software based on tone analysis of speech, and statistical methods for heart rate variability analysis. Sentiment analysis uses sentiment analysis libraries for speech data, as well as programming languages ​​such as Python.

[0568] The analyzed emotional state is used in creating narrated videos through natural language generation technology using a generative AI model. Specifically, the generative AI is given prompts such as, "Generate narration that reflects the exhilaration a user feels when enjoying upbeat music," and then generates the narration text.

[0569] The generated narration is incorporated into the video content and output as the final video. This allows for the creation of memorable videos based on the user's specific emotional events.

[0570] Users view videos created by this system through a dedicated application. Video sharing is done using social networking services and messaging functions, and users' personal information is protected by standard encryption technologies such as TLS.

[0571] This allows users to reflect on their emotional state and emotionally relive important moments in their daily lives.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The device acquires activity information from wearable devices and smart home sensors worn by the user. Specifically, it receives heart rate, location information, and audio data via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local memory. Physical sensor data is taken in as input, and each data point is captured in real time based on this data. The output provides instantaneous physiological and environmental information of the user.

[0575] Step 2:

[0576] The terminal sends the activity information obtained in Step 1 to the server using the SSL / TLS encryption protocol. Specifically, the terminal securely uploads the data over the internet using HTTPS. At this time, the input is the physiological and environmental information generated earlier, and the output is the data received by the server in a format that can be parsed.

[0577] Step 3:

[0578] The server receives the transmitted data and performs emotion analysis. Based on previous functions, it analyzes tone from the audio data and identifies the user's emotional state from heart rate variability. Specifically, it applies an emotion analysis algorithm and analyzes the input sensor data to label emotions. The output generates the user's emotional state (e.g., happiness, surprise) numerically or categorically.

[0579] Step 4:

[0580] The server generates narration text using a generative AI model based on the results of the emotion analysis. Specifically, the emotion data from step 3 is input to the generative AI as a prompt, giving instructions such as, "Generate narration that reflects the exhilaration the user feels when enjoying upbeat music." After data processing based on this prompt, the narration text is output using natural language generation technology.

[0581] Step 5:

[0582] The server uses the generated narration to create a video with narration using automated video editing software. Specifically, it incorporates the narration and related visual effects into a video template and creates a video file after editing is complete. Here, the input is the narration text and user activity visual data, and the output is a viewable video file.

[0583] Step 6:

[0584] The user receives and watches the completed narrated video on their device. The device plays this video using its default media player app. Specifically, the user presses the video playback button and can enjoy the emotional narration playing through the video and audio. The input is the completed video, and the output is the user's emotional experience through their sight and hearing.

[0585] (Application Example 2)

[0586] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] In today's living environment, there is a need for systems that record individual daily activities and efficiently recognize users' emotions. Furthermore, there is a need for technology that generates and provides videos that reflect users' emotional states so that they can comfortably reflect on their own experiences. However, current technologies often suffer from low accuracy in emotion recognition and insufficient personalization of generated content, and solving these problems is the objective of this invention.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0589] In this invention, the server includes means for acquiring activity information from the user, means for analyzing the acquired activity information to identify important events and analyze the emotional state, and means for automatically generating a video with narration added based on the identified events and providing it to the user. This enables personalized feedback that corresponds to the user's emotional state, allowing for a richer reflection on daily activity experiences.

[0590] A "user" is an individual who uses this system or is the subject whose activities are recorded.

[0591] "Activity information" refers to data about the user's daily activities, including heart rate, location information, and voice tone.

[0592] "Important events" are moments or events that are particularly noteworthy from an emotional perspective, as analyzed from user activity data.

[0593] "Emotional state" refers to the emotional response or mental state a user exhibits when performing a particular activity.

[0594] "Methods for automatically generating videos" refers to a function that automatically creates visual content with narration based on identified important events and corresponding emotional states.

[0595] "Feedback" refers to information provided to users that encourages them to re-evaluate and emotionally respond to their own activities and feelings.

[0596] In order to implement this invention, it is essential that the user wears or installs a wearable device or smart home sensor to collect activity information. This allows the device to acquire activity information in real time and transmit it to the server.

[0597] The server uses Python and related libraries (e.g., OpenCV, Librosa) to run the emotion engine and analyze the acquired data. It processes heart rate, location information, and voice data to identify the user's emotional state.

[0598] Based on identified key events, the server generates a video with narration using video processing tools such as FFmpeg. This video is customized for each user, with narration added to take into account the user's emotional state.

[0599] The generated videos are transferred to the device, allowing users to emotionally reflect on their daily activities by watching them. The videos incorporate visually appealing and emotionally engaging elements, enriching the user's life experience.

[0600] Furthermore, the servers encrypt and securely manage data to protect user privacy. This allows users to use the system with peace of mind.

[0601] For example, a user could record a picnic in a park during a holiday, capturing moments of interaction with their pet. The video could include clips of moments that brought a smile to the user's face, allowing them to emotionally relive those memories.

[0602] Examples of prompt statements to input into a generative AI model include the following:

[0603] "Based on the following data, please generate a video with narration that resonates with the user's emotions: Time: 14:30, Heart rate: 72, Voice tone: Lively, Event: Family picnic in the park."

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The device acquires activity information from wearable devices worn by the user and smart home sensors installed in the home. Inputs include heart rate, location information, and voice data, which are stored in a temporary database.

[0607] Step 2:

[0608] The device transmits the acquired activity information to the server via the internet. The input is the data collected in step 1, and the output arrives at the server as data packets. The data is appropriately formatted and prepared for transmission.

[0609] Step 3:

[0610] The server analyzes the received activity information using an emotion engine. The input is the data packets sent in step 2, and the emotional state is output by performing data processing, including voice tone analysis and changes in heart rate.

[0611] Step 4:

[0612] The server identifies significant events based on the identified emotional states. The input is the emotional states analyzed in step 3, and the output is organized as a list of significant events. This list includes events of particularly high emotional interest.

[0613] Step 5:

[0614] The server automatically generates narrated videos based on identified events. The input consists of the list from step 4 and the user's emotional state, and the video generation software generates the output video clip. The video is accompanied by narration created by a speech generation engine.

[0615] Step 6:

[0616] The server transfers the generated video to the terminal. The input is the video generated in step 5, and the output is a video file format playable on the terminal. The user can watch this to reflect on their daily life.

[0617] Step 7:

[0618] The server encrypts and securely stores all activity information and generated content to ensure user privacy. Inputs consist of all data that needs to be saved, and outputs are stored in secure storage as encrypted data.

[0619] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0620] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0621] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] As shown in Figure 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.

[0625] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0626] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0627] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0628] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0629] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0630] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0631] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0632] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0633] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0634] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0635] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] This invention relates to a system that automatically records a user's daily activities, analyzes that data, and generates videos with narration. This system is realized by acquiring activity data from wearable devices and smart home sensor terminals and transmitting that data to a server.

[0637] First, the device continuously collects activity data such as the user's location, heart rate, and walking data. The collected data is periodically transferred to a server via the internet. The server integrates the received data and analyzes it using AI. This identifies important events and the user's emotional state. To identify important events, a pre-configured algorithm is used to extract data that deviates from normal patterns. Based on these analysis results, narration is created using natural language generation technology.

[0638] Next, the server selects particularly noteworthy scenes from the user's day and generates a video with narration. The finished video is then adjusted to maintain quality and sent to the user's device. The user can watch the video presented on their device and reflect on their day. They can also share this video with family and friends.

[0639] Furthermore, user activity data must be managed securely. The server protects privacy by encrypting data and strictly controlling access. This allows users to use the system with peace of mind. For example, if a user enjoys sports with friends one day, a video reflecting the user's fulfilling experience can be generated from their heart rate, location information, and conversation content. In this way, important moments in daily life can be easily reviewed, providing a more enriching experience.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The device continuously collects activity data from the user's wearable devices and smart home sensors. This includes heart rate, location information, and voice data.

[0643] Step 2:

[0644] The device sends the collected activity data to the server via the internet at regular intervals. The transmission is encrypted for data security.

[0645] Step 3:

[0646] The server integrates the received activity data, removing duplicates and imputing missing values ​​to maintain consistency. This creates a daily activity timeline.

[0647] Step 4:

[0648] The server uses AI algorithms to analyze the integrated data and identify important events and emotional shifts. For example, it can detect special activities from sudden increases in heart rate or changes in movement patterns.

[0649] Step 5:

[0650] The server generates narration content using natural language generation technology based on the analysis results. This narration is adjusted according to the user's emotions and activities.

[0651] Step 6:

[0652] The server combines selected key scenes and narration to generate a video. Video editing techniques are then used to create a visually appealing format.

[0653] Step 7:

[0654] The server sends the completed video with narration to the user's device. The video data is optimized taking into account the user's network conditions.

[0655] Step 8:

[0656] The device notifies the user that a video has been sent and prompts them to watch it. The user can then play the video and reflect on their day.

[0657] Step 9:

[0658] The server maintains privacy by encrypting and storing user activity data and implementing access controls. As a result, user data is managed securely.

[0659] (Example 1)

[0660] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In modern society, users are expected to enrich their lives by meticulously recording their daily activities and reviewing those records. However, manually recording and analyzing daily activities to identify important moments is laborious and difficult for the average consumer. Furthermore, protecting the privacy of collected data is a crucial issue.

[0662] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0663] In this invention, the server includes means for acquiring information based on user activity, means for identifying unique events using the acquired information, and means for automatically generating videos with commentary using language generation technology based on the identified events. This allows users to easily acquire videos to review their daily activities, easily share important moments, and ensure data security.

[0664] A "user" refers to an individual who uses the system to acquire their own activity data and enjoys videos generated based on that data.

[0665] "Activity-based information" refers to information that includes biometric data and movement history data, such as the user's daily location information, heart rate, and exercise level.

[0666] "Identifying unusual events" refers to the process of detecting unusual patterns or significant events from acquired activity data.

[0667] "Language generation technology" refers to technology that uses artificial intelligence models to automatically generate sentences and explanations in natural language based on data.

[0668] "Videos with commentary" refers to video content that includes narration or textual information about a unique event.

[0669] "Secretively managing" means protecting users' personal information and activity data from third parties by encrypting and restricting access to store it safely.

[0670] This invention is a system that acquires and analyzes user activity data and automatically generates videos with narration. This allows users to easily obtain videos reflecting on their day while ensuring the confidentiality of their data.

[0671] The device uses sensors to record the user's daily activities. Specifically, it can use commonly available wearable devices or smartphones to acquire the user's movement routes and biometric information. For example, by utilizing smartphones equipped with location services and biometric data sensors, it is possible to collect very detailed activity data.

[0672] The server integrates activity data sent from the terminals and analyzes it using AI technology. Software such as Python and deep learning libraries can be used for the analysis. This process applies algorithms to identify unique events and the user's emotional state.

[0673] Based on the analysis results, the server uses a generative AI model to create narration. This model generates natural language output consistent with the user's data. The generated narration is then used to create a video, which is then transformed into a video with commentary. This video generation is then finished using video editing software (e.g., a common video editing tool) to create highly engaging content that appeals to both sight and sound.

[0674] Ultimately, users receive the completed video through their devices, allowing them to vividly relive their daily experiences. Furthermore, for security reasons, the server encrypts user information and manages data under strict access control.

[0675] As a concrete example, by using data on tourist destinations visited by a user on their day off and providing a prompt to the AI ​​model such as, "Generate a video with pleasant music and emotionally rich narration based on yesterday's activity data," it becomes possible to recreate the wonderful experiences of that day as a video.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The device collects data about the user's daily activities. Specifically, it uses wearable devices and smartphones to acquire biometric information such as location, steps taken, and heart rate. This data becomes input data and is stored in real time. As output, the device prepares to send this activity data to a server.

[0679] Step 2:

[0680] The device periodically sends the collected activity data to the server via the internet. For security reasons, the data is encrypted using the SSL / TLS protocol. The input is the data collected in step 1, and the output is a message indicating that the data has been successfully sent to the server.

[0681] Step 3:

[0682] The server integrates the received activity data and centralizes the information acquired from each sensor. This input is the raw data received from the terminal. The server converts the data into an analyzable format and preprocesses it to prepare the output for the next analysis step.

[0683] Step 4:

[0684] The server uses AI technology to analyze the organized data. During the analysis, it identifies unique events and emotional changes from user activity patterns. The input here is pre-processed data, and the output is a list of unique events and metadata. This data is obtained by performing anomaly detection using algorithms.

[0685] Step 5:

[0686] The server uses a generative AI model to create narration based on identified events. The input is the event data extracted in step 4. The AI ​​is given text as a prompt, such as "Generate a video with emotionally rich narration and pleasant music based on yesterday's activity data," and the model outputs an explanation in natural language.

[0687] Step 6:

[0688] The server uses video editing software to generate a video incorporating narration. The input data consists of the narration generated in step 5 and images / videos of the user's activities. The output is a video with commentary. In this process, the server edits the video to be visually appealing and incorporates appropriate music and text.

[0689] Step 7:

[0690] The server properly compresses and encrypts the completed video and sends it to the user's device. The output is a viewable video file, and the input is the product generated in step 6. By receiving this, the user can review their activities.

[0691] (Application Example 1)

[0692] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] In modern society, improving safety and convenience through the use of personal activity data is considered important. However, collecting and analyzing personal activity information presents many challenges in terms of privacy protection and information security management. Furthermore, there is a need for methods to effectively utilize information obtained from daily activities to improve the quality of life for residents. This invention was proposed to solve these problems.

[0694] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0695] In this invention, the server includes means for acquiring activity information from users, means for analyzing the acquired activity information to identify important events, means for automatically generating narrated videos using natural language generation technology based on the identified events, means for providing the generated videos to users via communication means, means for securely managing user information through encryption, and means for enabling local governments to utilize the collected information to improve the safety and resident satisfaction of the entire city. This makes it possible to extract useful information from individual activity data and improve safety and quality of life while protecting privacy.

[0696] A "user" is an entity that utilizes the system and provides information about their personal activities.

[0697] "Activity information" refers to a collection of digital information, including location data, biometric data, and environmental data related to a user's daily activities.

[0698] "Means" refers to technical methods, devices, or programs used to realize the various functions that constitute a system.

[0699] A "server" is a computer system on a network that receives information from users, processes it, and provides necessary functions.

[0700] An "event" refers to a user's actions or circumstances identified as important from the analyzed activity data.

[0701] "Natural language generation technology" is a technology that uses machine learning and AI to enable computers to generate text using human language.

[0702] "Narrated videos" are audiovisual content that includes audio commentary based on the user's activity information.

[0703] "Communication methods" refer to technologies such as internet connectivity and wireless communication used to transmit generated videos and information to users.

[0704] "Encryption" is a technology that maintains the confidentiality of information by obfuscating digital information based on a specific algorithm.

[0705] A "local government" is an administrative organization that constitutes a local public body and works to improve the safety and satisfaction of its residents.

[0706] "Overall safety and resident satisfaction in a town" refers to the sense of public security in a particular area and the satisfaction residents derive from living in that area.

[0707] The embodiment of this invention is a system that collects user activity information, analyzes that data to extract important events, and creates a video with narration using natural language generation technology. The system mainly consists of the following components.

[0708] First, the system utilizes wearable devices or smartphones worn by the user as terminals. These terminals have the capability to collect user activity information, such as location, heart rate, and environmental data, in real time from sensors. The collected data is encrypted and then transmitted to a server via the internet. This encryption is for the purpose of protecting privacy.

[0709] The server centrally manages the received activity information and applies advanced algorithms to identify important events. This process utilizes machine learning models and AI technologies to detect anomalies and noteworthy events from the collected data patterns.

[0710] Next, the server uses a generative AI model to create narration in natural language in order to record the user's experience as a video. Based on this generated narration, video processing software is used to automatically generate a video with narration.

[0711] The generated videos are delivered to the user's device using communication technology. During this process, the video quality is adjusted to ensure a comfortable viewing experience for the user. Through these videos, users can reflect on daily events, and videos containing safety information and local information provided by local governments are also offered. This contributes to improving the quality of life for residents and enhancing local safety.

[0712] For example, a user's activities when visiting a park with their family on a holiday are recorded and played back via a generated video. This video includes narration describing their enjoyment, and external weather information is integrated to help the user vividly recall the memories of that day.

[0713] An example of a prompt for a generative AI model would be: "Create a narration recreating a memory of going to the park with family today. Example: We arrived at the park at 10:00 AM and started strolling around 10 minutes later."

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The device acquires user activity information in real time. It receives location information, heart rate, and environmental data from sensors in wearable devices and smartphones as input. This data undergoes initial data format conversion on the device, is encrypted to enhance security, and then transmitted to the server via the internet.

[0717] Step 2:

[0718] The server receives encrypted data sent from the terminal. It decrypts the input data and imports it into a centralized database. In this process, the data is sorted chronologically, missing values ​​and outliers are cleaned, and the data is formatted into an analyzable format.

[0719] Step 3:

[0720] The server uses an analysis engine to identify significant events. The input here is organized activity data. Machine learning algorithms are used to identify anomalies or noteworthy events that deviate from normal patterns. The output of this process is a list of identified events.

[0721] Step 4:

[0722] The server generates narration using a generative AI model based on identified events. The input consists of a list of events and related data. The AI ​​model uses prompts to generate natural language narration and outputs the result in text format.

[0723] Step 5:

[0724] The server generates a video with narration based on the generated narration. The input here is the narration text and associated visual data. Appropriate video scenes are selected using video processing software, and the narration is converted into speech using speech synthesis technology and integrated into the video. The output is the completed video data.

[0725] Step 6:

[0726] The server transfers the generated video to the user's terminal using communication technology. The input here is the completed video. The video file is converted into a streaming format accessible to the user, and distribution is performed by notifying the user application.

[0727] Step 7:

[0728] Users watch videos they receive notifications for on their devices. In this step, video playback software provides the best possible viewing experience tailored to the user. Users can also review their daily experiences and obtain safety and local information.

[0729] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0730] This invention is a system that incorporates an emotion engine that automatically records a user's daily activities and recognizes the user's emotions from that data. This system provides users with a means to reflect on their daily lives in a more enriching way by offering them videos with narration.

[0731] First, the device acquires activity data from wearable devices worn by the user or smart home sensors installed in the home. This data includes heart rate, location information, and voice data. The device then transmits the acquired data to a server via the internet. This allows the server to monitor the user's activity in real time.

[0732] Next, the server analyzes the received activity data using an emotion engine to determine the user's emotional state. The emotion engine identifies emotions from, for example, changes in voice tone and heart rate, and based on this, identifies specific emotional events as important events. In this process, it analyzes various emotions such as happiness, sadness, and surprise.

[0733] The server then automatically generates a video with narration, using the identified emotional event as the core of the narration. The narration is created using natural language generation technology, corresponding to the user's emotional state. For example, adding narration that reflects the excitement the user felt when attending a music concert makes the video more emotionally engaging.

[0734] Finally, users can view the generated video on their device. They can also use the video sharing function on their device to share the emotional moment with friends and family. Furthermore, the server uses encryption technology to protect user data and manages it securely. This ensures that users can use the system with peace of mind in a private environment. For example, the excitement of a sporting event a user participated in can be expressed as an emotional climax in the video, allowing users to record their memorable experiences on video.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The device acquires user activity data from wearable devices and smart home sensors. This data includes heart rate, location information, and voice data.

[0738] Step 2:

[0739] The device sends the acquired activity data to the server at regular intervals. The transmitted data is encrypted for security purposes.

[0740] Step 3:

[0741] The server integrates the received activity data and performs preprocessing to maintain data integrity. This organizes the data in a timeline format.

[0742] Step 4:

[0743] The server uses an emotion engine to analyze data and identify the user's emotional state. For example, it uses voice tone analysis and heart rate variability to determine whether the user is happy or stressed.

[0744] Step 5:

[0745] The server selects important events based on the emotional events identified by the emotion engine. This selection takes into account the rise and fall of emotions during the user's activities.

[0746] Step 6:

[0747] The server uses natural language generation technology to create narration corresponding to the selected event. The narration is adjusted to match the user's emotions.

[0748] Step 7:

[0749] The server combines narration with selected scenes to generate a personalized narrated video for the user. The video is then visually edited professionally.

[0750] Step 8:

[0751] The server sends the generated video to the user's device. The video size and format are optimized to match the device's specifications.

[0752] Step 9:

[0753] The device notifies the user when a video arrives and makes it easy to access. The user can then watch the video and enjoy scenes that reflect their emotions.

[0754] Step 10:

[0755] The server securely manages user activity data and generated videos by storing them in an encrypted state in a database and implementing access control as needed. This process ensures user privacy.

[0756] (Example 2)

[0757] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] In modern times, there is a challenge in that there are limited means to accurately record and reflect on people's emotions in their daily lives. In particular, there are no systems available that can grasp a user's individual emotional state in real time and enable rich recollection based on that information.

[0759] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0760] In this invention, the server includes means for acquiring activity information from a user, means for analyzing the acquired activity information to identify an emotional state, and means for automatically generating a video with narration based on the identified emotional state. This enables real-time analysis of emotional states and the provision of compelling content to users based on that information.

[0761] "Activity information" refers to information that users generate in their daily lives, including physiological, geographical, and audio data.

[0762] "Emotional state" refers to data that indicates the emotional state of a user, obtained by analyzing activity information.

[0763] A "narrated video" is video content that incorporates audio commentary provided based on the user's emotional state.

[0764] "Personal information" refers to unique data about a user that requires privacy protection.

[0765] "Natural language generation technology" is a technology in which computer programs generate natural language that humans can understand based on text data.

[0766] This invention is a system that automatically records a user's daily activities, analyzes the user's emotions in real time, and generates a video with narration that reflects the results. This system utilizes a combination of multiple hardware and software components.

[0767] The device acquires activity information using wearable devices and smart home sensing equipment. Specifically, heart rate monitors, GPS location acquisition devices, and voice detection devices are used for data collection. This data is transmitted to the device via Bluetooth or Wi-Fi.

[0768] The device transmits the collected activity information to a server via the internet. This process uses protocols such as HTTP, and data encryption is employed for security purposes.

[0769] The server performs sentiment analysis based on the received activity information. This involves applying speech recognition software based on tone analysis of speech, and statistical methods for heart rate variability analysis. Sentiment analysis uses sentiment analysis libraries for speech data, as well as programming languages ​​such as Python.

[0770] The analyzed emotional state is used in creating narrated videos through natural language generation technology using a generative AI model. Specifically, the generative AI is given prompts such as, "Generate narration that reflects the exhilaration a user feels when enjoying upbeat music," and then generates the narration text.

[0771] The generated narration is incorporated into the video content and output as the final video. This allows for the creation of memorable videos based on the user's specific emotional events.

[0772] Users view videos created by this system through a dedicated application. Video sharing is done using social networking services and messaging functions, and users' personal information is protected by standard encryption technologies such as TLS.

[0773] This allows users to reflect on their emotional state and emotionally relive important moments in their daily lives.

[0774] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0775] Step 1:

[0776] The device acquires activity information from wearable devices and smart home sensors worn by the user. Specifically, it receives heart rate, location information, and audio data via Bluetooth or Wi-Fi. This data is temporarily stored in the device's local memory. Physical sensor data is taken in as input, and each data point is captured in real time based on this data. The output provides instantaneous physiological and environmental information of the user.

[0777] Step 2:

[0778] The terminal sends the activity information obtained in Step 1 to the server using the SSL / TLS encryption protocol. Specifically, the terminal securely uploads the data over the internet using HTTPS. At this time, the input is the physiological and environmental information generated earlier, and the output is the data received by the server in a format that can be parsed.

[0779] Step 3:

[0780] The server receives the transmitted data and performs emotion analysis. Based on previous functions, it analyzes tone from the audio data and identifies the user's emotional state from heart rate variability. Specifically, it applies an emotion analysis algorithm and analyzes the input sensor data to label emotions. The output generates the user's emotional state (e.g., happiness, surprise) numerically or categorically.

[0781] Step 4:

[0782] The server generates narration text using a generative AI model based on the results of the emotion analysis. Specifically, the emotion data from step 3 is input to the generative AI as a prompt, giving instructions such as, "Generate narration that reflects the exhilaration the user feels when enjoying upbeat music." After data processing based on this prompt, the narration text is output using natural language generation technology.

[0783] Step 5:

[0784] The server uses the generated narration to create a video with narration using automated video editing software. Specifically, it incorporates the narration and related visual effects into a video template and creates a video file after editing is complete. Here, the input is the narration text and user activity visual data, and the output is a viewable video file.

[0785] Step 6:

[0786] The user receives and watches the completed narrated video on their device. The device plays this video using its default media player app. Specifically, the user presses the video playback button and can enjoy the emotional narration playing through the video and audio. The input is the completed video, and the output is the user's emotional experience through their sight and hearing.

[0787] (Application Example 2)

[0788] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0789] In today's living environment, there is a need for systems that record individual daily activities and efficiently recognize users' emotions. Furthermore, there is a need for technology that generates and provides videos that reflect users' emotional states so that they can comfortably reflect on their own experiences. However, current technologies often suffer from low accuracy in emotion recognition and insufficient personalization of generated content, and solving these problems is the objective of this invention.

[0790] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0791] In this invention, the server includes means for acquiring activity information from the user, means for analyzing the acquired activity information to identify important events and analyze the emotional state, and means for automatically generating a video with narration added based on the identified events and providing it to the user. This enables personalized feedback that corresponds to the user's emotional state, allowing for a richer reflection on daily activity experiences.

[0792] A "user" is an individual who uses this system or is the subject whose activities are recorded.

[0793] "Activity information" refers to data about the user's daily activities, including heart rate, location information, and voice tone.

[0794] "Important events" are moments or events that are particularly noteworthy from an emotional perspective, as analyzed from user activity data.

[0795] "Emotional state" refers to the emotional response or mental state a user exhibits when performing a particular activity.

[0796] "Methods for automatically generating videos" refers to a function that automatically creates visual content with narration based on identified important events and corresponding emotional states.

[0797] "Feedback" refers to information provided to users that encourages them to re-evaluate and emotionally respond to their own activities and feelings.

[0798] In order to implement this invention, it is essential that the user wears or installs a wearable device or smart home sensor to collect activity information. This allows the device to acquire activity information in real time and transmit it to the server.

[0799] The server uses Python and related libraries (e.g., OpenCV, Librosa) to run the emotion engine and analyze the acquired data. It processes heart rate, location information, and voice data to identify the user's emotional state.

[0800] Based on identified key events, the server generates a video with narration using video processing tools such as FFmpeg. This video is customized for each user, with narration added to take into account the user's emotional state.

[0801] The generated videos are transferred to the device, allowing users to emotionally reflect on their daily activities by watching them. The videos incorporate visually appealing and emotionally engaging elements, enriching the user's life experience.

[0802] Furthermore, the servers encrypt and securely manage data to protect user privacy. This allows users to use the system with peace of mind.

[0803] For example, a user could record a picnic in a park during a holiday, capturing moments of interaction with their pet. The video could include clips of moments that brought a smile to the user's face, allowing them to emotionally relive those memories.

[0804] Examples of prompt statements to input into a generative AI model include the following:

[0805] "Based on the following data, please generate a video with narration that resonates with the user's emotions: Time: 14:30, Heart rate: 72, Voice tone: Lively, Event: Family picnic in the park."

[0806] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0807] Step 1:

[0808] The device acquires activity information from wearable devices worn by the user and smart home sensors installed in the home. Inputs include heart rate, location information, and voice data, which are stored in a temporary database.

[0809] Step 2:

[0810] The device transmits the acquired activity information to the server via the internet. The input is the data collected in step 1, and the output arrives at the server as data packets. The data is appropriately formatted and prepared for transmission.

[0811] Step 3:

[0812] The server analyzes the received activity information using an emotion engine. The input is the data packets sent in step 2, and the emotional state is output by performing data processing, including voice tone analysis and changes in heart rate.

[0813] Step 4:

[0814] The server identifies significant events based on the identified emotional states. The input is the emotional states analyzed in step 3, and the output is organized as a list of significant events. This list includes events of particularly high emotional interest.

[0815] Step 5:

[0816] The server automatically generates narrated videos based on identified events. The input consists of the list from step 4 and the user's emotional state, and the video generation software generates the output video clip. The video is accompanied by narration created by a speech generation engine.

[0817] Step 6:

[0818] The server transfers the generated video to the terminal. The input is the video generated in step 5, and the output is a video file format playable on the terminal. The user can watch this to reflect on their daily life.

[0819] Step 7:

[0820] The server encrypts and securely stores all activity information and generated content to ensure user privacy. Inputs consist of all data that needs to be saved, and outputs are stored in secure storage as encrypted data.

[0821] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0822] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0823] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0824] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0825] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0826] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0827] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0828] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0829] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0830] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0831] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0832] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0833] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0834] 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.

[0835] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0836] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0837] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0838] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0839] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0840] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0841] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0842] The following is further disclosed regarding the embodiments described above.

[0843] (Claim 1)

[0844] A means of obtaining activity data from users,

[0845] A means of analyzing acquired activity data to identify important events,

[0846] A means for automatically generating videos with narration based on identified events,

[0847] A means of providing the generated video to the user,

[0848] Means for securely managing user data,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein the means described above are used to continuously collect user activity data.

[0852] (Claim 3)

[0853] The system according to claim 1, which creates narration based on the identified event using natural language generation technology.

[0854] "Example 1"

[0855] (Claim 1)

[0856] Means of obtaining information based on user activity,

[0857] A means of identifying unique events using the acquired information,

[0858] A means for automatically generating videos with commentary using language generation technology based on identified events,

[0859] A means of presenting the generated video to the user,

[0860] A means of managing user information confidentially,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which continuously collects information regarding user activities using the means described above.

[0864] (Claim 3)

[0865] The system according to claim 1, which utilizes an artificial intelligence model when generating an explanation based on the identified event.

[0866] "Application Example 1"

[0867] (Claim 1)

[0868] A means of obtaining activity information from users,

[0869] A means of analyzing acquired activity information to identify important events,

[0870] A means for automatically generating videos with narration using natural language generation technology based on identified events,

[0871] A means of providing the generated video to the user via communication means,

[0872] A means of securely managing user information through encryption,

[0873] A means to enable local governments to utilize information collected to improve the overall safety and resident satisfaction of the city,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, which continuously collects information on the user's daily activities using the means described above.

[0877] (Claim 3)

[0878] The system according to claim 1, which generates a narration based on the identified event using a natural language generation model, and generates prompt text for the local government to provide the generated video to its residents.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] A means of obtaining activity information from users,

[0882] A means of identifying emotional states by analyzing acquired activity information,

[0883] A means for automatically generating videos with narration based on identified emotional states,

[0884] A means of presenting the generated video to the user,

[0885] Measures to protect users' personal information,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, which continuously collects user activity information using the means described above.

[0889] (Claim 3)

[0890] The system according to claim 1, which creates narration based on the identified emotional state using natural language generation technology.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] A means of obtaining user activity information,

[0894] A means of analyzing acquired activity information to identify important events,

[0895] A means for automatically generating a video with narration added, based on identified events, and analyzing emotional states.

[0896] A means of providing users with generated videos and providing emotional feedback through visual information,

[0897] To securely manage user information, we use encryption technology to protect it,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, which uses the means described above to acquire user activity information in real time and continuously, and proposes activities according to the emotional state.

[0901] (Claim 3)

[0902] The system according to claim 1, which generates narration based on the aforementioned identified events using natural language generation technology, and whose content enriches the individual's emotional experience. [Explanation of Symbols]

[0903] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining activity information from users, A means of analyzing acquired activity information to identify important events, A means for automatically generating videos with narration using natural language generation technology based on identified events, A means of providing the generated video to the user via communication means, A means of securely managing user information through encryption, A means to enable local governments to utilize information collected to improve the overall safety and resident satisfaction of the city, A system that includes this.

2. The system according to claim 1, which continuously collects information on the user's daily activities using the means described above.

3. The system according to claim 1, which generates a narration based on the identified event using a natural language generation model, and generates prompt text for the local government to provide the generated video to residents.

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