system

Abstract

We provide the system. [Solution] A generation device that analyzes digital information related to a specific date obtained from a data collection device and generates text in natural language form, A storage device for saving the generated text in chronological order, A display device that shows saved text to the user, 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

Problems to be Solved by the Invention

[0004] A diary that records daily activities serves a valuable archival function for users, but the manual input work is cumbersome and burdensome especially for busy users. Also, it is difficult to effectively utilize past behavior histories and purchase histories for reordering and memory supplementation. Thus, there is a lack of convenience and efficiency for users to continue using.

Means for Solving the Problems

[0005] This invention provides a system that automatically records user activities by incorporating a generation device that automatically analyzes digital information and generates text in natural language form. Furthermore, it achieves an easily accessible diary format by organizing the text generated by the storage device in chronological order and providing it to the user on a display device. In addition, it achieves the objective of reducing the burden on the user by continuously improving the generation algorithm using a learning device that receives feedback from the user, enabling the creation of a diary that meets the user's needs.

[0006] A "data acquisition device" is a system or device for acquiring information from a user's digital device.

[0007] "Digital information" is a general term for information that is stored and managed electronically, such as image information, activity schedule information, and purchase history information.

[0008] "Analysis" is the process of processing acquired digital information and converting it into structured information.

[0009] "Natural language text" refers to text expressed in natural language that is easily understandable to humans, using technologies such as generative AI.

[0010] A "generation device" is a device or system for automatically generating text based on data.

[0011] A "storage device" is a system or device that stores generated text and other digital data, making it accessible as needed.

[0012] A "display device" is a screen or interface used to visually present stored information to a user.

[0013] A "user" is an individual or group that uses this system to automatically create diaries or view information.

[0014] "Feedback" refers to the evaluation and opinions provided by users regarding the performance and output results of the system.

[0015] "Learning device" refers to a device or function for improving the system's algorithm and performance based on feedback from users.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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. <从 [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. <从 [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference number (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), etc.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a storage with a reference number 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, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system that automatically records a user's daily activities using their digital device and presents them as natural language text. The following describes an embodiment of the system and its operation in detail.

[0038] First, the user's device functions as a data collection device. Based on the user's consent, digital information such as images taken with the camera, appointments registered in the calendar, and purchase history from online shopping are collected on a daily basis. This information includes specific details about the user's activities and lifestyle.

[0039] The device transfers the collected information to the server. The server analyzes this information and uses generative AI to generate natural language text. Specifically, it identifies locations and events through image analysis, associates them with schedule information, and describes the events that occurred on that day. For example, if a photo of a museum taken by the user matches their planned visit for that day, the server will generate text such as, "Today I visited a museum and viewed various works of art."

[0040] The generated text is sent from the server to the user's terminal and saved chronologically by a storage device. Users can view their saved diaries through a dedicated application, making it easy to review past events. Furthermore, the terminal's display provides information in a visually appropriate format for the user.

[0041] Furthermore, users can provide feedback on the content of their diaries, and this feedback is collected by the server's learning system. The learning system uses the provided feedback to improve the algorithm of the generating AI, enabling the automatic generation of more personalized diaries. In this way, the system continues to evolve according to the user's needs.

[0042] As a concrete example, when a user visits a tourist destination, if they have taken numerous photos with their device and their travel plans are registered in their calendar, the server integrates this data and automatically creates a detailed diary entry including events and impressions from the tourist destination. In this way, the user can easily record and manage the activities they have undertaken. This invention provides a new means of streamlining the recording of daily life and supplementing the user's memory.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device collects image data from the camera roll with the user's consent. It also obtains schedule information from the calendar app and purchase history from browsers and shopping apps. This data is related to the user's daily activities.

[0046] Step 2:

[0047] The device organizes the collected data and groups information belonging to the same timeframe. This process includes associations based on date, time, and geographical location data.

[0048] Step 3:

[0049] The organized data is uploaded to the server. During this process, the data is encrypted, and communication is conducted using a secure protocol. Ensuring user privacy is crucial.

[0050] Step 4:

[0051] The server analyzes the received data and generates diary entries using a generative AI. It performs image recognition to identify places and people in photos, combines this with scheduled information, and describes the day's events in natural language.

[0052] Step 5:

[0053] The generated text is organized and structured by the server. This process also includes editing to ensure a natural flow of text. Personalized messages relevant to the user may be incorporated as needed.

[0054] Step 6:

[0055] The server sends the completed diary entry back to the terminal. The terminal receives it and saves it in an application that is easily accessible to the user. This saving is done chronologically, making it easy to refer to later.

[0056] Step 7:

[0057] Users can view the generated diary entries via their device. They can edit or comment on specific entries. Users can also provide feedback, which is used to improve the generation AI's algorithm.

[0058] Step 8:

[0059] The server analyzes user feedback and updates the AI ​​model to improve the accuracy and user-friendliness of future text generation. This process is continuous, improving both the system's precision and the user experience.

[0060] (Example 1)

[0061] 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."

[0062] In modern society, meticulously recording an individual's daily life is time-consuming and laborious, making it difficult. This can lead to important events and activities being lost from memory. Furthermore, there is a need to organize and display the collected information in a user-friendly format. Therefore, an effective and efficient activity recording and management system using digital devices is necessary.

[0063] 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.

[0064] In this invention, the server includes a process means for analyzing digital information collected by a data acquisition means and generating natural language text using a generation AI model; a data recording means for recording the generated text in chronological order; and a display means for presenting the recorded text to the user via an information output means. This makes it possible to automatically record and visually confirm the user's daily activities in detail.

[0065] "Data acquisition means" refers to a device or process that has the function of collecting a user's digital information.

[0066] "Digital information" refers to electronic data related to an individual's activities and lifestyle, such as visual information, schedule information, and purchase history information.

[0067] A "generative AI model" is an artificial intelligence model that generates natural language text based on input digital information.

[0068] "Process means" refers to a function or step for analyzing digital information and generating natural language text using a generative AI model.

[0069] "Data recording means" refers to a function or device for recording and saving generated text in chronological order.

[0070] "Information output means" refers to a device or method for visually presenting recorded information to a user.

[0071] "Display means" refers to a device or system used by a user to visually confirm recorded text.

[0072] A "learning tool" is a function or system that uses user feedback to improve the text generation algorithm.

[0073] This invention is a system for efficiently recording and managing a user's daily activities. This system consists of a user's terminal, a server, and a dedicated application.

[0074] The terminal functions as a data acquisition tool, collecting digital information about the user's daily activities. This digital information includes visual information captured by the camera, schedule information registered in schedule management software, and purchase history information on online platforms. The terminal transfers this information to the server at regular intervals.

[0075] The server has processing capabilities that analyze the received digital information and use a generative AI model to convert it into natural language. This process includes steps such as recognizing specific locations or events through image analysis and associating them with scheduled information. For example, if a user's photos taken at a tourist destination are linked to their schedule for that day, a sentence like "The user visited a tourist destination, took many photos, and had a fun day" might be generated. Open-source natural language generation frameworks are sometimes used as the generative AI model in this process.

[0076] The server sorts the generated text chronologically using a data recording device and sends it to the user's terminal. The terminal visually presents this information through a dedicated application. The user can easily review and recall past events using the dedicated application. An example of a prompt message is the instruction, "Summarize your day based on your experiences."

[0077] Furthermore, users can provide feedback on the generated text, including opinions and suggestions for improvement. The server collects this feedback and adaptively improves the AI ​​model through learning mechanisms. This allows the generated text to gradually adapt to the user's individual preferences and style, resulting in a more accurate and personalized diary.

[0078] This invention simplifies users' life record-keeping and enables accurate management and reflection of memories.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user's device collects digital information.

[0082] Input: Digital information related to the user's daily activities (e.g., photos taken with a camera, schedule information, purchase history).

[0083] Operation: The device collects digital information from sensors such as the camera and schedule management apps.

[0084] Output: Various digital data stored on the device.

[0085] Step 2:

[0086] The terminal transfers the collected digital information to the server.

[0087] Input: Digital data stored on the device.

[0088] Operation: The device encrypts the collected data and transfers it to the server via the network.

[0089] Output: Reception of digital information on the server.

[0090] Step 3:

[0091] The server analyzes the information it receives.

[0092] Input: Digital information transferred to the server.

[0093] Operation: The server uses image analysis and natural language processing techniques to recognize objects within images and interpret schedules.

[0094] Output: Analyzed location and event information.

[0095] Step 4:

[0096] The server generates natural language text using a generation AI model.

[0097] Input: Analysis results (location information and event information).

[0098] Operation: The generation AI model generates text based on the analysis results and the prompt text. An example of a prompt text is, "Summarize your day based on your user experience."

[0099] Output: The generated text in natural language format.

[0100] Step 5:

[0101] The server sends the generated text to the terminal, and the terminal saves the data.

[0102] Input: A generated text in natural language format.

[0103] Operation: The server sends text to the terminal, and the terminal saves it to the database in chronological order.

[0104] Output: Text data saved on the device, sorted by date and time.

[0105] Step 6:

[0106] Users can view their diaries and provide feedback through a dedicated application.

[0107] Input: Text data stored on the device.

[0108] Operation: Users use the application to view their diary entries and send feedback about the content.

[0109] Output: User feedback sent to the server.

[0110] Step 7:

[0111] The server improves the AI ​​model based on the feedback.

[0112] Input: Feedback provided by users.

[0113] Operation: The server's learning device analyzes the feedback and adjusts and improves the algorithm of the generated AI model.

[0114] Output: More accurate text generation using an improved AI model.

[0115] (Application Example 1)

[0116] 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."

[0117] With conventional technology, it has been difficult to easily record everyday events and experiences, and to automatically deliver personalized content based on those records. Furthermore, there is a demand not only for simple recording, but also for personalized information tailored to the user's interests. Therefore, there is a need to efficiently and effectively record users' daily activities and automatically generate and deliver personalized content using those records.

[0118] 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.

[0119] In this invention, the server includes means for analyzing digital information acquired from a data collection device and generating text in natural language; means for storing the generated text in chronological order; means for displaying the stored text to the user; and means for automatically delivering content based on the user's experience. This makes it possible for users not only to easily record everyday events but also to receive personalized content based on those records.

[0120] A "data acquisition device" is hardware or software used to acquire digital information about a user's daily activities.

[0121] "Analysis" refers to processing collected digital information and extracting meanings and patterns that are relevant to a specific purpose.

[0122] A "natural language text" is a text composed of language that humans can understand, and whose generated content is described using everyday language expressions.

[0123] A "generation device" is a device or system for generating natural language text based on analyzed information.

[0124] A "storage device" is a device or system that stores generated documents in a specified order and makes them available for retrieval as needed.

[0125] A "display device" is a device used to visually present stored text to a user.

[0126] A "content distribution device" is a device or system for delivering content generated based on the user's experience to the user.

[0127] "Feedback" refers to the opinions and reactions provided by users, and is information that can be used to improve the system.

[0128] A "learning device" is a device or system that improves the algorithm of a generation device based on feedback, enabling the generation of natural language text that is more suitable for the user.

[0129] "Digital information" refers to electronic data related to a user's daily activities, such as image information, activity plan information, and purchase history information.

[0130] The system for implementing this invention first uses a smartphone or smart glasses as the user terminal. These devices function as data collection devices and collect digital information about the user's daily activities. Specifically, they acquire image information, activity schedule information, purchase history information, etc. Only data that the user has given permission for is collected, thus protecting the privacy of the data.

[0131] The collected data is sent to a cloud server. The server analyzes the collected data and processes it to associate different pieces of information. Image recognition technology is used in this analysis to recognize specific locations and events and link them to calendar information. Based on the analysis results, a generative AI model generates sentences in natural language. For example, if a photo taken by the user at their travel destination matches their hiking plan for that day, the sentence "Today I enjoyed hiking while exploring a beautiful lake at my travel destination" will be generated.

[0132] The generated text is sent back to the user's device from the server and displayed chronologically by a dedicated application. Through this application, users can review their diary entries and reflect on their experiences visualized as content. Furthermore, by receiving user feedback, the server's learning system improves the AI's algorithm, enabling the generation of more personalized content.

[0133] For example, if a user takes photos of activities in a nature park during a weekend trip, and those activities are scheduled in their calendar, the server can automatically generate content such as "A wonderful holiday in the nature park, enjoyed a picnic and explored new hiking trails," and deliver it in blog format. In this way, users can easily share their personal experiences over the internet.

[0134] An example of a prompt message would be: "Generate a natural-sounding travelogue based on the user's camera images, the name of the place visited ('Nature Park'), and the activity ('Hiking')."

[0135] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0136] Step 1:

[0137] Users collect data on their daily activities using smartphones or smart glasses. Inputs include images taken by the user, calendar activity schedules, and purchase history. This data is temporarily stored on the device to prepare for the next processing step.

[0138] Step 2:

[0139] The device sends data authorized by the user to a cloud server. The input is collected digital information, and the output is raw data transferred to the server. During the data transfer process, the data is encrypted to protect privacy.

[0140] Step 3:

[0141] The server analyzes the received digital information. The input is encrypted digital data, and the output is intermediate data representing the analysis results. This analysis uses image recognition algorithms to identify specific objects and scenes and associate them with planned action information.

[0142] Step 4:

[0143] The server uses a generative AI model to generate natural language text from the analysis results. The input is the analyzed intermediate data, and the output is the constructed natural language text. In this process, prompt sentences are used to instruct the server to generate text to represent a specific situation.

[0144] Step 5:

[0145] The server sends the generated text to the terminal. The input is the generated text in natural language format, and the output is sent to the user's terminal as a message. The transmitted data is displayed to the user by a dedicated application running on the terminal.

[0146] Step 6:

[0147] Users use a dedicated application on their device to review the generated natural language text. Here, the input is text received from the server, and the output is displayed in a format that the user can visually review. This application allows users to reflect on their experiences.

[0148] Step 7:

[0149] Users provide feedback on the generated text. The input consists of user opinions and comments, and the output is processed by the server's learning system. This feedback is used to improve the algorithm, enabling more personalized content generation in the future.

[0150] 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.

[0151] This invention provides a system that automatically records a user's daily activities and further analyzes the user's emotions during those activities, presenting the results as text in natural language. The embodiments and operation of this system are described in detail below.

[0152] The system first collects digital information through the user's device. The collected information includes image data, activity schedules, and purchase history, which allows the system to obtain specific data about the user's daily life.

[0153] When data is transferred from the terminal to the server, it is encrypted with security and privacy in mind. The server is equipped with a generative AI and an emotion engine to analyze this data. In the analysis process, image recognition technology is used to detect the user's facial expressions and surrounding environment from images. Combined with planned activities and purchase history, it is possible to infer the user's emotions. For example, if a photo in which the user has a happy expression matches an appointment with a friend, the emotion will be specifically reflected in the text, such as "Enjoyed a conversation with a friend."

[0154] Next, the generation AI automatically generates natural language text with appropriate tone and content based on the analyzed data and emotional information. The generated text is organized chronologically on the server and then sent back to the user's device.

[0155] Users can view these automatically generated diary entries through a dedicated application. A key feature is that emotional information is included in the text, allowing users to create richer diaries that go beyond mere records of events and encompass the emotional state of the time.

[0156] Furthermore, user feedback is provided to the server to improve the algorithms of the generation AI and emotion engine. This enables the generation of more user-friendly diaries, and the system evolves according to user needs.

[0157] As a concrete example of this system, consider a scenario where a user enjoys a picnic with their family on a holiday. From digital information such as photos taken with the device, the picnic plan, and purchased food, the server can generate a diary entry in the form of, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park," and deliver it to the user.

[0158] In this way, the present invention provides a new system that enriches the recording of users' daily lives and enables them to reflect on them.

[0159] The following describes the processing flow.

[0160] Step 1:

[0161] With the user's permission, the device collects digital information such as image data from the camera roll, calendar events, and online purchase history. This collected information reflects the user's daily activities.

[0162] Step 2:

[0163] The device organizes the collected data and groups related information. For example, it forms a relevant dataset by grouping photos taken on the same date or at similar times with appointments scheduled for those dates.

[0164] Step 3:

[0165] The device transfers the organized data to the server. During this process, the data is encrypted, and secure communication protocols are used to protect user privacy.

[0166] Step 4:

[0167] The server analyzes the received data. First, it uses image recognition technology to analyze facial expressions and situations in photographs, and then, in conjunction with planned activities and purchase history, it infers the user's emotions. This analysis helps to understand what emotions the user was feeling at a particular time or event.

[0168] Step 5:

[0169] The emotion engine runs on the server and adjusts the tone and content of the text based on the inferred emotions. If the user's emotions are positive, it uses a bright tone in the text; if they are negative, it reflects that.

[0170] Step 6:

[0171] The generative AI generates natural language text based on the results of sentiment analysis. This text includes details of activities and emotional aspects of a specific day, forming a more personal diary.

[0172] Step 7:

[0173] The server organizes the generated diary entries in chronological order and sends them to the terminal. The terminal receives these entries and saves them within a dedicated application, allowing the user to easily access them later.

[0174] Step 8:

[0175] Users view diaries generated through an application on their device. They can add comments and make corrections to the diary within the application, and also send feedback to the server. This feedback is used to improve the diary generation process.

[0176] (Example 2)

[0177] 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".

[0178] Conventional recording systems often resulted in monotonous records of users' daily activities that failed to consider emotions, leading to insufficient informational value in reflecting on users' lives. Furthermore, the generated records frequently did not match the user's actual emotions or activities, highlighting the need for methods to improve their quality.

[0179] 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.

[0180] In this invention, the server includes an analysis means for analyzing data related to daily activities acquired through a data collection means and inferring the user's emotions; a generation means for generating natural language text based on the inferred emotion data and activity data; and a storage means for organizing and saving the generated text in chronological order. This makes it possible to richly record the user's daily activities from an emotional perspective and provide the user with high-quality information.

[0181] "Data collection means" refers to a device or program for efficiently and safely acquiring information about a user's daily activities.

[0182] "Analysis means" refers to a device or program that uses collected data to infer specific emotions or activities of a user, thereby enabling a deeper understanding.

[0183] A "generation means" is a device or program that effectively generates natural language text based on analyzed data and provides information in a user-friendly manner.

[0184] "Storage means" refers to a device or program for appropriately organizing generated text and saving data as needed.

[0185] "Display means" refers to a device or program that visually presents stored information to the user and makes it easily accessible.

[0186] "Learning tools" are algorithms and programs that utilize user feedback to improve the system's performance and the quality of its generated content.

[0187] "Image data" refers to information used to visually record user activity, and is digital data that includes images of faces, objects, and other objects.

[0188] "Schedule data" refers to information that reflects the user's daily schedule and plans.

[0189] "Recorded data" refers to information about a user's daily activities, including their behavioral history and purchase history.

[0190] This invention is a system that records a user's daily activities in detail and provides them in an emotionally rich written format. Embodiments of this system are described below.

[0191] First, users collect digital data related to their daily lives, such as image information, activity schedules, and purchase history, through their devices. This data is automatically encrypted as a security measure and stored securely.

[0192] Next, the device sends this encrypted data to the server. The data is transferred using a secure communication protocol, and privacy is strictly protected.

[0193] The server receives the transmitted data and performs analysis using a generative AI and an emotion engine. Specifically, it uses image recognition software to identify the user's facial expressions from images and infers the user's emotions and behavioral patterns based on their planned activities and purchase history. At the core of this system is the generative AI model, and an example of its prompt message is, "Analyze the user's emotions from their image and planned activities, and generate a reflection on their day."

[0194] Next, the generation AI generates text in natural language format based on the analyzed data. This text is then organized chronologically and sent to the user's device.

[0195] The user's device displays received messages through a dedicated application, allowing the user to easily access and review the content. For example, a user can use photos and schedule information to specifically record details of a picnic they had with their family on a holiday, such as, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park."

[0196] Furthermore, user feedback is collected by the server. This feedback is used to improve the accuracy of the generative AI and emotion engine, and the system evolves daily in response to user needs.

[0197] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0198] Step 1:

[0199] Users input image information, activity schedule information, and purchase history information using their own devices. This digital data is temporarily stored on the device and encrypted in real time for security. The input information is related to the user's daily activities and serves as basic data necessary for subsequent analysis. As output, the encrypted data is stored on the device.

[0200] Step 2:

[0201] The device transfers encrypted data to the server. This transmission uses a secure communication protocol, thus maintaining privacy and security. The input is encrypted data on the device, and the output after transmission is stored in data storage on the server.

[0202] Step 3:

[0203] The server receives the transmitted data and first decodes it to restore it to its original state. Next, it performs analysis using a generative AI model and an emotion analysis engine. At this stage, image recognition technology is used to identify the user's facial expressions and situation from the image information, and this is combined with planned actions and purchase history to infer the user's emotions. The input is the decoded data, and the output is the analyzed data inferring the user's emotions.

[0204] Step 4:

[0205] Based on the analyzed data, the server uses a generative AI model to generate natural language text. An example of a prompt used here is, "Analyze the user's emotions from their image and planned activities, and generate a text reflecting on their day." Based on this prompt, the analyzed data is used as input, and the output is a text that incorporates the user's activities and emotions.

[0206] Step 5:

[0207] The server organizes the generated text in chronological order and prepares it for transmission to the user's terminal. The organized text is formatted to allow for easy searching and visual review by the user. The input is the generated text, and the output is the organized text, which is temporarily stored on the server.

[0208] Step 6:

[0209] The terminal receives organized text sent from the server and displays it to the user using a dedicated application. Through this application, the user can easily review each scene and the emotional situations within it. The input is text sent from the server, and the output is displayed information in a format that the user can visually confirm.

[0210] (Application Example 2)

[0211] 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".

[0212] In modern commercial facilities, there is a need to offer product suggestions tailored to the individual needs and emotions of each customer in order to improve their satisfaction. However, with current technology, it has been difficult to analyze the emotions of individual customers and propose appropriate products and services based on them. A means to solve this problem and improve the customer experience is needed.

[0213] 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.

[0214] In this invention, the server includes a generation means for analyzing data acquired from a data collection means and generating sentences in natural language form; a storage means for saving the generated sentences in chronological order; a display means for displaying the saved sentences to the user; an emotion analysis means for analyzing the user's behavior and inferring their emotions; and a suggestion means for suggesting products and services based on the inferred emotions. This makes it possible to suggest products that are tailored to the user's emotions.

[0215] "Data collection means" refers to methods for acquiring information from users' terminals or other devices and using it for analysis.

[0216] A "generation means" is a means that has the function of creating a text in natural language form based on acquired data.

[0217] A "storage method" is a means for storing generated text in chronological order.

[0218] "Display means" refers to a means of presenting stored text or data in a way that allows users to visually confirm it.

[0219] "Emotional analysis methods" are means of performing analysis to infer the emotions of users from collected data.

[0220] A "proposal method" is a means of suggesting products and services optimized for the user based on analyzed emotional information.

[0221] To implement this invention, first, the terminal functions as a data collection means and acquires data about the user's activities. This data includes image data, schedule data, and transaction history data. This data is appropriately encrypted and then transferred to a server.

[0222] The server is equipped with a generation mechanism for analyzing the received data. Here, it uses the image recognition library OpenCV to recognize the user's facial expressions from images, combines them with behavioral data and transaction history, and analyzes emotions. Emotion analysis is used for the analysis, and an emotion engine is utilized to infer the user's emotions.

[0223] Based on the analyzed emotional information, the server executes a suggestion mechanism to propose products and services. Using a generative AI model, it automatically generates appropriate natural language sentences and notifies the user. For example, based on the user's facial expressions and purchase history, it can generate a prompt sentence such as, "The customer smiled while sampling product XYZ. What kind of offer would you like to propose?"

[0224] Furthermore, the generated text is organized chronologically using the server's storage system. Users can view this information through the display system and provide feedback. User feedback is used to improve the algorithm through the system's learning mechanism. As a result, the system continuously improves the accuracy of user sentiment analysis and evolves to provide more refined product recommendations.

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The terminal acquires user image data, activity plan data, and transaction history data as data collection tools. This data is temporarily stored on the terminal and transferred to the server via a communication module. The input is raw data related to the user's daily activities, and the output is encrypted data sent to the server.

[0228] Step 2:

[0229] The server decrypts the received encrypted data and analyzes the user's facial expressions from the image data using an image recognition library (e.g., OpenCV). The input is encrypted user data, and the output is analyzed information about the user's facial expressions. The server evaluates the analyzed facial expressions using emotion analysis tools and generates an estimated emotion.

[0230] Step 3:

[0231] The server uses a generative AI model to generate natural language-based suggestion sentences based on inferred sentiment information. An example of this prompt sentence is: "The customer smiled while sampling product XYZ. What kind of offer would you suggest?" The input is inferred sentiment data, and the output is a suggestion sentence for the user.

[0232] Step 4:

[0233] The server stores the generated suggestion text in a database in chronological order using a storage mechanism and notifies the user's terminal via a display mechanism. The input is the generated text, and the output is the notification message received on the user's terminal.

[0234] Step 5:

[0235] Users select products and services based on suggestions received through their devices and send their feedback to the server. The input is the user's feedback on the suggestions, and the output is the feedback data used for improvement.

[0236] Step 6:

[0237] The server uses user feedback as a learning tool to adjust the parameters of its sentence generation algorithm and sentiment analysis tools. This improves the accuracy of sentiment evaluation and suggestions in subsequent uses. The input is feedback data, and the output is the improved algorithm and updated sentiment analysis model.

[0238] 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.

[0239] Data generation model 58 is a 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 those described above. 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 shown 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.

[0240] 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.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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".

[0254] This invention provides a system that automatically records a user's daily activities using their digital device and presents them as natural language text. The following describes an embodiment of the system and its operation in detail.

[0255] First, the user's device functions as a data collection device. Based on the user's consent, digital information such as images taken with the camera, appointments registered in the calendar, and purchase history from online shopping are collected on a daily basis. This information includes specific details about the user's activities and lifestyle.

[0256] The device transfers the collected information to the server. The server analyzes this information and uses generative AI to generate natural language text. Specifically, it identifies locations and events through image analysis, associates them with schedule information, and describes the events that occurred on that day. For example, if a photo of a museum taken by the user matches their planned visit for that day, the server will generate text such as, "Today I visited a museum and viewed various works of art."

[0257] The generated text is sent from the server to the user's terminal and saved chronologically by a storage device. Users can view their saved diaries through a dedicated application, making it easy to review past events. Furthermore, the terminal's display provides information in a visually appropriate format for the user.

[0258] Furthermore, users can provide feedback on the content of their diaries, and this feedback is collected by the server's learning system. The learning system uses the provided feedback to improve the algorithm of the generating AI, enabling the automatic generation of more personalized diaries. In this way, the system continues to evolve according to the user's needs.

[0259] As a concrete example, when a user visits a tourist destination, if they have taken numerous photos with their device and their travel plans are registered in their calendar, the server integrates this data and automatically creates a detailed diary entry including events and impressions from the tourist destination. In this way, the user can easily record and manage the activities they have undertaken. This invention provides a new means of streamlining the recording of daily life and supplementing the user's memory.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] The device collects image data from the camera roll with the user's consent. It also obtains schedule information from the calendar app and purchase history from browsers and shopping apps. This data is related to the user's daily activities.

[0263] Step 2:

[0264] The device organizes the collected data and groups information belonging to the same timeframe. This process includes associations based on date, time, and geographical location data.

[0265] Step 3:

[0266] The organized data is uploaded to the server. During this process, the data is encrypted, and communication is conducted using a secure protocol. Ensuring user privacy is crucial.

[0267] Step 4:

[0268] The server analyzes the received data and generates diary entries using a generative AI. It performs image recognition to identify places and people in photos, combines this with scheduled information, and describes the day's events in natural language.

[0269] Step 5:

[0270] The generated text is organized and structured by the server. This process also includes editing to ensure a natural flow of text. Personalized messages relevant to the user may be incorporated as needed.

[0271] Step 6:

[0272] The server sends the completed diary entry back to the terminal. The terminal receives it and saves it in an application that is easily accessible to the user. This saving is done chronologically, making it easy to refer to later.

[0273] Step 7:

[0274] Users can view the generated diary entries via their device. They can edit or comment on specific entries. Users can also provide feedback, which is used to improve the generation AI's algorithm.

[0275] Step 8:

[0276] The server analyzes user feedback and updates the AI ​​model to improve the accuracy and user-friendliness of future text generation. This process is continuous, improving both the system's precision and the user experience.

[0277] (Example 1)

[0278] 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."

[0279] In modern society, meticulously recording an individual's daily life is time-consuming and laborious, making it difficult. This can lead to important events and activities being lost from memory. Furthermore, there is a need to organize and display the collected information in a user-friendly format. Therefore, an effective and efficient activity recording and management system using digital devices is necessary.

[0280] 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.

[0281] In this invention, the server includes a process means for analyzing digital information collected by a data acquisition means and generating natural language text using a generation AI model; a data recording means for recording the generated text in chronological order; and a display means for presenting the recorded text to the user via an information output means. This makes it possible to automatically record and visually confirm the user's daily activities in detail.

[0282] The "data acquisition means" is a device or process having a function of collecting digital information of a user.

[0283] The "digital information" is electronic data related to an individual's activities and lifestyle, such as visual information, schedule information, purchase history information, etc.

[0284] The "generative AI model" is a model of artificial intelligence that generates a sentence in natural language form based on the input digital information.

[0285] The "process means" is a function or step for analyzing digital information and generating a sentence in natural language form using the generative AI model.

[0286] The "data recording means" is a function or device for recording and storing the generated sentences in chronological order.

[0287] The "information output means" is a device or method for visually presenting the recorded information to the user.

[0288] The "display means" is a device or system used for the user to visually confirm the recorded sentences.

[0289] The "learning means" is a function or system for improving the sentence generation algorithm using feedback from the user. [[ID=3&]]

[0290] The present invention is a system for efficiently recording and managing a user's daily activities. This system is composed of using a user's terminal, a server, and a dedicated application.

[0291] The terminal functions as a data acquisition tool, collecting digital information about the user's daily activities. This digital information includes visual information captured by the camera, schedule information registered in schedule management software, and purchase history information on online platforms. The terminal transfers this information to the server at regular intervals.

[0292] The server has processing capabilities that analyze the received digital information and use a generative AI model to convert it into natural language. This process includes steps such as recognizing specific locations or events through image analysis and associating them with scheduled information. For example, if a user's photos taken at a tourist destination are linked to their schedule for that day, a sentence like "The user visited a tourist destination, took many photos, and had a fun day" might be generated. Open-source natural language generation frameworks are sometimes used as the generative AI model in this process.

[0293] The server sorts the generated text chronologically using a data recording device and sends it to the user's terminal. The terminal visually presents this information through a dedicated application. The user can easily review and recall past events using the dedicated application. An example of a prompt message is the instruction, "Summarize your day based on your experiences."

[0294] Furthermore, users can provide feedback on the generated text, including opinions and suggestions for improvement. The server collects this feedback and adaptively improves the AI ​​model through learning mechanisms. This allows the generated text to gradually adapt to the user's individual preferences and style, resulting in a more accurate and personalized diary.

[0295] This invention simplifies users' life record-keeping and enables accurate management and reflection of memories.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The user's terminal collects digital information.

[0299] Input: Digital information related to the user's daily activities (e.g., photos taken by the camera, schedule information, purchase history).

[0300] Action: The terminal collects digital information from sensors such as cameras and schedule management apps.

[0301] Output: Various digital data stored in the terminal.

[0302] Step 2:

[0303] The terminal transfers the collected digital information to the server.

[0304] Input: Digital data stored in the terminal.

[0305] Action: The terminal encrypts the collected data and transfers it to the server via the network.

[0306] Output: Receipt of digital information at the server.

[0307] Step 3:

[0308] The server analyzes the received information.

[0309] Input: Digital information transferred to the server.

[0310] Action: The server uses image analysis and natural language processing technologies to perform object recognition in images and interpretation of schedules.

[0311] Output: Analyzed location information and event information.

[0312] Step 4: <000098 The server generates natural language text using a generation AI model.

[0314] Input: Analysis results (location information and event information).

[0315] Operation: The generation AI model generates text based on the analysis results and the prompt text. An example of a prompt text is, "Summarize your day based on your user experience."

[0316] Output: The generated text in natural language format.

[0317] Step 5:

[0318] The server sends the generated text to the terminal, and the terminal saves the data.

[0319] Input: A generated text in natural language format.

[0320] Operation: The server sends text to the terminal, and the terminal saves it to the database in chronological order.

[0321] Output: Text data saved on the device, sorted by date and time.

[0322] Step 6:

[0323] Users can view their diaries and provide feedback through a dedicated application.

[0324] Input: Text data stored on the device.

[0325] Operation: Users use the application to view their diary entries and send feedback about the content.

[0326] Output: User feedback sent to the server.

[0327] Step 7:

[0328] The server improves the AI ​​model based on the feedback.

[0329] Input: Feedback provided by users.

[0330] Operation: The server's learning device analyzes the feedback and adjusts and improves the algorithm of the generated AI model.

[0331] Output: More accurate text generation using an improved AI model.

[0332] (Application Example 1)

[0333] 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."

[0334] With conventional technology, it has been difficult to easily record everyday events and experiences, and to automatically deliver personalized content based on those records. Furthermore, there is a demand not only for simple recording, but also for personalized information tailored to the user's interests. Therefore, there is a need to efficiently and effectively record users' daily activities and automatically generate and deliver personalized content using those records.

[0335] 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.

[0336] In this invention, the server includes means for analyzing digital information acquired from a data collection device and generating text in natural language; means for storing the generated text in chronological order; means for displaying the stored text to the user; and means for automatically delivering content based on the user's experience. This makes it possible for users not only to easily record everyday events but also to receive personalized content based on those records.

[0337] A "data acquisition device" is hardware or software used to acquire digital information about a user's daily activities.

[0338] "Analysis" refers to processing collected digital information and extracting meanings and patterns that are relevant to a specific purpose.

[0339] A "natural language text" is a text composed of language that humans can understand, and whose generated content is described using everyday language expressions.

[0340] A "generation device" is a device or system for generating natural language text based on analyzed information.

[0341] A "storage device" is a device or system that stores generated documents in a specified order and makes them available for retrieval as needed.

[0342] A "display device" is a device used to visually present stored text to a user.

[0343] A "content distribution device" is a device or system for delivering content generated based on the user's experience to the user.

[0344] "Feedback" refers to the opinions and reactions provided by users, and is information that can be used to improve the system.

[0345] A "learning device" is a device or system that improves the algorithm of a generation device based on feedback, enabling the generation of natural language text that is more suitable for the user.

[0346] "Digital information" refers to electronic data related to a user's daily activities, such as image information, activity plan information, and purchase history information.

[0347] The system for implementing this invention first uses a smartphone or smart glasses as the user terminal. These devices function as data collection devices and collect digital information about the user's daily activities. Specifically, they acquire image information, activity schedule information, purchase history information, etc. Only data that the user has given permission for is collected, thus protecting the privacy of the data.

[0348] The collected data is sent to a cloud server. The server analyzes the collected data and processes it to associate different pieces of information. Image recognition technology is used in this analysis to recognize specific locations and events and link them to calendar information. Based on the analysis results, a generative AI model generates sentences in natural language. For example, if a photo taken by the user at their travel destination matches their hiking plan for that day, the sentence "Today I enjoyed hiking while exploring a beautiful lake at my travel destination" will be generated.

[0349] The generated text is sent back to the user's device from the server and displayed chronologically by a dedicated application. Through this application, users can review their diary entries and reflect on their experiences visualized as content. Furthermore, by receiving user feedback, the server's learning system improves the AI's algorithm, enabling the generation of more personalized content.

[0350] For example, if a user takes photos of activities in a nature park during a weekend trip, and those activities are scheduled in their calendar, the server can automatically generate content such as "A wonderful holiday in the nature park, enjoyed a picnic and explored new hiking trails," and deliver it in blog format. In this way, users can easily share their personal experiences over the internet.

[0351] An example of a prompt message would be: "Generate a natural-sounding travelogue based on the user's camera images, the name of the place visited ('Nature Park'), and the activity ('Hiking')."

[0352] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0353] Step 1:

[0354] Users collect data on their daily activities using smartphones or smart glasses. Inputs include images taken by the user, calendar activity schedules, and purchase history. This data is temporarily stored on the device to prepare for the next processing step.

[0355] Step 2:

[0356] The device sends data authorized by the user to a cloud server. The input is collected digital information, and the output is raw data transferred to the server. During the data transfer process, the data is encrypted to protect privacy.

[0357] Step 3:

[0358] The server analyzes the received digital information. The input is encrypted digital data, and the output is intermediate data representing the analysis results. This analysis uses image recognition algorithms to identify specific objects and scenes and associate them with planned action information.

[0359] Step 4:

[0360] The server uses a generative AI model to generate natural language text from the analysis results. The input is the analyzed intermediate data, and the output is the constructed natural language text. In this process, prompt sentences are used to instruct the server to generate text to represent a specific situation.

[0361] Step 5:

[0362] The server sends the generated text to the terminal. The input is the generated text in natural language format, and the output is sent to the user's terminal as a message. The transmitted data is displayed to the user by a dedicated application running on the terminal.

[0363] Step 6:

[0364] Users use a dedicated application on their device to review the generated natural language text. Here, the input is text received from the server, and the output is displayed in a format that the user can visually review. This application allows users to reflect on their experiences.

[0365] Step 7:

[0366] Users provide feedback on the generated text. The input consists of user opinions and comments, and the output is processed by the server's learning system. This feedback is used to improve the algorithm, enabling more personalized content generation in the future.

[0367] 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.

[0368] This invention provides a system that automatically records a user's daily activities and further analyzes the user's emotions during those activities, presenting the results as text in natural language. The embodiments and operation of this system are described in detail below.

[0369] The system first collects digital information through the user's device. The collected information includes image data, activity schedules, and purchase history, which allows the system to obtain specific data about the user's daily life.

[0370] When data is transferred from the terminal to the server, it is encrypted with security and privacy in mind. The server is equipped with a generative AI and an emotion engine to analyze this data. In the analysis process, image recognition technology is used to detect the user's facial expressions and surrounding environment from images. Combined with planned activities and purchase history, it is possible to infer the user's emotions. For example, if a photo in which the user has a happy expression matches an appointment with a friend, the emotion will be specifically reflected in the text, such as "Enjoyed a conversation with a friend."

[0371] Next, the generation AI automatically generates natural language text with appropriate tone and content based on the analyzed data and emotional information. The generated text is organized chronologically on the server and then sent back to the user's device.

[0372] Users can view these automatically generated diary entries through a dedicated application. A key feature is that emotional information is included in the text, allowing users to create richer diaries that go beyond mere records of events and encompass the emotional state of the time.

[0373] Furthermore, user feedback is provided to the server to improve the algorithms of the generation AI and emotion engine. This enables the generation of more user-friendly diaries, and the system evolves according to user needs.

[0374] As a concrete example of this system, consider a scenario where a user enjoys a picnic with their family on a holiday. From digital information such as photos taken with the device, the picnic plan, and purchased food, the server can generate a diary entry in the form of, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park," and deliver it to the user.

[0375] In this way, the present invention provides a new system that enriches the recording of users' daily lives and enables them to reflect on them.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] With the user's permission, the device collects digital information such as image data from the camera roll, calendar events, and online purchase history. This collected information reflects the user's daily activities.

[0379] Step 2:

[0380] The device organizes the collected data and groups related information. For example, it forms a relevant dataset by grouping photos taken on the same date or at similar times with appointments scheduled for those dates.

[0381] Step 3:

[0382] The device transfers the organized data to the server. During this process, the data is encrypted, and secure communication protocols are used to protect user privacy.

[0383] Step 4:

[0384] The server analyzes the received data. First, it uses image recognition technology to analyze facial expressions and situations in photographs, and then, in conjunction with planned activities and purchase history, it infers the user's emotions. This analysis helps to understand what emotions the user was feeling at a particular time or event.

[0385] Step 5:

[0386] The emotion engine runs on the server and adjusts the tone and content of the text based on the inferred emotions. If the user's emotions are positive, it uses a bright tone in the text; if they are negative, it reflects that.

[0387] Step 6:

[0388] The generative AI generates natural language text based on the results of sentiment analysis. This text includes details of activities and emotional aspects of a specific day, forming a more personal diary.

[0389] Step 7:

[0390] The server organizes the generated diary entries in chronological order and sends them to the terminal. The terminal receives these entries and saves them within a dedicated application, allowing the user to easily access them later.

[0391] Step 8:

[0392] Users view diaries generated through an application on their device. They can add comments and make corrections to the diary within the application, and also send feedback to the server. This feedback is used to improve the diary generation process.

[0393] (Example 2)

[0394] 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".

[0395] Conventional recording systems often resulted in monotonous records of users' daily activities that failed to consider emotions, leading to insufficient informational value in reflecting on users' lives. Furthermore, the generated records frequently did not match the user's actual emotions or activities, highlighting the need for methods to improve their quality.

[0396] 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.

[0397] In this invention, the server includes an analysis means for analyzing data related to daily activities acquired through a data collection means and inferring the user's emotions; a generation means for generating natural language text based on the inferred emotion data and activity data; and a storage means for organizing and saving the generated text in chronological order. This makes it possible to richly record the user's daily activities from an emotional perspective and provide the user with high-quality information.

[0398] "Data collection means" refers to a device or program for efficiently and safely acquiring information about a user's daily activities.

[0399] "Analysis means" refers to a device or program that uses collected data to infer specific emotions or activities of a user, thereby enabling a deeper understanding.

[0400] A "generation means" is a device or program that effectively generates natural language text based on analyzed data and provides information in a user-friendly manner.

[0401] "Storage means" refers to a device or program for appropriately organizing generated text and saving data as needed.

[0402] "Display means" refers to a device or program that visually presents stored information to the user and makes it easily accessible.

[0403] "Learning tools" are algorithms and programs that utilize user feedback to improve the system's performance and the quality of its generated content.

[0404] "Image data" refers to information used to visually record user activity, and is digital data that includes images of faces, objects, and other objects.

[0405] "Schedule data" refers to information that reflects the user's daily schedule and plans.

[0406] "Recorded data" refers to information about a user's daily activities, including their behavioral history and purchase history.

[0407] This invention is a system that records a user's daily activities in detail and provides them in an emotionally rich written format. Embodiments of this system are described below.

[0408] First, users collect digital data related to their daily lives, such as image information, activity schedules, and purchase history, through their devices. This data is automatically encrypted as a security measure and stored securely.

[0409] Next, the device sends this encrypted data to the server. The data is transferred using a secure communication protocol, and privacy is strictly protected.

[0410] The server receives the transmitted data and performs analysis using a generative AI and an emotion engine. Specifically, it uses image recognition software to identify the user's facial expressions from images and infers the user's emotions and behavioral patterns based on their planned activities and purchase history. At the core of this system is the generative AI model, and an example of its prompt message is, "Analyze the user's emotions from their image and planned activities, and generate a reflection on their day."

[0411] Next, the generation AI generates text in natural language format based on the analyzed data. This text is then organized chronologically and sent to the user's device.

[0412] The user's device displays received messages through a dedicated application, allowing the user to easily access and review the content. For example, a user can use photos and schedule information to specifically record details of a picnic they had with their family on a holiday, such as, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park."

[0413] Furthermore, user feedback is collected by the server. This feedback is used to improve the accuracy of the generative AI and emotion engine, and the system evolves daily in response to user needs.

[0414] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0415] Step 1:

[0416] Users input image information, activity schedule information, and purchase history information using their own devices. This digital data is temporarily stored on the device and encrypted in real time for security. The input information is related to the user's daily activities and serves as basic data necessary for subsequent analysis. As output, the encrypted data is stored on the device.

[0417] Step 2:

[0418] The device transfers encrypted data to the server. This transmission uses a secure communication protocol, thus maintaining privacy and security. The input is encrypted data on the device, and the output after transmission is stored in data storage on the server.

[0419] Step 3:

[0420] The server receives the transmitted data and first decodes it to restore it to its original state. Next, it performs analysis using a generative AI model and an emotion analysis engine. At this stage, image recognition technology is used to identify the user's facial expressions and situation from the image information, and this is combined with planned actions and purchase history to infer the user's emotions. The input is the decoded data, and the output is the analyzed data inferring the user's emotions.

[0421] Step 4:

[0422] Based on the analyzed data, the server uses a generative AI model to generate natural language text. An example of a prompt used here is, "Analyze the user's emotions from their image and planned activities, and generate a text reflecting on their day." Based on this prompt, the analyzed data is used as input, and the output is a text that incorporates the user's activities and emotions.

[0423] Step 5:

[0424] The server organizes the generated text in chronological order and prepares it for transmission to the user's terminal. The organized text is formatted to allow for easy searching and visual review by the user. The input is the generated text, and the output is the organized text, which is temporarily stored on the server.

[0425] Step 6:

[0426] The terminal receives organized text sent from the server and displays it to the user using a dedicated application. Through this application, the user can easily review each scene and the emotional situations within it. The input is text sent from the server, and the output is displayed information in a format that the user can visually confirm.

[0427] (Application Example 2)

[0428] 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."

[0429] In modern commercial facilities, there is a need to offer product suggestions tailored to the individual needs and emotions of each customer in order to improve their satisfaction. However, with current technology, it has been difficult to analyze the emotions of individual customers and propose appropriate products and services based on them. A means to solve this problem and improve the customer experience is needed.

[0430] 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.

[0431] In this invention, the server includes a generation means for analyzing data acquired from a data collection means and generating sentences in natural language form; a storage means for saving the generated sentences in chronological order; a display means for displaying the saved sentences to the user; an emotion analysis means for analyzing the user's behavior and inferring their emotions; and a suggestion means for suggesting products and services based on the inferred emotions. This makes it possible to suggest products that are tailored to the user's emotions.

[0432] "Data collection means" refers to methods for acquiring information from users' terminals or other devices and using it for analysis.

[0433] A "generation means" is a means that has the function of creating a text in natural language form based on acquired data.

[0434] A "storage method" is a means for storing generated text in chronological order.

[0435] "Display means" refers to a means of presenting stored text or data in a way that allows users to visually confirm it.

[0436] "Emotional analysis methods" are means of performing analysis to infer the emotions of users from collected data.

[0437] A "proposal method" is a means of suggesting products and services optimized for the user based on analyzed emotional information.

[0438] To implement this invention, first, the terminal functions as a data collection means and acquires data about the user's activities. This data includes image data, schedule data, and transaction history data. This data is appropriately encrypted and then transferred to a server.

[0439] The server is equipped with a generation mechanism for analyzing the received data. Here, it uses the image recognition library OpenCV to recognize the user's facial expressions from images, combines them with behavioral data and transaction history, and analyzes emotions. Emotion analysis is used for the analysis, and an emotion engine is utilized to infer the user's emotions.

[0440] Based on the analyzed emotional information, the server executes a suggestion mechanism to propose products and services. Using a generative AI model, it automatically generates appropriate natural language sentences and notifies the user. For example, based on the user's facial expressions and purchase history, it can generate a prompt sentence such as, "The customer smiled while sampling product XYZ. What kind of offer would you like to propose?"

[0441] Furthermore, the generated text is organized chronologically using the server's storage system. Users can view this information through the display system and provide feedback. User feedback is used to improve the algorithm through the system's learning mechanism. As a result, the system continuously improves the accuracy of user sentiment analysis and evolves to provide more refined product recommendations.

[0442] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0443] Step 1:

[0444] The terminal acquires user image data, activity plan data, and transaction history data as data collection tools. This data is temporarily stored on the terminal and transferred to the server via a communication module. The input is raw data related to the user's daily activities, and the output is encrypted data sent to the server.

[0445] Step 2:

[0446] The server decrypts the received encrypted data and analyzes the user's facial expressions from the image data using an image recognition library (e.g., OpenCV). The input is encrypted user data, and the output is analyzed information about the user's facial expressions. The server evaluates the analyzed facial expressions using emotion analysis tools and generates an estimated emotion.

[0447] Step 3:

[0448] The server uses a generative AI model to generate natural language-based suggestion sentences based on inferred sentiment information. An example of this prompt sentence is: "The customer smiled while sampling product XYZ. What kind of offer would you suggest?" The input is inferred sentiment data, and the output is a suggestion sentence for the user.

[0449] Step 4:

[0450] The server stores the generated suggestion text in a database in chronological order using a storage mechanism and notifies the user's terminal via a display mechanism. The input is the generated text, and the output is the notification message received on the user's terminal.

[0451] Step 5:

[0452] Users select products and services based on suggestions received through their devices and send their feedback to the server. The input is the user's feedback on the suggestions, and the output is the feedback data used for improvement.

[0453] Step 6:

[0454] The server uses user feedback as a learning tool to adjust the parameters of its sentence generation algorithm and sentiment analysis tools. This improves the accuracy of sentiment evaluation and suggestions in subsequent uses. The input is feedback data, and the output is the improved algorithm and updated sentiment analysis model.

[0455] 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.

[0456] 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 those described above. 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 shown 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.

[0457] 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.

[0458] [Third Embodiment]

[0459] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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".

[0471] This invention provides a system that automatically records a user's daily activities using their digital device and presents them as natural language text. The following describes an embodiment of the system and its operation in detail.

[0472] First, the user's device functions as a data collection device. Based on the user's consent, digital information such as images taken with the camera, appointments registered in the calendar, and purchase history from online shopping are collected on a daily basis. This information includes specific details about the user's activities and lifestyle.

[0473] The device transfers the collected information to the server. The server analyzes this information and uses generative AI to generate natural language text. Specifically, it identifies locations and events through image analysis, associates them with schedule information, and describes the events that occurred on that day. For example, if a photo of a museum taken by the user matches their planned visit for that day, the server will generate text such as, "Today I visited a museum and viewed various works of art."

[0474] The generated text is sent from the server to the user's terminal and saved chronologically by a storage device. Users can view their saved diaries through a dedicated application, making it easy to review past events. Furthermore, the terminal's display provides information in a visually appropriate format for the user.

[0475] Furthermore, users can provide feedback on the content of their diaries, and this feedback is collected by the server's learning system. The learning system uses the provided feedback to improve the algorithm of the generating AI, enabling the automatic generation of more personalized diaries. In this way, the system continues to evolve according to the user's needs.

[0476] As a concrete example, when a user visits a tourist destination, if they have taken numerous photos with their device and their travel plans are registered in their calendar, the server integrates this data and automatically creates a detailed diary entry including events and impressions from the tourist destination. In this way, the user can easily record and manage the activities they have undertaken. This invention provides a new means of streamlining the recording of daily life and supplementing the user's memory.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The device collects image data from the camera roll with the user's consent. It also obtains schedule information from the calendar app and purchase history from browsers and shopping apps. This data is related to the user's daily activities.

[0480] Step 2:

[0481] The device organizes the collected data and groups information belonging to the same timeframe. This process includes associations based on date, time, and geographical location data.

[0482] Step 3:

[0483] The organized data is uploaded to the server. During this process, the data is encrypted, and communication is conducted using a secure protocol. Ensuring user privacy is crucial.

[0484] Step 4:

[0485] The server analyzes the received data and generates diary entries using a generative AI. It performs image recognition to identify places and people in photos, combines this with scheduled information, and describes the day's events in natural language.

[0486] Step 5:

[0487] The generated text is organized and structured by the server. This process also includes editing to ensure a natural flow of text. Personalized messages relevant to the user may be incorporated as needed.

[0488] Step 6:

[0489] The server sends the completed diary entry back to the terminal. The terminal receives it and saves it in an application that is easily accessible to the user. This saving is done chronologically, making it easy to refer to later.

[0490] Step 7:

[0491] Users can view the generated diary entries via their device. They can edit or comment on specific entries. Users can also provide feedback, which is used to improve the generation AI's algorithm.

[0492] Step 8:

[0493] The server analyzes user feedback and updates the AI ​​model to improve the accuracy and user-friendliness of future text generation. This process is continuous, improving both the system's precision and the user experience.

[0494] (Example 1)

[0495] 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."

[0496] In modern society, meticulously recording an individual's daily life is time-consuming and laborious, making it difficult. This can lead to important events and activities being lost from memory. Furthermore, there is a need to organize and display the collected information in a user-friendly format. Therefore, an effective and efficient activity recording and management system using digital devices is necessary.

[0497] 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.

[0498] In this invention, the server includes a process means for analyzing digital information collected by a data acquisition means and generating natural language text using a generation AI model; a data recording means for recording the generated text in chronological order; and a display means for presenting the recorded text to the user via an information output means. This makes it possible to automatically record and visually confirm the user's daily activities in detail.

[0499] "Data acquisition means" refers to a device or process that has the function of collecting a user's digital information.

[0500] "Digital information" refers to electronic data related to an individual's activities and lifestyle, such as visual information, schedule information, and purchase history information.

[0501] A "generative AI model" is an artificial intelligence model that generates natural language text based on input digital information.

[0502] "Process means" refers to a function or step for analyzing digital information and generating natural language text using a generative AI model.

[0503] "Data recording means" refers to a function or device for recording and saving generated text in chronological order.

[0504] "Information output means" refers to a device or method for visually presenting recorded information to a user.

[0505] "Display means" refers to a device or system used by a user to visually confirm recorded text.

[0506] A "learning tool" is a function or system that uses user feedback to improve the text generation algorithm.

[0507] This invention is a system for efficiently recording and managing a user's daily activities. This system consists of a user's terminal, a server, and a dedicated application.

[0508] The terminal functions as a data acquisition tool, collecting digital information about the user's daily activities. This digital information includes visual information captured by the camera, schedule information registered in schedule management software, and purchase history information on online platforms. The terminal transfers this information to the server at regular intervals.

[0509] The server has processing capabilities that analyze the received digital information and use a generative AI model to convert it into natural language. This process includes steps such as recognizing specific locations or events through image analysis and associating them with scheduled information. For example, if a user's photos taken at a tourist destination are linked to their schedule for that day, a sentence like "The user visited a tourist destination, took many photos, and had a fun day" might be generated. Open-source natural language generation frameworks are sometimes used as the generative AI model in this process.

[0510] The server sorts the generated text chronologically using a data recording device and sends it to the user's terminal. The terminal visually presents this information through a dedicated application. The user can easily review and recall past events using the dedicated application. An example of a prompt message is the instruction, "Summarize your day based on your experiences."

[0511] Furthermore, users can provide feedback on the generated text, including opinions and suggestions for improvement. The server collects this feedback and adaptively improves the AI ​​model through learning mechanisms. This allows the generated text to gradually adapt to the user's individual preferences and style, resulting in a more accurate and personalized diary.

[0512] This invention simplifies users' life record-keeping and enables accurate management and reflection of memories.

[0513] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0514] Step 1:

[0515] The user's device collects digital information.

[0516] Input: Digital information related to the user's daily activities (e.g., photos taken with a camera, schedule information, purchase history).

[0517] Operation: The device collects digital information from sensors such as the camera and schedule management apps.

[0518] Output: Various digital data stored on the device.

[0519] Step 2:

[0520] The terminal transfers the collected digital information to the server.

[0521] Input: Digital data stored on the device.

[0522] Operation: The device encrypts the collected data and transfers it to the server via the network.

[0523] Output: Reception of digital information on the server.

[0524] Step 3:

[0525] The server analyzes the information it receives.

[0526] Input: Digital information transferred to the server.

[0527] Operation: The server uses image analysis and natural language processing techniques to recognize objects within images and interpret schedules.

[0528] Output: Analyzed location and event information.

[0529] Step 4:

[0530] The server generates natural language text using a generation AI model.

[0531] Input: Analysis results (location information and event information).

[0532] Operation: The generation AI model generates text based on the analysis results and the prompt text. An example of a prompt text is, "Summarize your day based on your user experience."

[0533] Output: The generated text in natural language format.

[0534] Step 5:

[0535] The server sends the generated text to the terminal, and the terminal saves the data.

[0536] Input: A generated text in natural language format.

[0537] Operation: The server sends text to the terminal, and the terminal saves it to the database in chronological order.

[0538] Output: Text data saved on the device, sorted by date and time.

[0539] Step 6:

[0540] Users can view their diaries and provide feedback through a dedicated application.

[0541] Input: Text data stored on the device.

[0542] Operation: Users use the application to view their diary entries and send feedback about the content.

[0543] Output: User feedback sent to the server.

[0544] Step 7:

[0545] The server improves the AI ​​model based on the feedback.

[0546] Input: Feedback provided by users.

[0547] Operation: The server's learning device analyzes the feedback and adjusts and improves the algorithm of the generated AI model.

[0548] Output: More accurate text generation using an improved AI model.

[0549] (Application Example 1)

[0550] 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."

[0551] With conventional technology, it has been difficult to easily record everyday events and experiences, and to automatically deliver personalized content based on those records. Furthermore, there is a demand not only for simple recording, but also for personalized information tailored to the user's interests. Therefore, there is a need to efficiently and effectively record users' daily activities and automatically generate and deliver personalized content using those records.

[0552] 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.

[0553] In this invention, the server includes means for analyzing digital information acquired from a data collection device and generating text in natural language; means for storing the generated text in chronological order; means for displaying the stored text to the user; and means for automatically delivering content based on the user's experience. This makes it possible for users not only to easily record everyday events but also to receive personalized content based on those records.

[0554] A "data acquisition device" is hardware or software used to acquire digital information about a user's daily activities.

[0555] "Analysis" refers to processing collected digital information and extracting meanings and patterns that are relevant to a specific purpose.

[0556] A "natural language text" is a text composed of language that humans can understand, and whose generated content is described using everyday language expressions.

[0557] A "generation device" is a device or system for generating natural language text based on analyzed information.

[0558] A "storage device" is a device or system that stores generated documents in a specified order and makes them available for retrieval as needed.

[0559] A "display device" is a device used to visually present stored text to a user.

[0560] A "content distribution device" is a device or system for delivering content generated based on the user's experience to the user.

[0561] "Feedback" refers to the opinions and reactions provided by users, and is information that can be used to improve the system.

[0562] A "learning device" is a device or system that improves the algorithm of a generation device based on feedback, enabling the generation of natural language text that is more suitable for the user.

[0563] "Digital information" refers to electronic data related to a user's daily activities, such as image information, activity plan information, and purchase history information.

[0564] The system for implementing this invention first uses a smartphone or smart glasses as the user terminal. These devices function as data collection devices and collect digital information about the user's daily activities. Specifically, they acquire image information, activity schedule information, purchase history information, etc. Only data that the user has given permission for is collected, thus protecting the privacy of the data.

[0565] The collected data is sent to a cloud server. The server analyzes the collected data and processes it to associate different pieces of information. Image recognition technology is used in this analysis to recognize specific locations and events and link them to calendar information. Based on the analysis results, a generative AI model generates sentences in natural language. For example, if a photo taken by the user at their travel destination matches their hiking plan for that day, the sentence "Today I enjoyed hiking while exploring a beautiful lake at my travel destination" will be generated.

[0566] The generated text is sent back to the user's device from the server and displayed chronologically by a dedicated application. Through this application, users can review their diary entries and reflect on their experiences visualized as content. Furthermore, by receiving user feedback, the server's learning system improves the AI's algorithm, enabling the generation of more personalized content.

[0567] For example, if a user takes photos of activities in a nature park during a weekend trip, and those activities are scheduled in their calendar, the server can automatically generate content such as "A wonderful holiday in the nature park, enjoyed a picnic and explored new hiking trails," and deliver it in blog format. In this way, users can easily share their personal experiences over the internet.

[0568] An example of a prompt message would be: "Generate a natural-sounding travelogue based on the user's camera images, the name of the place visited ('Nature Park'), and the activity ('Hiking')."

[0569] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0570] Step 1:

[0571] Users collect data on their daily activities using smartphones or smart glasses. Inputs include images taken by the user, calendar activity schedules, and purchase history. This data is temporarily stored on the device to prepare for the next processing step.

[0572] Step 2:

[0573] The device sends data authorized by the user to a cloud server. The input is collected digital information, and the output is raw data transferred to the server. During the data transfer process, the data is encrypted to protect privacy.

[0574] Step 3:

[0575] The server analyzes the received digital information. The input is encrypted digital data, and the output is intermediate data representing the analysis results. This analysis uses image recognition algorithms to identify specific objects and scenes and associate them with planned action information.

[0576] Step 4:

[0577] The server uses a generative AI model to generate natural language text from the analysis results. The input is the analyzed intermediate data, and the output is the constructed natural language text. In this process, prompt sentences are used to instruct the server to generate text to represent a specific situation.

[0578] Step 5:

[0579] The server sends the generated text to the terminal. The input is the generated text in natural language format, and the output is sent to the user's terminal as a message. The transmitted data is displayed to the user by a dedicated application running on the terminal.

[0580] Step 6:

[0581] Users use a dedicated application on their device to review the generated natural language text. Here, the input is text received from the server, and the output is displayed in a format that the user can visually review. This application allows users to reflect on their experiences.

[0582] Step 7:

[0583] Users provide feedback on the generated text. The input consists of user opinions and comments, and the output is processed by the server's learning system. This feedback is used to improve the algorithm, enabling more personalized content generation in the future.

[0584] 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.

[0585] This invention provides a system that automatically records a user's daily activities and further analyzes the user's emotions during those activities, presenting the results as text in natural language. The embodiments and operation of this system are described in detail below.

[0586] The system first collects digital information through the user's device. The collected information includes image data, activity schedules, and purchase history, which allows the system to obtain specific data about the user's daily life.

[0587] When data is transferred from the terminal to the server, it is encrypted with security and privacy in mind. The server is equipped with a generative AI and an emotion engine to analyze this data. In the analysis process, image recognition technology is used to detect the user's facial expressions and surrounding environment from images. Combined with planned activities and purchase history, it is possible to infer the user's emotions. For example, if a photo in which the user has a happy expression matches an appointment with a friend, the emotion will be specifically reflected in the text, such as "Enjoyed a conversation with a friend."

[0588] Next, the generation AI automatically generates natural language text with appropriate tone and content based on the analyzed data and emotional information. The generated text is organized chronologically on the server and then sent back to the user's device.

[0589] Users can view these automatically generated diary entries through a dedicated application. A key feature is that emotional information is included in the text, allowing users to create richer diaries that go beyond mere records of events and encompass the emotional state of the time.

[0590] Furthermore, user feedback is provided to the server to improve the algorithms of the generation AI and emotion engine. This enables the generation of more user-friendly diaries, and the system evolves according to user needs.

[0591] As a concrete example of this system, consider a scenario where a user enjoys a picnic with their family on a holiday. From digital information such as photos taken with the device, the picnic plan, and purchased food, the server can generate a diary entry in the form of, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park," and deliver it to the user.

[0592] In this way, the present invention provides a new system that enriches the recording of users' daily lives and enables them to reflect on them.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] With the user's permission, the device collects digital information such as image data from the camera roll, calendar events, and online purchase history. This collected information reflects the user's daily activities.

[0596] Step 2:

[0597] The device organizes the collected data and groups related information. For example, it forms a relevant dataset by grouping photos taken on the same date or at similar times with appointments scheduled for those dates.

[0598] Step 3:

[0599] The device transfers the organized data to the server. During this process, the data is encrypted, and secure communication protocols are used to protect user privacy.

[0600] Step 4:

[0601] The server analyzes the received data. First, it uses image recognition technology to analyze facial expressions and situations in photographs, and then, in conjunction with planned activities and purchase history, it infers the user's emotions. This analysis helps to understand what emotions the user was feeling at a particular time or event.

[0602] Step 5:

[0603] The emotion engine runs on the server and adjusts the tone and content of the text based on the inferred emotions. If the user's emotions are positive, it uses a bright tone in the text; if they are negative, it reflects that.

[0604] Step 6:

[0605] The generative AI generates natural language text based on the results of sentiment analysis. This text includes details of activities and emotional aspects of a specific day, forming a more personal diary.

[0606] Step 7:

[0607] The server organizes the generated diary entries in chronological order and sends them to the terminal. The terminal receives these entries and saves them within a dedicated application, allowing the user to easily access them later.

[0608] Step 8:

[0609] Users view diaries generated through an application on their device. They can add comments and make corrections to the diary within the application, and also send feedback to the server. This feedback is used to improve the diary generation process.

[0610] (Example 2)

[0611] 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."

[0612] Conventional recording systems often resulted in monotonous records of users' daily activities that failed to consider emotions, leading to insufficient informational value in reflecting on users' lives. Furthermore, the generated records frequently did not match the user's actual emotions or activities, highlighting the need for methods to improve their quality.

[0613] 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.

[0614] In this invention, the server includes an analysis means for analyzing data related to daily activities acquired through a data collection means and inferring the user's emotions; a generation means for generating natural language text based on the inferred emotion data and activity data; and a storage means for organizing and saving the generated text in chronological order. This makes it possible to richly record the user's daily activities from an emotional perspective and provide the user with high-quality information.

[0615] "Data collection means" refers to a device or program for efficiently and safely acquiring information about a user's daily activities.

[0616] "Analysis means" refers to a device or program that uses collected data to infer specific emotions or activities of a user, thereby enabling a deeper understanding.

[0617] A "generation means" is a device or program that effectively generates natural language text based on analyzed data and provides information in a user-friendly manner.

[0618] "Storage means" refers to a device or program for appropriately organizing generated text and saving data as needed.

[0619] "Display means" refers to a device or program that visually presents stored information to the user and makes it easily accessible.

[0620] "Learning tools" are algorithms and programs that utilize user feedback to improve the system's performance and the quality of its generated content.

[0621] "Image data" refers to information used to visually record user activity, and is digital data that includes images of faces, objects, and other objects.

[0622] "Schedule data" refers to information that reflects the user's daily schedule and plans.

[0623] "Recorded data" refers to information about a user's daily activities, including their behavioral history and purchase history.

[0624] This invention is a system that records a user's daily activities in detail and provides them in an emotionally rich written format. Embodiments of this system are described below.

[0625] First, users collect digital data related to their daily lives, such as image information, activity schedules, and purchase history, through their devices. This data is automatically encrypted as a security measure and stored securely.

[0626] Next, the device sends this encrypted data to the server. The data is transferred using a secure communication protocol, and privacy is strictly protected.

[0627] The server receives the transmitted data and performs analysis using a generative AI and an emotion engine. Specifically, it uses image recognition software to identify the user's facial expressions from images and infers the user's emotions and behavioral patterns based on their planned activities and purchase history. At the core of this system is the generative AI model, and an example of its prompt message is, "Analyze the user's emotions from their image and planned activities, and generate a reflection on their day."

[0628] Next, the generation AI generates text in natural language format based on the analyzed data. This text is then organized chronologically and sent to the user's device.

[0629] The user's device displays received messages through a dedicated application, allowing the user to easily access and review the content. For example, a user can use photos and schedule information to specifically record details of a picnic they had with their family on a holiday, such as, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park."

[0630] Furthermore, user feedback is collected by the server. This feedback is used to improve the accuracy of the generative AI and emotion engine, and the system evolves daily in response to user needs.

[0631] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0632] Step 1:

[0633] Users input image information, activity schedule information, and purchase history information using their own devices. This digital data is temporarily stored on the device and encrypted in real time for security. The input information is related to the user's daily activities and serves as basic data necessary for subsequent analysis. As output, the encrypted data is stored on the device.

[0634] Step 2:

[0635] The device transfers encrypted data to the server. This transmission uses a secure communication protocol, thus maintaining privacy and security. The input is encrypted data on the device, and the output after transmission is stored in data storage on the server.

[0636] Step 3:

[0637] The server receives the transmitted data and first decodes it to restore it to its original state. Next, it performs analysis using a generative AI model and an emotion analysis engine. At this stage, image recognition technology is used to identify the user's facial expressions and situation from the image information, and this is combined with planned actions and purchase history to infer the user's emotions. The input is the decoded data, and the output is the analyzed data inferring the user's emotions.

[0638] Step 4:

[0639] Based on the analyzed data, the server uses a generative AI model to generate natural language text. An example of a prompt used here is, "Analyze the user's emotions from their image and planned activities, and generate a text reflecting on their day." Based on this prompt, the analyzed data is used as input, and the output is a text that incorporates the user's activities and emotions.

[0640] Step 5:

[0641] The server organizes the generated text in chronological order and prepares it for transmission to the user's terminal. The organized text is formatted to allow for easy searching and visual review by the user. The input is the generated text, and the output is the organized text, which is temporarily stored on the server.

[0642] Step 6:

[0643] The terminal receives organized text sent from the server and displays it to the user using a dedicated application. Through this application, the user can easily review each scene and the emotional situations within it. The input is text sent from the server, and the output is displayed information in a format that the user can visually confirm.

[0644] (Application Example 2)

[0645] 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."

[0646] In modern commercial facilities, there is a need to offer product suggestions tailored to the individual needs and emotions of each customer in order to improve their satisfaction. However, with current technology, it has been difficult to analyze the emotions of individual customers and propose appropriate products and services based on them. A means to solve this problem and improve the customer experience is needed.

[0647] 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.

[0648] In this invention, the server includes a generation means for analyzing data acquired from a data collection means and generating sentences in natural language form; a storage means for saving the generated sentences in chronological order; a display means for displaying the saved sentences to the user; an emotion analysis means for analyzing the user's behavior and inferring their emotions; and a suggestion means for suggesting products and services based on the inferred emotions. This makes it possible to suggest products that are tailored to the user's emotions.

[0649] "Data collection means" refers to methods for acquiring information from users' terminals or other devices and using it for analysis.

[0650] A "generation means" is a means that has the function of creating a text in natural language form based on acquired data.

[0651] A "storage method" is a means for storing generated text in chronological order.

[0652] "Display means" refers to a means of presenting stored text or data in a way that allows users to visually confirm it.

[0653] "Emotional analysis methods" are means of performing analysis to infer the emotions of users from collected data.

[0654] A "proposal method" is a means of suggesting products and services optimized for the user based on analyzed emotional information.

[0655] To implement this invention, first, the terminal functions as a data collection means and acquires data about the user's activities. This data includes image data, schedule data, and transaction history data. This data is appropriately encrypted and then transferred to a server.

[0656] The server is equipped with a generation mechanism for analyzing the received data. Here, it uses the image recognition library OpenCV to recognize the user's facial expressions from images, combines them with behavioral data and transaction history, and analyzes emotions. Emotion analysis is used for the analysis, and an emotion engine is utilized to infer the user's emotions.

[0657] Based on the analyzed emotional information, the server executes a suggestion mechanism to propose products and services. Using a generative AI model, it automatically generates appropriate natural language sentences and notifies the user. For example, based on the user's facial expressions and purchase history, it can generate a prompt sentence such as, "The customer smiled while sampling product XYZ. What kind of offer would you like to propose?"

[0658] Furthermore, the generated text is organized chronologically using the server's storage system. Users can view this information through the display system and provide feedback. User feedback is used to improve the algorithm through the system's learning mechanism. As a result, the system continuously improves the accuracy of user sentiment analysis and evolves to provide more refined product recommendations.

[0659] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0660] Step 1:

[0661] The terminal acquires user image data, activity plan data, and transaction history data as data collection tools. This data is temporarily stored on the terminal and transferred to the server via a communication module. The input is raw data related to the user's daily activities, and the output is encrypted data sent to the server.

[0662] Step 2:

[0663] The server decrypts the received encrypted data and analyzes the user's facial expressions from the image data using an image recognition library (e.g., OpenCV). The input is encrypted user data, and the output is analyzed information about the user's facial expressions. The server evaluates the analyzed facial expressions using emotion analysis tools and generates an estimated emotion.

[0664] Step 3:

[0665] The server uses a generative AI model to generate natural language-based suggestion sentences based on inferred sentiment information. An example of this prompt sentence is: "The customer smiled while sampling product XYZ. What kind of offer would you suggest?" The input is inferred sentiment data, and the output is a suggestion sentence for the user.

[0666] Step 4:

[0667] The server stores the generated suggestion text in a database in chronological order using a storage mechanism and notifies the user's terminal via a display mechanism. The input is the generated text, and the output is the notification message received on the user's terminal.

[0668] Step 5:

[0669] Users select products and services based on suggestions received through their devices and send their feedback to the server. The input is the user's feedback on the suggestions, and the output is the feedback data used for improvement.

[0670] Step 6:

[0671] The server uses user feedback as a learning tool to adjust the parameters of its sentence generation algorithm and sentiment analysis tools. This improves the accuracy of sentiment evaluation and suggestions in subsequent uses. The input is feedback data, and the output is the improved algorithm and updated sentiment analysis model.

[0672] 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.

[0673] 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 those described above. 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 shown 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.

[0674] 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.

[0675] [Fourth Embodiment]

[0676] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0677] 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.

[0678] 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).

[0679] 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.

[0680] 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.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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".

[0689] This invention provides a system that automatically records a user's daily activities using their digital device and presents them as natural language text. The following describes an embodiment of the system and its operation in detail.

[0690] First, the user's device functions as a data collection device. Based on the user's consent, digital information such as images taken with the camera, appointments registered in the calendar, and purchase history from online shopping are collected on a daily basis. This information includes specific details about the user's activities and lifestyle.

[0691] The device transfers the collected information to the server. The server analyzes this information and uses generative AI to generate natural language text. Specifically, it identifies locations and events through image analysis, associates them with schedule information, and describes the events that occurred on that day. For example, if a photo of a museum taken by the user matches their planned visit for that day, the server will generate text such as, "Today I visited a museum and viewed various works of art."

[0692] The generated text is sent from the server to the user's terminal and saved chronologically by a storage device. Users can view their saved diaries through a dedicated application, making it easy to review past events. Furthermore, the terminal's display provides information in a visually appropriate format for the user.

[0693] Furthermore, users can provide feedback on the content of their diaries, and this feedback is collected by the server's learning system. The learning system uses the provided feedback to improve the algorithm of the generating AI, enabling the automatic generation of more personalized diaries. In this way, the system continues to evolve according to the user's needs.

[0694] As a concrete example, when a user visits a tourist destination, if they have taken numerous photos with their device and their travel plans are registered in their calendar, the server integrates this data and automatically creates a detailed diary entry including events and impressions from the tourist destination. In this way, the user can easily record and manage the activities they have undertaken. This invention provides a new means of streamlining the recording of daily life and supplementing the user's memory.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The device collects image data from the camera roll with the user's consent. It also obtains schedule information from the calendar app and purchase history from browsers and shopping apps. This data is related to the user's daily activities.

[0698] Step 2:

[0699] The device organizes the collected data and groups information belonging to the same timeframe. This process includes associations based on date, time, and geographical location data.

[0700] Step 3:

[0701] The organized data is uploaded to the server. During this process, the data is encrypted, and communication is conducted using a secure protocol. Ensuring user privacy is crucial.

[0702] Step 4:

[0703] The server analyzes the received data and generates diary entries using a generative AI. It performs image recognition to identify places and people in photos, combines this with scheduled information, and describes the day's events in natural language.

[0704] Step 5:

[0705] The generated text is organized and structured by the server. This process also includes editing to ensure a natural flow of text. Personalized messages relevant to the user may be incorporated as needed.

[0706] Step 6:

[0707] The server sends the completed diary entry back to the terminal. The terminal receives it and saves it in an application that is easily accessible to the user. This saving is done chronologically, making it easy to refer to later.

[0708] Step 7:

[0709] Users can view the generated diary entries via their device. They can edit or comment on specific entries. Users can also provide feedback, which is used to improve the generation AI's algorithm.

[0710] Step 8:

[0711] The server analyzes user feedback and updates the AI ​​model to improve the accuracy and user-friendliness of future text generation. This process is continuous, improving both the system's precision and the user experience.

[0712] (Example 1)

[0713] 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".

[0714] In modern society, meticulously recording an individual's daily life is time-consuming and laborious, making it difficult. This can lead to important events and activities being lost from memory. Furthermore, there is a need to organize and display the collected information in a user-friendly format. Therefore, an effective and efficient activity recording and management system using digital devices is necessary.

[0715] 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.

[0716] In this invention, the server includes a process means for analyzing digital information collected by a data acquisition means and generating natural language text using a generation AI model; a data recording means for recording the generated text in chronological order; and a display means for presenting the recorded text to the user via an information output means. This makes it possible to automatically record and visually confirm the user's daily activities in detail.

[0717] "Data acquisition means" refers to a device or process that has the function of collecting a user's digital information.

[0718] "Digital information" refers to electronic data related to an individual's activities and lifestyle, such as visual information, schedule information, and purchase history information.

[0719] A "generative AI model" is an artificial intelligence model that generates natural language text based on input digital information.

[0720] "Process means" refers to a function or step for analyzing digital information and generating natural language text using a generative AI model.

[0721] "Data recording means" refers to a function or device for recording and saving generated text in chronological order.

[0722] "Information output means" refers to a device or method for visually presenting recorded information to a user.

[0723] "Display means" refers to a device or system used by a user to visually confirm recorded text.

[0724] A "learning tool" is a function or system that uses user feedback to improve the text generation algorithm.

[0725] This invention is a system for efficiently recording and managing a user's daily activities. This system consists of a user's terminal, a server, and a dedicated application.

[0726] The terminal functions as a data acquisition tool, collecting digital information about the user's daily activities. This digital information includes visual information captured by the camera, schedule information registered in schedule management software, and purchase history information on online platforms. The terminal transfers this information to the server at regular intervals.

[0727] The server has processing capabilities that analyze the received digital information and use a generative AI model to convert it into natural language. This process includes steps such as recognizing specific locations or events through image analysis and associating them with scheduled information. For example, if a user's photos taken at a tourist destination are linked to their schedule for that day, a sentence like "The user visited a tourist destination, took many photos, and had a fun day" might be generated. Open-source natural language generation frameworks are sometimes used as the generative AI model in this process.

[0728] The server sorts the generated text chronologically using a data recording device and sends it to the user's terminal. The terminal visually presents this information through a dedicated application. The user can easily review and recall past events using the dedicated application. An example of a prompt message is the instruction, "Summarize your day based on your experiences."

[0729] Furthermore, users can provide feedback on the generated text, including opinions and suggestions for improvement. The server collects this feedback and adaptively improves the AI ​​model through learning mechanisms. This allows the generated text to gradually adapt to the user's individual preferences and style, resulting in a more accurate and personalized diary.

[0730] This invention simplifies users' life record-keeping and enables accurate management and reflection of memories.

[0731] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0732] Step 1:

[0733] The user's device collects digital information.

[0734] Input: Digital information related to the user's daily activities (e.g., photos taken with a camera, schedule information, purchase history).

[0735] Operation: The device collects digital information from sensors such as the camera and schedule management apps.

[0736] Output: Various digital data stored on the device.

[0737] Step 2:

[0738] The terminal transfers the collected digital information to the server.

[0739] Input: Digital data stored on the device.

[0740] Operation: The device encrypts the collected data and transfers it to the server via the network.

[0741] Output: Reception of digital information on the server.

[0742] Step 3:

[0743] The server analyzes the information it receives.

[0744] Input: Digital information transferred to the server.

[0745] Operation: The server uses image analysis and natural language processing techniques to recognize objects within images and interpret schedules.

[0746] Output: Analyzed location and event information.

[0747] Step 4:

[0748] The server generates natural language text using a generation AI model.

[0749] Input: Analysis results (location information and event information).

[0750] Operation: The generation AI model generates text based on the analysis results and the prompt text. An example of a prompt text is, "Summarize your day based on your user experience."

[0751] Output: The generated text in natural language format.

[0752] Step 5:

[0753] The server sends the generated text to the terminal, and the terminal saves the data.

[0754] Input: A generated text in natural language format.

[0755] Operation: The server sends text to the terminal, and the terminal saves it to the database in chronological order.

[0756] Output: Text data saved on the device, sorted by date and time.

[0757] Step 6:

[0758] Users can view their diaries and provide feedback through a dedicated application.

[0759] Input: Text data stored on the device.

[0760] Operation: Users use the application to view their diary entries and send feedback about the content.

[0761] Output: User feedback sent to the server.

[0762] Step 7:

[0763] The server improves the AI ​​model based on the feedback.

[0764] Input: Feedback provided by users.

[0765] Operation: The server's learning device analyzes the feedback and adjusts and improves the algorithm of the generated AI model.

[0766] Output: More accurate text generation using an improved AI model.

[0767] (Application Example 1)

[0768] 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".

[0769] With conventional technology, it has been difficult to easily record everyday events and experiences, and to automatically deliver personalized content based on those records. Furthermore, there is a demand not only for simple recording, but also for personalized information tailored to the user's interests. Therefore, there is a need to efficiently and effectively record users' daily activities and automatically generate and deliver personalized content using those records.

[0770] 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.

[0771] In this invention, the server includes means for analyzing digital information acquired from a data collection device and generating text in natural language; means for storing the generated text in chronological order; means for displaying the stored text to the user; and means for automatically delivering content based on the user's experience. This makes it possible for users not only to easily record everyday events but also to receive personalized content based on those records.

[0772] A "data acquisition device" is hardware or software used to acquire digital information about a user's daily activities.

[0773] "Analysis" refers to processing collected digital information and extracting meanings and patterns that are relevant to a specific purpose.

[0774] A "natural language text" is a text composed of language that humans can understand, and whose generated content is described using everyday language expressions.

[0775] A "generation device" is a device or system for generating natural language text based on analyzed information.

[0776] A "storage device" is a device or system that stores generated documents in a specified order and makes them available for retrieval as needed.

[0777] A "display device" is a device used to visually present stored text to a user.

[0778] A "content distribution device" is a device or system for delivering content generated based on the user's experience to the user.

[0779] "Feedback" refers to the opinions and reactions provided by users, and is information that can be used to improve the system.

[0780] A "learning device" is a device or system that improves the algorithm of a generation device based on feedback, enabling the generation of natural language text that is more suitable for the user.

[0781] "Digital information" refers to electronic data related to a user's daily activities, such as image information, activity plan information, and purchase history information.

[0782] The system for implementing this invention first uses a smartphone or smart glasses as the user terminal. These devices function as data collection devices and collect digital information about the user's daily activities. Specifically, they acquire image information, activity schedule information, purchase history information, etc. Only data that the user has given permission for is collected, thus protecting the privacy of the data.

[0783] The collected data is sent to a cloud server. The server analyzes the collected data and processes it to associate different pieces of information. Image recognition technology is used in this analysis to recognize specific locations and events and link them to calendar information. Based on the analysis results, a generative AI model generates sentences in natural language. For example, if a photo taken by the user at their travel destination matches their hiking plan for that day, the sentence "Today I enjoyed hiking while exploring a beautiful lake at my travel destination" will be generated.

[0784] The generated text is sent back to the user's device from the server and displayed chronologically by a dedicated application. Through this application, users can review their diary entries and reflect on their experiences visualized as content. Furthermore, by receiving user feedback, the server's learning system improves the AI's algorithm, enabling the generation of more personalized content.

[0785] For example, if a user takes photos of activities in a nature park during a weekend trip, and those activities are scheduled in their calendar, the server can automatically generate content such as "A wonderful holiday in the nature park, enjoyed a picnic and explored new hiking trails," and deliver it in blog format. In this way, users can easily share their personal experiences over the internet.

[0786] An example of a prompt message would be: "Generate a natural-sounding travelogue based on the user's camera images, the name of the place visited ('Nature Park'), and the activity ('Hiking')."

[0787] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0788] Step 1:

[0789] Users collect data on their daily activities using smartphones or smart glasses. Inputs include images taken by the user, calendar activity schedules, and purchase history. This data is temporarily stored on the device to prepare for the next processing step.

[0790] Step 2:

[0791] The device sends data authorized by the user to a cloud server. The input is collected digital information, and the output is raw data transferred to the server. During the data transfer process, the data is encrypted to protect privacy.

[0792] Step 3:

[0793] The server analyzes the received digital information. The input is encrypted digital data, and the output is intermediate data representing the analysis results. This analysis uses image recognition algorithms to identify specific objects and scenes and associate them with planned action information.

[0794] Step 4:

[0795] The server uses a generative AI model to generate natural language text from the analysis results. The input is the analyzed intermediate data, and the output is the constructed natural language text. In this process, prompt sentences are used to instruct the server to generate text to represent a specific situation.

[0796] Step 5:

[0797] The server sends the generated text to the terminal. The input is the generated text in natural language format, and the output is sent to the user's terminal as a message. The transmitted data is displayed to the user by a dedicated application running on the terminal.

[0798] Step 6:

[0799] Users use a dedicated application on their device to review the generated natural language text. Here, the input is text received from the server, and the output is displayed in a format that the user can visually review. This application allows users to reflect on their experiences.

[0800] Step 7:

[0801] Users provide feedback on the generated text. The input consists of user opinions and comments, and the output is processed by the server's learning system. This feedback is used to improve the algorithm, enabling more personalized content generation in the future.

[0802] 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.

[0803] This invention provides a system that automatically records a user's daily activities and further analyzes the user's emotions during those activities, presenting the results as text in natural language. The embodiments and operation of this system are described in detail below.

[0804] The system first collects digital information through the user's device. The collected information includes image data, activity schedules, and purchase history, which allows the system to obtain specific data about the user's daily life.

[0805] When data is transferred from the terminal to the server, it is encrypted with security and privacy in mind. The server is equipped with a generative AI and an emotion engine to analyze this data. In the analysis process, image recognition technology is used to detect the user's facial expressions and surrounding environment from images. Combined with planned activities and purchase history, it is possible to infer the user's emotions. For example, if a photo in which the user has a happy expression matches an appointment with a friend, the emotion will be specifically reflected in the text, such as "Enjoyed a conversation with a friend."

[0806] Next, the generation AI automatically generates natural language text with appropriate tone and content based on the analyzed data and emotional information. The generated text is organized chronologically on the server and then sent back to the user's device.

[0807] Users can view these automatically generated diary entries through a dedicated application. A key feature is that emotional information is included in the text, allowing users to create richer diaries that go beyond mere records of events and encompass the emotional state of the time.

[0808] Furthermore, user feedback is provided to the server to improve the algorithms of the generation AI and emotion engine. This enables the generation of more user-friendly diaries, and the system evolves according to user needs.

[0809] As a concrete example of this system, consider a scenario where a user enjoys a picnic with their family on a holiday. From digital information such as photos taken with the device, the picnic plan, and purchased food, the server can generate a diary entry in the form of, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park," and deliver it to the user.

[0810] In this way, the present invention provides a new system that enriches the recording of users' daily lives and enables them to reflect on them.

[0811] The following describes the processing flow.

[0812] Step 1:

[0813] With the user's permission, the device collects digital information such as image data from the camera roll, calendar events, and online purchase history. This collected information reflects the user's daily activities.

[0814] Step 2:

[0815] The device organizes the collected data and groups related information. For example, it forms a relevant dataset by grouping photos taken on the same date or at similar times with appointments scheduled for those dates.

[0816] Step 3:

[0817] The device transfers the organized data to the server. During this process, the data is encrypted, and secure communication protocols are used to protect user privacy.

[0818] Step 4:

[0819] The server analyzes the received data. First, it uses image recognition technology to analyze facial expressions and situations in photographs, and then, in conjunction with planned activities and purchase history, it infers the user's emotions. This analysis helps to understand what emotions the user was feeling at a particular time or event.

[0820] Step 5:

[0821] The emotion engine runs on the server and adjusts the tone and content of the text based on the inferred emotions. If the user's emotions are positive, it uses a bright tone in the text; if they are negative, it reflects that.

[0822] Step 6:

[0823] The generative AI generates natural language text based on the results of sentiment analysis. This text includes details of activities and emotional aspects of a specific day, forming a more personal diary.

[0824] Step 7:

[0825] The server organizes the generated diary entries in chronological order and sends them to the terminal. The terminal receives these entries and saves them within a dedicated application, allowing the user to easily access them later.

[0826] Step 8:

[0827] Users view diaries generated through an application on their device. They can add comments and make corrections to the diary within the application, and also send feedback to the server. This feedback is used to improve the diary generation process.

[0828] (Example 2)

[0829] 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".

[0830] Conventional recording systems often resulted in monotonous records of users' daily activities that failed to consider emotions, leading to insufficient informational value in reflecting on users' lives. Furthermore, the generated records frequently did not match the user's actual emotions or activities, highlighting the need for methods to improve their quality.

[0831] 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.

[0832] In this invention, the server includes an analysis means for analyzing data related to daily activities acquired through a data collection means and inferring the user's emotions; a generation means for generating natural language text based on the inferred emotion data and activity data; and a storage means for organizing and saving the generated text in chronological order. This makes it possible to richly record the user's daily activities from an emotional perspective and provide the user with high-quality information.

[0833] "Data collection means" refers to a device or program for efficiently and safely acquiring information about a user's daily activities.

[0834] "Analysis means" refers to a device or program that uses collected data to infer specific emotions or activities of a user, thereby enabling a deeper understanding.

[0835] A "generation means" is a device or program that effectively generates natural language text based on analyzed data and provides information in a user-friendly manner.

[0836] "Storage means" refers to a device or program for appropriately organizing generated text and saving data as needed.

[0837] "Display means" refers to a device or program that visually presents stored information to the user and makes it easily accessible.

[0838] "Learning tools" are algorithms and programs that utilize user feedback to improve the system's performance and the quality of its generated content.

[0839] "Image data" refers to information used to visually record user activity, and is digital data that includes images of faces, objects, and other objects.

[0840] "Schedule data" refers to information that reflects the user's daily schedule and plans.

[0841] "Recorded data" refers to information about a user's daily activities, including their behavioral history and purchase history.

[0842] This invention is a system that records a user's daily activities in detail and provides them in an emotionally rich written format. Embodiments of this system are described below.

[0843] First, users collect digital data related to their daily lives, such as image information, activity schedules, and purchase history, through their devices. This data is automatically encrypted as a security measure and stored securely.

[0844] Next, the device sends this encrypted data to the server. The data is transferred using a secure communication protocol, and privacy is strictly protected.

[0845] The server receives the transmitted data and performs analysis using a generative AI and an emotion engine. Specifically, it uses image recognition software to identify the user's facial expressions from images and infers the user's emotions and behavioral patterns based on their planned activities and purchase history. At the core of this system is the generative AI model, and an example of its prompt message is, "Analyze the user's emotions from their image and planned activities, and generate a reflection on their day."

[0846] Next, the generation AI generates text in natural language format based on the analyzed data. This text is then organized chronologically and sent to the user's device.

[0847] The user's device displays received messages through a dedicated application, allowing the user to easily access and review the content. For example, a user can use photos and schedule information to specifically record details of a picnic they had with their family on a holiday, such as, "Today I had a fun picnic with my family. Everyone was smiling, and the children especially enjoyed playing in the park."

[0848] Furthermore, user feedback is collected by the server. This feedback is used to improve the accuracy of the generative AI and emotion engine, and the system evolves daily in response to user needs.

[0849] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0850] Step 1:

[0851] Users input image information, activity schedule information, and purchase history information using their own devices. This digital data is temporarily stored on the device and encrypted in real time for security. The input information is related to the user's daily activities and serves as basic data necessary for subsequent analysis. As output, the encrypted data is stored on the device.

[0852] Step 2:

[0853] The device transfers encrypted data to the server. This transmission uses a secure communication protocol, thus maintaining privacy and security. The input is encrypted data on the device, and the output after transmission is stored in data storage on the server.

[0854] Step 3:

[0855] The server receives the transmitted data and first decodes it to restore it to its original state. Next, it performs analysis using a generative AI model and an emotion analysis engine. At this stage, image recognition technology is used to identify the user's facial expressions and situation from the image information, and this is combined with planned actions and purchase history to infer the user's emotions. The input is the decoded data, and the output is the analyzed data inferring the user's emotions.

[0856] Step 4:

[0857] Based on the analyzed data, the server uses a generative AI model to generate natural language text. An example of a prompt used here is, "Analyze the user's emotions from their image and planned activities, and generate a text reflecting on their day." Based on this prompt, the analyzed data is used as input, and the output is a text that incorporates the user's activities and emotions.

[0858] Step 5:

[0859] The server organizes the generated text in chronological order and prepares it for transmission to the user's terminal. The organized text is formatted to allow for easy searching and visual review by the user. The input is the generated text, and the output is the organized text, which is temporarily stored on the server.

[0860] Step 6:

[0861] The terminal receives organized text sent from the server and displays it to the user using a dedicated application. Through this application, the user can easily review each scene and the emotional situations within it. The input is text sent from the server, and the output is displayed information in a format that the user can visually confirm.

[0862] (Application Example 2)

[0863] 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".

[0864] In modern commercial facilities, there is a need to offer product suggestions tailored to the individual needs and emotions of each customer in order to improve their satisfaction. However, with current technology, it has been difficult to analyze the emotions of individual customers and propose appropriate products and services based on them. A means to solve this problem and improve the customer experience is needed.

[0865] 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.

[0866] In this invention, the server includes a generation means for analyzing data acquired from a data collection means and generating sentences in natural language form; a storage means for saving the generated sentences in chronological order; a display means for displaying the saved sentences to the user; an emotion analysis means for analyzing the user's behavior and inferring their emotions; and a suggestion means for suggesting products and services based on the inferred emotions. This makes it possible to suggest products that are tailored to the user's emotions.

[0867] "Data collection means" refers to methods for acquiring information from users' terminals or other devices and using it for analysis.

[0868] A "generation means" is a means that has the function of creating a text in natural language form based on acquired data.

[0869] A "storage method" is a means for storing generated text in chronological order.

[0870] "Display means" refers to a means of presenting stored text or data in a way that allows users to visually confirm it.

[0871] "Emotional analysis methods" are means of performing analysis to infer the emotions of users from collected data.

[0872] A "proposal method" is a means of suggesting products and services optimized for the user based on analyzed emotional information.

[0873] To implement this invention, first, the terminal functions as a data collection means and acquires data about the user's activities. This data includes image data, schedule data, and transaction history data. This data is appropriately encrypted and then transferred to a server.

[0874] The server is equipped with a generation mechanism for analyzing the received data. Here, it uses the image recognition library OpenCV to recognize the user's facial expressions from images, combines them with behavioral data and transaction history, and analyzes emotions. Emotion analysis is used for the analysis, and an emotion engine is utilized to infer the user's emotions.

[0875] Based on the analyzed emotional information, the server executes a suggestion mechanism to propose products and services. Using a generative AI model, it automatically generates appropriate natural language sentences and notifies the user. For example, based on the user's facial expressions and purchase history, it can generate a prompt sentence such as, "The customer smiled while sampling product XYZ. What kind of offer would you like to propose?"

[0876] Furthermore, the generated text is organized chronologically using the server's storage system. Users can view this information through the display system and provide feedback. User feedback is used to improve the algorithm through the system's learning mechanism. As a result, the system continuously improves the accuracy of user sentiment analysis and evolves to provide more refined product recommendations.

[0877] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0878] Step 1:

[0879] The terminal acquires user image data, activity plan data, and transaction history data as data collection tools. This data is temporarily stored on the terminal and transferred to the server via a communication module. The input is raw data related to the user's daily activities, and the output is encrypted data sent to the server.

[0880] Step 2:

[0881] The server decrypts the received encrypted data and analyzes the user's facial expressions from the image data using an image recognition library (e.g., OpenCV). The input is encrypted user data, and the output is analyzed information about the user's facial expressions. The server evaluates the analyzed facial expressions using emotion analysis tools and generates an estimated emotion.

[0882] Step 3:

[0883] The server uses a generative AI model to generate natural language-based suggestion sentences based on inferred sentiment information. An example of this prompt sentence is: "The customer smiled while sampling product XYZ. What kind of offer would you suggest?" The input is inferred sentiment data, and the output is a suggestion sentence for the user.

[0884] Step 4:

[0885] The server stores the generated suggestion text in a database in chronological order using a storage mechanism and notifies the user's terminal via a display mechanism. The input is the generated text, and the output is the notification message received on the user's terminal.

[0886] Step 5:

[0887] Users select products and services based on suggestions received through their devices and send their feedback to the server. The input is the user's feedback on the suggestions, and the output is the feedback data used for improvement.

[0888] Step 6:

[0889] The server uses user feedback as a learning tool to adjust the parameters of its sentence generation algorithm and sentiment analysis tools. This improves the accuracy of sentiment evaluation and suggestions in subsequent uses. The input is feedback data, and the output is the improved algorithm and updated sentiment analysis model.

[0890] 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.

[0891] 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 those described above. 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 shown 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.

[0892] In the above embodiment, an example was given in which the 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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."

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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 as being incorporated by reference.

[0911] The following is further disclosed regarding the embodiments described above.

[0912] (Claim 1)

[0913] A generation device that analyzes digital information related to a specific date obtained from a data collection device and generates text in natural language form,

[0914] A storage device for saving the generated text in chronological order,

[0915] A display device that shows saved text to the user,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, further comprising a learning device that improves the text generation algorithm of the generation device using user feedback.

[0919] (Claim 3)

[0920] The system according to claim 1, characterized in that the acquired digital information includes image information, activity plan information, and purchase history information.

[0921] "Example 1"

[0922] (Claim 1)

[0923] A process means for analyzing digital information collected by data acquisition means and generating natural language text using a generative AI model,

[0924] A data recording means for recording generated text in chronological order,

[0925] A display means that presents recorded text to the user via an information output means,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, further comprising a learning means for improving the text generation algorithm by the process means based on feedback from users.

[0929] (Claim 3)

[0930] The system according to claim 1, characterized in that the acquired digital information includes visual information, schedule information, and purchase history information.

[0931] "Application Example 1"

[0932] (Claim 1)

[0933] A generation device that analyzes digital information related to a specific date obtained from a data collection device and generates text in natural language form,

[0934] A storage device for saving the generated text in chronological order,

[0935] A display device that shows saved text to the user,

[0936] A content distribution device that automatically delivers content based on user experience,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, further comprising a learning device that improves the text generation algorithm of the generation device using user feedback.

[0940] (Claim 3)

[0941] The system according to claim 1, characterized in that the acquired digital information includes image information, planned activity information, and purchase history information, and further generates content based on the user's experience.

[0942] "Example 2 of combining an emotion engine"

[0943] (Claim 1)

[0944] An analysis method that analyzes data related to daily activities obtained through data collection means and infers the user's emotions,

[0945] A generation method for generating natural language text based on inferred emotion data and activity data,

[0946] A storage method for organizing and saving generated text in chronological order,

[0947] A display means for visually presenting saved text to the user,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, further comprising a learning means for learning the algorithm of the generation means in order to improve the quality of the generated text by reflecting user feedback.

[0951] (Claim 3)

[0952] The system according to claim 1, wherein the data acquired by the analysis means includes image data, scheduled data, and recorded data.

[0953] "Application example 2 when combining with an emotional engine"

[0954] (Claim 1)

[0955] A generation means that analyzes data obtained from a data collection means and generates sentences in natural language form,

[0956] A storage means for saving the generated sentences in chronological order,

[0957] A display means for displaying saved text to the user,

[0958] A means of sentiment analysis that analyzes user behavior and infers emotions,

[0959] A proposal method that suggests products and services based on inferred emotions,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, further comprising a learning means for improving the sentence generation algorithm by the generation means using feedback from users.

[0963] (Claim 3)

[0964] The system according to claim 1, characterized in that the acquired data includes image data, schedule data, and transaction history data. [Explanation of Symbols]

[0965] 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 generation device that analyzes digital information related to a specific date obtained from a data collection device and generates text in natural language form, A storage device for saving the generated text in chronological order, A display device that shows saved text to the user, A system that includes this.

2. The system according to claim 1, further comprising a learning device that improves the text generation algorithm of the generation device using user feedback.

3. The system according to claim 1, characterized in that the acquired digital information includes image information, activity plan information, and purchase history information.

Images