System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring location information, health care information, payment information, search information, and weather information; means for automatically generating a life record of a user based on the acquired information; means for the user to view, add, and modify the life record; means for converting the automatically generated life record into a diary format; and means for analyzing the life record and presenting an idea of life improvement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, an increasing number of people desire to improve the quality of their lives, but many are unsure of what specific improvements to make. Many people also find it difficult to manually write a diary reflecting on their daily experiences. Seniors and people with memory impairments face particular challenges in improving their lifestyles because they lack an easy way to review their actions. This invention aims to automatically generate a lifestyle record based on a variety of data obtained from the user's smartphone, allowing users to effortlessly reflect on their lifestyle and obtain hints for improvement. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes the following means. First, it provides means for acquiring location information, health care information, payment information, search information, and weather information. This allows for the collection of a variety of information related to the user's daily life. Next, it provides means for automatically generating a user's life log based on the collected information, allowing the user to view, add to, and modify the log. It also provides means for converting the automatically generated life log into a diary format, allowing the user to easily create a diary. Finally, it includes means for analyzing the life log and presenting ideas for improving their lifestyle. This configuration allows the user to effortlessly reflect on their life and identify specific ways to improve it.

[0006] "Location information" is data that indicates a user's current location or past movement history using technologies such as GPS, Wi-Fi, and cell towers installed in the user's smartphone.

[0007] "Healthcare information" refers to data about a user's health status, such as the number of steps taken, heart rate, sleep time, and calories burned, collected through smartphone sensors and related applications.

[0008] "Payment information" refers to transaction data when a user makes a payment using a smartphone, and includes information such as the store name, purchase amount, and purchase date and time.

[0009] "Search Information" refers to historical data about searches conducted by a user using an internet browser or search engine, including search terms and the time of the search.

[0010] "Weather information" refers to data about the weather when a user is at a specific location, including information such as temperature, precipitation, and wind speed.

[0011] "Life Log" is a detailed log of a user's daily life that is constructed by combining location information, health information, payment information, search information, and weather information collected from the user's smartphone.

[0012] "Automatic generation" refers to a process that is carried out automatically by a computer without human intervention based on data collected by the system from the user.

[0013] The "diary format" is a written format that describes events and activities that occurred on a specific day in chronological order, and users can freely add to or edit entries.

[0014] "Ideas for improving lifestyle" are specific suggestions for improving the quality of a user's life, such as health management and efficient time use, based on the analysis of the user's life log. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention relates to a system that acquires location information, healthcare information, payment information, search information, and weather information and automatically generates a user's life log. Specific embodiments for implementing this system will be described below.

[0037] System configuration

[0038] This system mainly consists of three components: a server, a device (e.g., a smartphone), and a user. The device collects various data about the user's life, and the server analyzes the data to generate a life record and provide it to the user.

[0039] Program processing

[0040] 1. Acquiring and recording location information

[0041] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc., which records the user's current location and past movement history. The acquired location information is saved in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[0042] 2. Acquisition and analysis of healthcare information

[0043] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[0044] 3. Acquisition and recording of payment information

[0045] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[0046] 4. Acquisition and analysis of search information

[0047] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[0048] 5. Obtaining and linking weather information

[0049] The server uses a weather information API to obtain weather data (temperature, probability of precipitation, etc.) based on each user's location information. The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[0050] Automatic generation and conversion to diary format

[0051] The server automatically generates a daily life log based on the collected and analyzed data. Users can view this log through the application and make additions or corrections as needed. A function is also provided to convert the automatically generated life log into a diary format for easier viewing by users.

[0052] Presenting ideas for improving lifestyles

[0053] The server's AI analyzes the generated life log and suggests lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, it can provide specific suggestions to users who are not getting enough exercise, such as "walking at least 8,000 steps three days a week."

[0054] Specific examples

[0055] Day 1

[0056] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0057] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[0058] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0059] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0060] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[0061] Such a system allows users to reflect on their lives and find concrete ways to improve them.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The device periodically obtains the user's current location information using the GPS sensor, Wi-Fi location information, and cell tower data. The obtained location information is temporarily stored in local storage in latitude and longitude format.

[0065] Step 2:

[0066] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[0067] Step 3:

[0068] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[0069] Step 4:

[0070] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor), and stores the collected data in local storage in the form of steps, heart rate, sleep time, etc.

[0071] Step 5:

[0072] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0073] Step 6:

[0074] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[0075] Step 7:

[0076] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app. The terminal receives a real-time notification at the time of payment, which triggers data collection. The collected data is saved in local storage.

[0077] Step 8:

[0078] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[0079] Step 9:

[0080] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[0081] Step 10:

[0082] The device collects browser and search engine history data (search keywords, date and time), and the collected search data is stored in local storage.

[0083] Step 11:

[0084] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0085] Step 12:

[0086] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[0087] Step 13:

[0088] The server uses a weather information API based on each user's location information to periodically obtain weather data (temperature, probability of precipitation, etc.) for the relevant area.

[0089] Step 14:

[0090] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[0091] Step 15:

[0092] The server automatically generates a daily life log from the collected and analyzed data, and outputs the log in diary format for viewing by the user.

[0093] Step 16:

[0094] Users can view the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved on the server in real time.

[0095] Step 17:

[0096] The server's AI analyzes the generated lifestyle records and generates lifestyle improvement ideas based on the user's behavioral patterns and health condition.

[0097] Step 18:

[0098] Based on the analysis results, the server notifies the user of lifestyle improvement suggestions (for example, "Aim to take 8,000 steps or more three days a week.") Notifications are sent via the application.

[0099] Example 1

[0100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0101] Conventional lifestyle record systems only collect individual pieces of information and lack the functionality to integrate and analyze them comprehensively, making it difficult to provide specific suggestions that directly lead to improvements in users' lifestyles. Furthermore, there are limited ways to convert the collected information into a format that users can easily view and edit. This creates the challenge of preventing users from gaining a detailed understanding of their own lifestyles and making specific improvements.

[0102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0103] In this invention, the server includes means for acquiring location information, health status information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, and means for the user to view, add to, and modify the life log. This makes it possible to integrate various information that has previously been collected individually and perform comprehensive analysis. It also makes it possible to provide specific lifestyle improvement suggestions to the user, and converting the log into a diary format makes it easier to view and modify.

[0104] "Location Information" means data that indicates a user's current geographic location and movement history obtained using technologies such as GPS, Wi-Fi, and cell towers.

[0105] "Health Information" refers to data related to your physical health collected from built-in sensors, such as pedometers, heart rate monitors, and sleep trackers, and from health apps.

[0106] "Payment information" refers to data related to the purchase history (store name, purchase amount, purchase date and time) made by the user through a payment app, etc.

[0107] "Search Information" refers to data regarding the search keywords and the date and time of the search performed by a user using a browser or search engine.

[0108] "Weather information" refers to weather-related data such as temperature and precipitation probability at a specific location, obtained through a weather information API.

[0109] "Life Log" is a record of a user's daily activities and status that is automatically generated based on location information, health status information, payment information, search information, and weather information.

[0110] "Reverse geocoding" is a technology that converts location information such as latitude and longitude into specific addresses or place names.

[0111] A "prompt sentence" is an instruction sentence that a generative AI model uses as input when generating natural language.

[0112] The present invention relates to a system that automatically generates a user's life log by integrating location information, health status information, payment information, search information, and weather information. This system mainly consists of three entities: a server, a terminal (e.g., a smartphone), and a user. Specific embodiments for implementing this system are described below.

[0113] System configuration

[0114] The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. Specifically, the device collects data using GPS sensors, healthcare apps, payment apps, browsers, etc. and periodically sends it to the server. The server processes the received data and runs software to automatically generate the life record.

[0115] Acquiring and recording location information

[0116] The device periodically acquires location information using GPS sensors, Wi-Fi, and cell towers. This information is stored in the device's local storage and periodically sent to a server. The server then sends the received location data to a reverse geocoding API, which converts it into a specific address or place name. The converted data is then recorded in a database.

[0117] Acquisition and analysis of healthcare information

[0118] The device uses built-in sensors and a health app to collect health status information from the pedometer, heart rate monitor, and sleep tracker. This data is stored in local storage and periodically sent to a server. The server analyzes the received data and records it in a database. Analysis methods include averaging the data and filtering outliers.

[0119] Acquisition and recording of payment information

[0120] The device collects purchase history data from the payment app. This information is stored in local storage and periodically sent to the server. The server analyzes the received payment data to automatically classify spending categories and understand spending patterns. The analysis results are recorded in a database.

[0121] Acquiring and analyzing search information

[0122] The device collects browser and search engine history data (search keywords, date and time). This information is stored in local storage and periodically sent to the server. The server analyzes the received search data to help understand the user's interests and needs. It also provides a content recommendation function based on this data.

[0123] Obtaining and linking weather information

[0124] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the location information and reflected in the user's daily life record.

[0125] Automatic generation and conversion to diary format

[0126] The server runs a program to automatically generate daily life logs based on the collected and analyzed data. This program uses a generative AI model and generates a life log in natural language by inputting prompt sentences. The generated life log is converted into a diary format and displayed in the application in a format that is easy for users to view.

[0127] Presenting ideas for improving lifestyles

[0128] The server's AI analyzes the generated lifestyle records and makes suggestions for lifestyle improvements based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise will receive specific suggestions such as "walk at least 8,000 steps three days a week." This allows users to reflect on their lifestyle in detail and identify areas for improvement.

[0129] Specific examples

[0130] The following specific scenarios are possible:

[0131] The device collects location information when the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0132] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[0133] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0134] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0135] The server generates a lifestyle record based on this information and makes suggestions for improving your lifestyle, such as, "Today you took 7,000 steps. You're 1,000 steps away from your goal of 8,000 steps."

[0136] As described above, the present invention allows users to record their own lifestyle in detail and obtain specific methods for improving their lifestyle.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1: Obtain and record location information

[0139] The device acquires location information using the GPS sensor, Wi-Fi, and cell towers. The acquired location information is saved in the device's local storage. Specifically, the GPS sensor is activated every 60 seconds, and the acquired latitude and longitude information is written to the local storage. Every certain period (for example, every hour), the collected location data is compiled in JSON format and sent to the server using the HTTPS protocol.

[0140] Input: Latitude and longitude information obtained from the GPS sensor

[0141] Output: Location information stored in the device's local storage and location packets sent to the server

[0142] Step 2: Reverse geocoding and database recording

[0143] The server sends the received location information to a reverse geocoding API, which converts the latitude and longitude into specific addresses and place names. The information returned by the API is analyzed and recorded in a database. Specifically, multiple latitudes and longitudes are requested from the API, and the corresponding addresses and place names are obtained. The obtained geographic information is written to the database.

[0144] Input: Location information packet (latitude and longitude) sent from the device

[0145] Output: Specific geographic information recorded in a database

[0146] Step 3: Capture and record healthcare information

[0147] The device uses built-in sensors and the healthcare app to collect health status information such as heart rate, number of steps, and sleep time. The collected data is stored in the device's local storage. Specifically, the device queries the healthcare API every 30 minutes and adds the obtained data to the local storage. The collected health information is then sent to the server at regular intervals (for example, once a day).

[0148] Input: Health status information obtained from the health app or built-in sensors

[0149] Output: Health status information stored in the device's local storage and health information packets sent to the server

[0150] Step 4: Analyze health information and record it in the database

[0151] The server analyzes the received health status information and records it in a database. Specifically, it normalizes the received data, filters out outliers, and then aggregates the data for each user and writes it to the database.

[0152] Input: Health information packet sent from the device

[0153] Output: Health status information recorded in a database

[0154] Step 5: Capture and record payment information

[0155] The device collects purchase history data from the payment app and stores it in local storage. Specifically, it receives a notification every time the payment app updates the purchase history and writes the details to local storage. The collected data is sent to the server at regular intervals (for example, once a day).

[0156] Input: Purchase history data from payment app

[0157] Output: Payment information stored in the device's local storage and the payment information packet sent to the server

[0158] Step 6: Analyze payment information and record it in the database

[0159] The server analyzes the received payment information and records it in a database. The analysis method involves automatically classifying expenditure categories and understanding expenditure patterns. The analysis results are written to the database.

[0160] Input: Payment information packet sent from the terminal

[0161] Output: Spending pattern information recorded in a database

[0162] Step 7: Capture and record search information

[0163] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. Specifically, every time a user performs a search, the information is recorded in local storage. The collected search data is sent to the server at regular intervals (for example, once a day).

[0164] Input: Search history data from browsers and search engines

[0165] Output: Search information stored in the device's local storage and search information packets sent to the server

[0166] Step 8: Analyze search results and record database information

[0167] The server analyzes the received search information and records it in a database. The analysis method involves analyzing the frequency of search keywords and trends in search time periods. The analysis results are written to the database.

[0168] Input: Search information packet sent from the terminal

[0169] Output: Search interest information recorded in a database

[0170] Step 9: Retrieving and correlating weather information

[0171] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is associated with the location information and recorded in a database.

[0172] Input: Location information recorded on the server

[0173] Output: Weather data obtained from the weather information API, weather information associated with location information recorded in the database

[0174] Step 10: Automatic generation and conversion to diary format

[0175] The server automatically generates daily life logs based on the collected and analyzed data. Using a generative AI model, the server generates a life log in natural language by inputting prompts. The generated life log is converted into a diary format and displayed for users to view, add to, and edit through the application.

[0176] Input: Various information recorded in the database (location information, health information, payment information, search information, weather information)

[0177] Output: Automatically generated diary-formatted daily records

[0178] Step 11: Present ideas for improving your life

[0179] The server's AI analyzes the generated life log and presents lifestyle improvement suggestions based on the user's behavioral patterns and health status. Specific suggestions are displayed to the user as notifications.

[0180] Input: Automatically generated life log

[0181] Output: Suggestions for improving the user's lifestyle

[0182] (Application example 1)

[0183] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0184] In modern life, a variety of information is generated, including location information, healthcare information, payment information, search information, and weather information. However, there are only a limited number of systems that integrate this information to provide personalized services. In particular, food delivery services lack suggestions tailored to the user's health condition and lifestyle. This makes it difficult for users to select the optimal meal for their health condition and lifestyle.

[0185] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0186] In this invention, the server includes means for acquiring location information, health care information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, means for the user to view, add to, and modify the life log, means for converting the automatically generated life log into a diary format, means for analyzing the life log and presenting ideas for improving lifestyle, means for providing a personalized food delivery service to the user based on the acquired information, means for proposing a meal plan tailored to the user's health condition based on the acquired health care information, and means for recommending optimal meal options based on the acquired location information and weather information. This allows the user to receive personalized meal suggestions tailored to their health condition and lifestyle.

[0187] "Location information" is data that indicates a user's current location or history, and is obtained using technologies such as GPS, Wi-Fi, and cell towers.

[0188] "Health information" refers to data that indicates a user's health and activity status, and is obtained through built-in sensors and applications, such as pedometers, heart rate monitors, and sleep trackers.

[0189] "Payment information" is data that indicates a user's purchase history and expenditures, and includes information such as the store name, purchase amount, and purchase date and time.

[0190] "Search information" is data that indicates the search keywords and browsing history used by a user on the Internet, and is obtained through a browser or search engine.

[0191] "Weather information" is data that indicates the weather conditions in a specific area and time, and includes elements such as temperature, probability of precipitation, and wind speed.

[0192] "Life Log" is a record of a user's daily life that is created by integrating location information, health information, payment information, search information, and weather information.

[0193] The "diary format" is a format in which daily events and data are written in chronological order to make the record of daily life easier for users to understand.

[0194] A "food delivery service" is a service that delivers meals ordered online by a user to a specified location.

[0195] A "meal plan" is a meal suggestion that takes into account the user's health condition and nutritional balance.

[0196] "Meal selection" refers to the act or process of a user selecting the optimal meal based on specific criteria (health status, location, weather, etc.).

[0197] This invention provides a system that collects a user's location information, healthcare information, payment information, search information, and weather information, and automatically generates a user's life log based on this data. The system is mainly composed of three entities: a server, a terminal (e.g., a smartphone), and the user.

[0198] Acquiring and recording location information

[0199] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc. The acquired location information is stored in local storage and periodically sent to the server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[0200] Acquisition and analysis of healthcare information

[0201] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[0202] Acquisition and recording of payment information

[0203] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[0204] Acquiring and analyzing search information

[0205] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[0206] Obtaining and linking weather information

[0207] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[0208] Providing personalized food delivery services

[0209] Based on the acquired information, the system can provide users with personalized food delivery services, such as recommending menu items from nearby restaurants based on the user's current location and taking weather information into account to suggest suitable meal choices.

[0210] Meal plan suggestions based on your health status

[0211] Based on the acquired health information, the system will propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[0212] Hardware and software used

[0213] The following hardware and software are used to realize this system.

[0214] Hardware: Smartphone (iOS / Android compatible), smartwatch (health data acquisition)

[0215] Software: Mobile applications (Swift, Kotlin), server side (Node.js, Python), databases (MongoDB, PostgreSQL)

[0216] Specific examples

[0217] 1. A user searches for "healthy restaurants near me."

[0218] 2. The system will recommend the best restaurant and menu based on the user's current location and health information.

[0219] 3. Example prompt: "Show restaurant suggestions based on the user's location and recommend calorie-friendly menu items based on their health data."

[0220] With the above configuration, users can receive a personalized food delivery service that is best suited to their health condition and lifestyle.

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1: Obtain and record location information

[0223] The device periodically acquires the user's location information using technologies such as GPS, Wi-Fi, and cell towers. The acquired location information is stored in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[0224] Input: User location information (GPS data)

[0225] Data processing: saving to local storage, using reverse geocoding API

[0226] Output: Specific address or place name

[0227] Step 2: Acquire and analyze healthcare information

[0228] The device collects health data (such as steps taken, heart rate, and sleep time) from the health app and built-in sensors. The collected data is stored in local storage and periodically sent to a server. The server analyzes the received health data, records it in a database, and updates the user's daily record.

[0229] Input: User's health information (step count, heart rate, sleep time, etc.)

[0230] Data processing: saving to local storage, data analysis

[0231] Output: Analysis results, recording to database

[0232] Step 3: Capture and record payment information

[0233] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[0234] Input: User's payment information (purchase history data)

[0235] Data processing: saving to local storage, data analysis

[0236] Output: Analysis results, user spending patterns, records in database

[0237] Step 4: Obtaining and analyzing search information

[0238] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[0239] Input: User search information (search keywords, date and time)

[0240] Data processing: saving to local storage, data analysis

[0241] Output: Analysis results, user interests and needs

[0242] Step 5: Obtaining and Correlating Weather Information

[0243] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[0244] Input: User location information, weather information API data

[0245] Data processing: Use of weather information API, association with location information, recording in database

[0246] Output: Weather data, recorded in database

[0247] Step 6: Offer a personalized food delivery service

[0248] The server uses the collected information to provide users with personalized food delivery services, and the device recommends menu items from nearby restaurants based on the user's current location and takes weather information into account to suggest appropriate meal choices.

[0249] Input: User location information, payment information, weather information, health information

[0250] Data processing: information integration, application of recommendation algorithms

[0251] Output: Personalized menu recommendations, suggesting appropriate meal choices

[0252] Step 7: Suggested meal plan based on health status

[0253] The server uses the collected health information to propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[0254] Input: User's healthcare information

[0255] Data processing: analysis of health information, generation of meal plans

[0256] Output: Meal plan suggestions based on health status

[0257] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0258] The present invention combines a system that automatically generates a user's life log based on location information, healthcare information, payment information, search information, and weather information with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system are described below.

[0259] System configuration

[0260] This system mainly consists of four components: a server, a device (e.g., a smartphone), a user, and an emotion engine. The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. The emotion engine also recognizes the user's emotional state and uses that information to personalize the lifestyle improvement ideas provided by the server.

[0261] Program processing

[0262] 1. Acquiring and recording location information

[0263] The device periodically acquires the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and temporarily stores it in local storage. At predetermined intervals, the acquired location information is sent to the server. The server then converts the received location information into specific addresses and facility names using a reverse geocoding API and stores them in a database.

[0264] 2. Acquisition and analysis of healthcare information

[0265] The device collects the user's health data from the healthcare app and built-in sensors and stores it in local storage. At regular intervals, the collected health data is sent to a server. The server analyzes the received health data, detects abnormal values ​​and identifies trends, records them in a database, and updates the user's lifestyle record.

[0266] 3. Acquisition and recording of payment information

[0267] The device collects purchase history data from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data to understand purchasing patterns and spending trends, and records them in a database.

[0268] 4. Acquisition and analysis of search information

[0269] The device collects browser and search engine history data and stores it in local storage. At regular intervals, the collected search data is sent to a server. The server analyzes the received search data to obtain information about the user's interests and needs, and records the analysis results in a database.

[0270] 5. Obtaining and linking weather information

[0271] The server periodically retrieves weather data based on each user's location using a weather information API. The retrieved weather data is stored in a database, associated with the user's location and a timestamp.

[0272] 6. Emotion recognition

[0273] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. This emotional data is also sent to the server.

[0274] 7. Automatic generation and conversion to diary format

[0275] The server automatically generates a daily life log based on the collected and analyzed data. The generated life log is output in diary format and can be viewed by the user. The user can view the automatically generated life log through the application and make additions or corrections as needed. Additions and corrections are saved on the server in real time.

[0276] 8. Presenting ideas for improving life

[0277] The server's AI analyzes the generated life log and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise may be advised to aim to walk more than 8,000 steps three days a week. Furthermore, depending on the user's emotional state, it can also suggest relaxation techniques and positive activities to reduce stress.

[0278] Specific examples

[0279] Day 1

[0280] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0281] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[0282] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0283] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0284] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[0285] The device uses an emotion engine to analyze the user's emotional state from their voice and facial expressions and transmits the results to the server.

[0286] The server analyzes the user's emotional data and provides emotion-based advice such as, "You seem to be feeling stressed lately. Why not try 15 minutes of relaxation?"

[0287] Such a system would allow users to gain a deeper understanding of their lives, learn concrete ways to improve them, and receive advice that takes into account their emotional state.

[0288] The processing flow will be explained below.

[0289] Step 1:

[0290] The device periodically obtains the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and the obtained location information is temporarily stored in local storage in latitude and longitude format.

[0291] Step 2:

[0292] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[0293] Step 3:

[0294] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[0295] Step 4:

[0296] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor). The collected data (e.g., number of steps, heart rate, sleep time) is stored in local storage.

[0297] Step 5:

[0298] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0299] Step 6:

[0300] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[0301] Step 7:

[0302] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. The terminal receives a real-time notification at the time of payment, which triggers data collection.

[0303] Step 8:

[0304] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[0305] Step 9:

[0306] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[0307] Step 10:

[0308] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage.

[0309] Step 11:

[0310] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0311] Step 12:

[0312] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[0313] Step 13:

[0314] The server uses a weather information API based on each user's location information to periodically obtain relevant weather data (temperature, probability of precipitation, etc.).

[0315] Step 14:

[0316] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[0317] Step 15:

[0318] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. The emotion data is stored in local storage.

[0319] Step 16:

[0320] The device also transmits emotion data to the server at regular intervals, using a secure communication protocol such as HTTPS.

[0321] Step 17:

[0322] The server automatically generates daily life records based on the life record data and emotion data, and outputs the generated life records in diary format for users to view.

[0323] Step 18:

[0324] Users can check the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved on the server in real time.

[0325] Step 19:

[0326] Based on the collected and analyzed data, the server's AI analyzes the user's behavioral patterns and health status, and generates ideas for improving lifestyles.

[0327] Step 20:

[0328] The server notifies the user of the generated lifestyle improvement ideas. For example, if the user's step count is insufficient, the server will suggest, "Today's step count was 7,000. You are 1,000 steps away from your goal of 8,000." The server will also suggest relaxation techniques and positive activities to reduce stress based on the user's emotional state.

[0329] Example 2

[0330] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0331] While existing technologies exist for automatically generating user life logs, they simply collect and record data and lack the flexibility to include specific suggestions for improving the user's emotional state or lifestyle. Furthermore, they lack a mechanism for providing personalized suggestions to improve quality of life. This makes it difficult for users to gain a deeper understanding of their own lives and identify specific behavioral improvements.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, health information, purchase information, search information, and weather information, means for automatically generating a user's life record based on the acquired information, and means for recognizing the user's emotional state and storing the information in a database. This allows the user to understand their own life record in detail and receive specific lifestyle improvement suggestions based on their emotional state.

[0333] "Location information" is data that indicates a user's current physical location and is obtained from GPS sensors, Wi-Fi, cell tower data, etc.

[0334] "Health information" is data that indicates the user's health condition and physical activity, and includes information such as the number of steps taken, heart rate, and sleep time.

[0335] "Purchase information" refers to transaction data when a user purchases a product or service, and includes the purchase date and time, amount, and store information.

[0336] "Search information" is data that indicates a user's interests and needs based on the search queries and browsing history that the user performs on the Internet.

[0337] "Weather information" refers to environmental data such as the weather, temperature, and humidity at the user's location, and is information obtained from weather APIs, etc.

[0338] "Life records" are data that show the activities and trends of a user's daily life, integrating the user's location information, health information, purchase information, search information, and weather information.

[0339] "Emotional state" is data that indicates a user's psychological or emotional state, and is information analyzed from voice, facial expressions, text input, etc.

[0340] A "database" is an information system for systematically storing, managing, and analyzing data such as a user's daily records and emotional state.

[0341] "Lifestyle Improvement Suggestions" are specific suggestions and advice based on the user's lifestyle records and emotional state, with the aim of improving the user's quality of life.

[0342] The present invention combines a system that automatically generates a user's life log based on location information, health information, purchase information, search information, and weather information with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system are described below.

[0343] System configuration

[0344] This system mainly consists of four components: a server, a terminal (e.g., a smart device), a user, and an emotion engine. The terminal collects various data about the user's life, and the server analyzes the data to generate a life record and provide it to the user. The emotion engine also recognizes the user's emotional state and uses that information to personalize the life improvement ideas provided by the server.

[0345] Program processing

[0346] Acquiring and recording location information

[0347] The device uses GPS sensors, Wi-Fi, and cell tower data to obtain the user's current location information and temporarily stores it in local storage. At regular intervals, the device sends the obtained location information to the server. The server then uses a reverse geocoding API to convert the received location information into specific addresses and facility names and stores them in a database.

[0348] Acquisition and analysis of health information

[0349] The device collects the user's health data from the healthcare app and built-in sensors and stores it in local storage. At regular intervals, the device sends the collected health data to a server. The server analyzes the received health data, detects abnormal values ​​and identifies trends, records them in a database, and updates the user's lifestyle record.

[0350] Acquisition and recording of purchasing information

[0351] The device collects purchase history data from the payment app and stores it in local storage. At regular intervals, the device sends the collected payment data to the server. The server analyzes the received payment data, identifies purchasing patterns and spending trends, and records them in a database.

[0352] Acquiring and analyzing search information

[0353] The device collects browser and search engine history data and stores it in local storage. At regular intervals, the device sends the collected search data to the server. The server analyzes the received search data, obtains information about the user's interests and needs, and records the analysis results in a database.

[0354] Obtaining and linking weather information

[0355] The server periodically retrieves relevant weather data using a weather information API based on each user's location information. The retrieved weather data is stored in a database, associated with the user's location information and a timestamp.

[0356] emotion recognition

[0357] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. This emotional data is also sent to the server.

[0358] Automatic generation and conversion to journal format

[0359] The server automatically generates a daily life record based on the collected and analyzed data. The generated life record is output in a diary format that can be viewed by the user. The user can view the automatically generated life record through the application and make additions or corrections as needed. Additions and corrections are saved on the server in real time.

[0360] Presenting ideas for improving lifestyles

[0361] The server's AI analyzes the generated life log and emotional data and makes lifestyle improvement suggestions based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise might be advised to aim to walk more than 8,000 steps three days a week. Furthermore, depending on the user's emotional state, it can also suggest relaxation techniques and positive activities to reduce stress.

[0362] Specific examples

[0363] Day 1

[0364] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0365] The device collects data from the health app, showing that the user walked 7,000 steps and slept for seven hours.

[0366] The terminal collects purchasing information when a user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0367] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0368] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[0369] The device uses an emotion engine to analyze the user's emotional state from their voice and facial expressions and transmits the results to the server.

[0370] The server analyzes the user's emotional data and provides emotion-based advice such as, "You seem to be feeling stressed lately. Why not try 15 minutes of relaxation?"

[0371] Such a system would allow users to gain a deeper understanding of their lives, learn concrete ways to improve them, and receive advice that takes into account their emotional state.

[0372] Example prompts for generative AI models

[0373] "Generate a user's life record based on location, health, purchase, search, and weather information."

[0374] "Analyze users' emotions and provide emotion-based advice for improving their lives."

[0375] "Convert the user's daily activity record into a diary format and provide it via push notification."

[0376] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0377] Step 1:

[0378] The device obtains the user's current location information using GPS sensors, Wi-Fi, and cell tower data. This allows the device to determine the user's location and temporarily store the obtained location information in local storage. For example, the device can determine the user's specific location using latitude and longitude information obtained from the GPS sensor and Wi-Fi connection information. The input is location data from the sensor, and the output is location information stored in local storage.

[0379] Step 2:

[0380] The device sends the acquired location information to the server at a predetermined interval (e.g., 30 minutes). Specifically, the location information is uploaded to the server at regular intervals using batch processing. The input is the location information saved in the local storage, and the output is the location information sent to the server.

[0381] Step 3:

[0382] The server converts the received location information into a specific address or facility name using a reverse geocoding API (e.g., map information API). In this conversion process, the coordinate information is input into the API to obtain detailed address information. The input is the transmitted location information, and the output is the converted address or facility name.

[0383] Step 4:

[0384] The server saves the converted address and facility name in a database. At this time, the location information is saved with a timestamp, making it possible to track where the user was at a later date. The input is the converted address data, and the output is the information saved in the database.

[0385] Step 5:

[0386] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate sensor). This data includes the number of steps, heart rate, sleep time, etc. The input is the health data measured by the sensors, and the output is the health data stored in local storage.

[0387] Step 6:

[0388] The device sends the collected health data to the server at a predetermined interval (e.g., once a day). The input is the health data stored in the local storage, and the output is the health data sent to the server.

[0389] Step 7:

[0390] The server analyzes the received health data to detect outliers and identify trends. For example, it uses an AI model to detect sudden fluctuations in heart rate or lack of sleep. The input is the transmitted health data, and the output is the analysis results.

[0391] Step 8:

[0392] The server records the analysis results in a database and updates the user's life record. This update process continuously stores daily health data for future trend analysis. The input is the analysis results, and the output is the updated life record.

[0393] Step 9:

[0394] The terminal collects purchase history data from a payment app (e.g., an electronic payment app). This includes the purchase date and time, amount, and store information. The input is transaction data from the payment app, and the output is purchase information stored in local storage.

[0395] Step 10:

[0396] The terminal sends the collected purchasing data to the server at regular intervals (e.g., one week). The input is the purchasing data stored in the local storage, and the output is the purchasing data sent to the server.

[0397] Step 11:

[0398] The server analyzes the received purchase data to understand purchasing patterns and spending trends. For example, it uses machine learning algorithms to detect specific consumption habits and wasteful spending. The input is the submitted purchase data, and the output is the analysis results.

[0399] Step 12:

[0400] The server records the analysis results in a database and updates the user's purchase history. The input is the analysis results, and the output is the updated purchase history data.

[0401] Step 13:

[0402] The device collects browser and search engine history data, including user search queries and browsing history. The input is the history data from the browser and search engine, and the output is search information stored in local storage.

[0403] Step 14:

[0404] The device sends the search data collected at regular intervals (e.g., once a month) to the server. The input is the search data stored in the local storage, and the output is the search data sent to the server.

[0405] Step 15:

[0406] The server analyzes the received search data to obtain information about the user's interests and needs. For example, it uses natural language processing to analyze the search query and identify specific topics or issues of interest. The input is the submitted search data, and the output is the analysis results.

[0407] Step 16:

[0408] The server records the analysis results in a database and updates the user's interests and needs. The input is the analysis results and the output is the updated interest data.

[0409] Step 17:

[0410] The server periodically obtains the relevant weather data using a weather information API based on each user's location information. The input is the user's location information, and the output is the obtained weather data.

[0411] Step 18:

[0412] The server associates the acquired weather data with location information and a timestamp and stores them in a database. The input is the acquired weather data, and the output is the weather information stored in the database.

[0413] Step 19:

[0414] The device analyzes the user's emotional state through an emotion engine based on the user's voice, facial expressions, and text input. For example, it analyzes the tone of the voice and evaluates the emotion using facial expression recognition software. The input is voice and facial expression data, and the output is analyzed emotional data.

[0415] Step 20:

[0416] The device sends the analyzed emotion data to the server. The input is the data analyzed by the emotion engine, and the output is the emotion data sent to the server.

[0417] Step 21:

[0418] The server stores the received emotion data in a database. The input is the transmitted emotion data, and the output is the emotion information stored in the database.

[0419] Step 22:

[0420] The server automatically generates a daily life log based on the collected and analyzed data. It uses a generative AI model to integrate multiple data sources and record the user's daily activities in detail. The input is multiple data sources (location information, health information, purchase information, search information, weather information, and emotion data), and the output is an automatically generated life log.

[0421] Step 23:

[0422] The server converts the generated life records into a diary format and outputs them in a viewable format for users. The input is the automatically generated life records, and the output is the life records converted into diary format.

[0423] Step 24:

[0424] The user can view the automatically generated life log through the application and add or modify it as needed. The input is the life log converted into a diary format, and the output is the added or modified record.

[0425] Step 25:

[0426] The server saves the additions and corrections in real time. The input is the life log updated by the user, and the output is the updated information stored in the database.

[0427] Step 26:

[0428] The server's AI analyzes the generated life records and emotional data and makes lifestyle improvement suggestions based on the user's behavioral patterns and health status. For example, it makes specific suggestions such as "aim to walk more than 8,000 steps three days a week." The input is the life records and emotional data to be analyzed, and the output is lifestyle improvement suggestions.

[0429] Step 27:

[0430] The server presents the generated lifestyle improvement suggestions to the user. The input is the lifestyle improvement suggestions, and the output is a notification or display to the user.

[0431] (Application example 2)

[0432] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0433] Conventional food delivery systems are unable to make suggestions that take into account the user's health information or emotional state, making it difficult to provide a service that meets the diverse needs of users. Furthermore, because they are unable to make personalized suggestions based on the user's emotions, weather, or health status, there is a need to improve service satisfaction. A new system that solves these issues is needed.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring location information, health care information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, means including an emotion engine for analyzing the user's emotional state, means for providing personalized advice or suggestions to the user based on the acquired emotion information, means for the user to view, add to, and modify the life log, means for converting the automatically generated life log into a diary format, and means for analyzing the life log and emotion information and presenting ideas for improving the user's lifestyle. This makes it possible to suggest optimal food delivery options based on the user's emotions, health condition, and weather.

[0435] "Location information" is information that indicates the user's current location, obtained using GPS sensors, Wi-Fi, cell tower data, etc.

[0436] "Healthcare information" refers to data about a user's health status collected through health apps, built-in sensors, etc.

[0437] "Payment information" refers to information about a user's purchase history and expenditures obtained from the payment app.

[0438] "Search information" is information that indicates a user's interests and needs and is collected as browser and search engine history data.

[0439] "Weather information" refers to information about weather conditions at a specific location, obtained through a weather information API or the like.

[0440] An "emotion engine" is a system for analyzing a user's emotional state from their voice, facial expressions, text input, etc.

[0441] "User's life record" is data that records the user's daily activities and status, created based on location information, health information, payment information, search information, weather information, and emotional information.

[0442] "Personalized advice or suggestions" means specific advice or suggestions tailored to a user's particular needs or circumstances, based on the user's individual data and emotional state.

[0443] The "means for converting into diary format" is a function for converting the collected and analyzed data into a diary format that can be easily viewed and understood by the user.

[0444] "Ideas for improving lifestyle" is an approach that analyzes lifestyle records and emotional information to provide specific suggestions for improving users' behavior and health.

[0445] The system embodying the present invention collects and analyzes various data of users to provide personalized food delivery suggestions. This system acquires location information, health care information, payment information, search information, weather information, and emotion information, automatically generates a lifestyle log, and provides suggestions to users.

[0446] System configuration

[0447] The system mainly consists of the following components:

[0448] Server: The central element responsible for analyzing data and creating life records.

[0449] Device: A device that captures and transmits user data, such as a smartphone or tablet.

[0450] Emotion Engine: A system for analyzing the user's emotional state.

[0451] User: Receive advice and suggestions provided by the system and use them to improve their lives.

[0452] Data collection and analysis

[0453] The device acquires location information using GPS sensors, Wi-Fi, and cell tower data, and sends it to the server at regular intervals. The server then uses a reverse geocoding API to convert the information into addresses and facility names, which are then stored in a database.

[0454] The device collects user health data from the health app and built-in sensors and sends it to a server, which then analyzes the data to detect abnormalities and identify trends.

[0455] The device collects purchase history data from payment apps and sends it to a server, which analyzes purchase patterns and spending trends. Additionally, the device collects browser and search engine history data and sends it to a server to analyze user interests and needs.

[0456] The server periodically retrieves weather information based on the user's location using a weather information API, and stores the retrieved weather information in a database, associated with the location and a timestamp.

[0457] Analysis and utilization of emotional information

[0458] The device analyzes the user's emotional state through an emotion engine based on their voice, facial expressions, and text input. This emotion data is also sent to the server. For example, if a user inputs, "I am feeling a bit stressed and tired today," the emotion engine will determine that the user is in a "stressed" state.

[0459] Auto-generate and provide suggestions

[0460] The server automatically generates a daily life record based on the collected and analyzed data. The generated life record is displayed in diary format, and users can view it through the application and make additions or corrections as needed.

[0461] The server's AI analyzes the generated life log and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, if the user's emotional state is "stressed" and health data indicates a lack of exercise, it will suggest "eating foods that have a relaxing effect." It will also recommend "drinking warm soup" on rainy days.

[0462] As described above, the system of the present invention can comprehensively analyze a variety of user data and provide optimal food delivery suggestions based on emotions, health status, and weather. Users can refer to these suggestions and make more effective lifestyle improvements.

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] The device obtains the user's location information. Specifically, it uses GPS sensors, Wi-Fi, and cell tower data to determine the user's current location. This location data is temporarily stored in local storage and sent to the server at regular intervals. The input is the current location information, and the output is a data point containing the location information.

[0466] Step 2:

[0467] The server converts the received location information into a specific address or facility name using a reverse geocoding API. The input is location data, and the output is data containing the converted address or facility name. The server stores this data in a database.

[0468] Step 3:

[0469] The device collects the user's health data from the healthcare app and built-in sensors. The collected data includes the number of steps taken, heart rate, and sleep time. This data is also temporarily stored in local storage and sent to the server at regular intervals. The input is health data, and the output is the collected health data.

[0470] Step 4:

[0471] The server analyzes the received health data to detect abnormal values ​​and grasp trends. Specifically, it uses logic that issues a warning if the number of steps is below a certain level or if the heart rate is abnormal. The input is the received health data, and the output is the analysis results and trend information. This information is also stored in a database.

[0472] Step 5:

[0473] The terminal collects purchase history data from the payment app. The acquired purchase history data is temporarily stored in local storage and periodically sent to the server. The input is payment information, and the output is the collected purchase history data.

[0474] Step 6:

[0475] The server analyzes the received payment data to understand purchasing patterns and spending trends. For example, it analyzes what products are being purchased frequently and which areas of spending are increasing. The input is payment data, and the output is analyzed purchasing patterns and spending information. This data is also stored in a database.

[0476] Step 7:

[0477] The device collects browser and search engine history data. This data is collected as data for analyzing what information the user has searched for. This data is also temporarily stored in local storage and sent to the server at regular intervals. The input is search history data, and the output is the collected search history data.

[0478] Step 8:

[0479] The server analyzes the received search data to obtain information about the user's interests and needs. The input is search history data, and the output is analyzed information about the user's interests and needs. This data is also stored in a database.

[0480] Step 9:

[0481] The server periodically retrieves weather information based on the user's location using a weather API. The input is the location, and the output is the current weather data. This weather data is stored in a database, associated with the location and a timestamp.

[0482] Step 10:

[0483] The device analyzes the user's emotional state through an emotion engine based on their voice, facial expressions, text input, etc. For example, if a user inputs "I am feeling a bit stressed and tired today," the emotion engine analyzes the emotional state as "stressed." This emotion data is sent to the server. The input is the user input data for emotion analysis, and the output is the analyzed emotional state.

[0484] Step 11:

[0485] The server automatically generates a daily life log based on the collected and analyzed data, including location information, health information, payment information, search information, weather information, and emotion information. The input is these multiple data sources, and the output is the generated life log.

[0486] Step 12:

[0487] The server's AI analyzes the generated life records and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, it suggests "eating foods that have a relaxing effect" to a user who is not getting enough exercise. These suggestions are provided to the user via their device. The input is the life records and emotional data, and the output is ideas for lifestyle improvement.

[0488] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0489] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0490] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0491] [Second embodiment]

[0492] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0493] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0494] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0495] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0496] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0497] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0498] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0499] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0500] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0501] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0502] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0503] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0504] The present invention relates to a system that acquires location information, healthcare information, payment information, search information, and weather information and automatically generates a user's life log. Specific embodiments for implementing this system will be described below.

[0505] System configuration

[0506] This system mainly consists of three components: a server, a device (e.g., a smartphone), and a user. The device collects various data about the user's life, and the server analyzes the data to generate a life record and provide it to the user.

[0507] Program processing

[0508] 1. Acquiring and recording location information

[0509] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc., which records the user's current location and past movement history. The acquired location information is saved in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[0510] 2. Acquisition and analysis of healthcare information

[0511] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[0512] 3. Acquisition and recording of payment information

[0513] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[0514] 4. Acquisition and analysis of search information

[0515] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[0516] 5. Obtaining and linking weather information

[0517] The server uses a weather information API to obtain weather data (temperature, probability of precipitation, etc.) based on each user's location information. The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[0518] Automatic generation and conversion to diary format

[0519] The server automatically generates a daily life log based on the collected and analyzed data. Users can view this log through the application and add or modify it as needed. A function is also provided to convert the automatically generated life log into a diary format for easier viewing by users.

[0520] Presenting ideas for improving lifestyles

[0521] The server's AI analyzes the generated life log and suggests lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, it can provide specific suggestions to users who are not getting enough exercise, such as "walking at least 8,000 steps three days a week."

[0522] Specific examples

[0523] Day 1

[0524] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0525] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[0526] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0527] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0528] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[0529] Such a system allows users to reflect on their lives and find concrete ways to improve them.

[0530] The processing flow will be explained below.

[0531] Step 1:

[0532] The device periodically obtains the user's current location information using the GPS sensor, Wi-Fi location information, and cell tower data. The obtained location information is temporarily stored in local storage in latitude and longitude format.

[0533] Step 2:

[0534] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[0535] Step 3:

[0536] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[0537] Step 4:

[0538] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor), and stores the collected data in local storage in the form of steps, heart rate, sleep time, etc.

[0539] Step 5:

[0540] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0541] Step 6:

[0542] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[0543] Step 7:

[0544] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app. The terminal receives a real-time notification at the time of payment, which triggers data collection. The collected data is saved in local storage.

[0545] Step 8:

[0546] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[0547] Step 9:

[0548] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[0549] Step 10:

[0550] The device collects browser and search engine history data (search keywords, date and time), and the collected search data is stored in local storage.

[0551] Step 11:

[0552] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0553] Step 12:

[0554] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[0555] Step 13:

[0556] The server uses a weather information API based on each user's location information to periodically obtain weather data (temperature, probability of precipitation, etc.) for the relevant area.

[0557] Step 14:

[0558] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[0559] Step 15:

[0560] The server automatically generates a daily life log from the collected and analyzed data, and outputs the log in diary format for viewing by the user.

[0561] Step 16:

[0562] Users can view the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved on the server in real time.

[0563] Step 17:

[0564] The server's AI analyzes the generated lifestyle records and generates lifestyle improvement ideas based on the user's behavioral patterns and health condition.

[0565] Step 18:

[0566] Based on the analysis results, the server notifies the user of lifestyle improvement suggestions (for example, "Aim to take 8,000 steps or more three days a week.") Notifications are sent via the application.

[0567] Example 1

[0568] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0569] Conventional lifestyle record systems only collect individual pieces of information and lack the functionality to integrate and analyze them comprehensively, making it difficult to provide specific suggestions that directly lead to improvements in users' lifestyles. Furthermore, there are limited ways to convert the collected information into a format that users can easily view and edit. This creates the challenge of preventing users from gaining a detailed understanding of their own lifestyles and making specific improvements.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0571] In this invention, the server includes means for acquiring location information, health status information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, and means for the user to view, add to, and modify the life log. This makes it possible to integrate various information that has previously been collected individually and perform comprehensive analysis. It also makes it possible to provide specific lifestyle improvement suggestions to the user, and converting the log into a diary format makes it easier to view and modify.

[0572] "Location Information" means data that indicates a user's current geographic location and movement history obtained using technologies such as GPS, Wi-Fi, and cell towers.

[0573] "Health Information" refers to data related to your physical health collected from built-in sensors, such as pedometers, heart rate monitors, and sleep trackers, and from health apps.

[0574] "Payment information" refers to data related to the purchase history (store name, purchase amount, purchase date and time) made by the user through a payment app, etc.

[0575] "Search Information" refers to data regarding the search keywords and the date and time of the search performed by a user using a browser or search engine.

[0576] "Weather information" refers to weather-related data such as temperature and precipitation probability at a specific location, obtained through a weather information API.

[0577] "Life Log" is a record of a user's daily activities and status that is automatically generated based on location information, health status information, payment information, search information, and weather information.

[0578] "Reverse geocoding" is a technology that converts location information such as latitude and longitude into specific addresses or place names.

[0579] A "prompt sentence" is an instruction sentence that a generative AI model uses as input when generating natural language.

[0580] The present invention relates to a system that automatically generates a user's life log by integrating location information, health status information, payment information, search information, and weather information. This system mainly consists of three entities: a server, a terminal (e.g., a smartphone), and a user. Specific embodiments for implementing this system are described below.

[0581] System configuration

[0582] The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. Specifically, the device collects data using GPS sensors, healthcare apps, payment apps, browsers, etc. and periodically sends it to the server. The server processes the received data and runs software to automatically generate the life record.

[0583] Acquiring and recording location information

[0584] The device periodically acquires location information using GPS sensors, Wi-Fi, and cell towers. This information is stored in the device's local storage and periodically sent to a server. The server then sends the received location data to a reverse geocoding API, which converts it into a specific address or place name. The converted data is then recorded in a database.

[0585] Acquisition and analysis of healthcare information

[0586] The device uses built-in sensors and a health app to collect health status information from the pedometer, heart rate monitor, and sleep tracker. This data is stored in local storage and periodically sent to a server. The server analyzes the received data and records it in a database. Analysis methods include averaging the data and filtering outliers.

[0587] Acquisition and recording of payment information

[0588] The device collects purchase history data from the payment app. This information is stored in local storage and periodically sent to the server. The server analyzes the received payment data to automatically classify spending categories and understand spending patterns. The analysis results are recorded in a database.

[0589] Acquiring and analyzing search information

[0590] The device collects browser and search engine history data (search keywords, date and time). This information is stored in local storage and periodically sent to the server. The server analyzes the received search data to help understand the user's interests and needs. It also provides a content recommendation function based on this data.

[0591] Obtaining and linking weather information

[0592] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the location information and reflected in the user's daily life record.

[0593] Automatic generation and conversion to diary format

[0594] The server runs a program to automatically generate daily life logs based on the collected and analyzed data. This program uses a generative AI model and generates a life log in natural language by inputting prompt sentences. The generated life log is converted into a diary format and displayed in the application in a format that is easy for users to view.

[0595] Presenting ideas for improving lifestyles

[0596] The server's AI analyzes the generated lifestyle records and makes suggestions for lifestyle improvements based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise will receive specific suggestions such as "walk at least 8,000 steps three days a week." This allows users to reflect on their lifestyle in detail and identify areas for improvement.

[0597] Specific examples

[0598] The following specific scenarios are possible:

[0599] The device collects location information when the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0600] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[0601] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0602] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0603] The server generates a lifestyle record based on this information and makes suggestions for improving your lifestyle, such as, "Today you took 7,000 steps. You're 1,000 steps away from your goal of 8,000 steps."

[0604] As described above, the present invention allows users to record their own lifestyle in detail and obtain specific methods for improving their lifestyle.

[0605] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0606] Step 1: Obtain and record location information

[0607] The device acquires location information using the GPS sensor, Wi-Fi, and cell towers. The acquired location information is saved in the device's local storage. Specifically, the GPS sensor is activated every 60 seconds, and the acquired latitude and longitude information is written to the local storage. Every certain period (for example, every hour), the collected location data is compiled in JSON format and sent to the server using the HTTPS protocol.

[0608] Input: Latitude and longitude information obtained from the GPS sensor

[0609] Output: Location information stored in the device's local storage and location packets sent to the server

[0610] Step 2: Reverse geocoding and database recording

[0611] The server sends the received location information to a reverse geocoding API, which converts the latitude and longitude into specific addresses and place names. The information returned by the API is analyzed and recorded in a database. Specifically, multiple latitudes and longitudes are requested from the API, and the corresponding addresses and place names are obtained. The obtained geographic information is written to the database.

[0612] Input: Location information packet (latitude and longitude) sent from the device

[0613] Output: Specific geographic information recorded in a database

[0614] Step 3: Capture and record healthcare information

[0615] The device uses built-in sensors and the healthcare app to collect health status information such as heart rate, number of steps, and sleep time. The collected data is stored in the device's local storage. Specifically, the device queries the healthcare API every 30 minutes and adds the obtained data to the local storage. The collected health information is then sent to the server at regular intervals (for example, once a day).

[0616] Input: Health status information obtained from the health app or built-in sensors

[0617] Output: Health status information stored in the device's local storage and health information packets sent to the server

[0618] Step 4: Analyze health information and record it in the database

[0619] The server analyzes the received health status information and records it in a database. Specifically, it normalizes the received data, filters out outliers, and then aggregates the data for each user and writes it to the database.

[0620] Input: Health information packet sent from the device

[0621] Output: Health status information recorded in a database

[0622] Step 5: Capture and record payment information

[0623] The device collects purchase history data from the payment app and stores it in local storage. Specifically, it receives a notification every time the payment app updates the purchase history and writes the details to local storage. The collected data is sent to the server at regular intervals (for example, once a day).

[0624] Input: Purchase history data from payment app

[0625] Output: Payment information stored in the device's local storage and the payment information packet sent to the server

[0626] Step 6: Analyze payment information and record it in the database

[0627] The server analyzes the received payment information and records it in a database. The analysis method involves automatically classifying expenditure categories and understanding expenditure patterns. The analysis results are written to the database.

[0628] Input: Payment information packet sent from the terminal

[0629] Output: Spending pattern information recorded in a database

[0630] Step 7: Capture and record search information

[0631] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. Specifically, every time a user performs a search, the information is recorded in local storage. The collected search data is sent to the server at regular intervals (for example, once a day).

[0632] Input: Search history data from browsers and search engines

[0633] Output: Search information stored in the device's local storage and search information packets sent to the server

[0634] Step 8: Analyze search results and record database information

[0635] The server analyzes the received search information and records it in a database. The analysis method involves analyzing the frequency of search keywords and trends in search time periods. The analysis results are written to the database.

[0636] Input: Search information packet sent from the terminal

[0637] Output: Search interest information recorded in a database

[0638] Step 9: Retrieving and correlating weather information

[0639] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is associated with the location information and recorded in a database.

[0640] Input: Location information recorded on the server

[0641] Output: Weather data obtained from the weather information API, weather information associated with location information recorded in the database

[0642] Step 10: Automatic generation and conversion to diary format

[0643] The server automatically generates daily life logs based on the collected and analyzed data. Using a generative AI model, the server generates a life log in natural language by inputting prompts. The generated life log is converted into a diary format and displayed for users to view, add to, and edit through the application.

[0644] Input: Various information recorded in the database (location information, health information, payment information, search information, weather information)

[0645] Output: Automatically generated diary-formatted daily records

[0646] Step 11: Present ideas for improving your life

[0647] The server's AI analyzes the generated life log and presents lifestyle improvement suggestions based on the user's behavioral patterns and health status. Specific suggestions are displayed to the user as notifications.

[0648] Input: Automatically generated life log

[0649] Output: Suggestions for improving the user's lifestyle

[0650] (Application example 1)

[0651] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0652] In modern life, a variety of information is generated, including location information, healthcare information, payment information, search information, and weather information. However, there are only a limited number of systems that integrate this information to provide personalized services. In particular, food delivery services lack suggestions tailored to the user's health condition and lifestyle. This makes it difficult for users to select the optimal meal for their health condition and lifestyle.

[0653] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0654] In this invention, the server includes means for acquiring location information, health care information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, means for the user to view, add to, and modify the life log, means for converting the automatically generated life log into a diary format, means for analyzing the life log and presenting ideas for improving lifestyle, means for providing a personalized food delivery service to the user based on the acquired information, means for proposing a meal plan tailored to the user's health condition based on the acquired health care information, and means for recommending optimal meal options based on the acquired location information and weather information. This allows the user to receive personalized meal suggestions tailored to their health condition and lifestyle.

[0655] "Location information" is data that indicates a user's current location or history, and is obtained using technologies such as GPS, Wi-Fi, and cell towers.

[0656] "Health information" refers to data that indicates a user's health and activity status, and is obtained through built-in sensors and applications, such as pedometers, heart rate monitors, and sleep trackers.

[0657] "Payment information" is data that indicates a user's purchase history and expenditures, and includes information such as the store name, purchase amount, and purchase date and time.

[0658] "Search information" is data that indicates the search keywords and browsing history used by a user on the Internet, and is obtained through a browser or search engine.

[0659] "Weather information" is data that indicates the weather conditions in a specific area and time, and includes elements such as temperature, probability of precipitation, and wind speed.

[0660] "Life Log" is a record of a user's daily life that is created by integrating location information, health information, payment information, search information, and weather information.

[0661] The "diary format" is a format in which daily events and data are written in chronological order to make the record of daily life easier for users to understand.

[0662] A "food delivery service" is a service that delivers meals ordered online by a user to a specified location.

[0663] A "meal plan" is a meal suggestion that takes into account the user's health condition and nutritional balance.

[0664] "Meal selection" refers to the act or process of a user selecting the optimal meal based on specific criteria (health status, location, weather, etc.).

[0665] This invention provides a system that collects a user's location information, healthcare information, payment information, search information, and weather information, and automatically generates a user's life log based on this data. The system is mainly composed of three entities: a server, a terminal (e.g., a smartphone), and the user.

[0666] Acquiring and recording location information

[0667] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc. The acquired location information is stored in local storage and periodically sent to the server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[0668] Acquisition and analysis of healthcare information

[0669] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[0670] Acquisition and recording of payment information

[0671] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[0672] Acquiring and analyzing search information

[0673] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[0674] Obtaining and linking weather information

[0675] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[0676] Providing personalized food delivery services

[0677] Based on the acquired information, the system can provide users with personalized food delivery services, such as recommending menu items from nearby restaurants based on the user's current location and taking weather information into account to suggest suitable meal choices.

[0678] Meal plan suggestions based on your health status

[0679] Based on the acquired health information, the system will propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[0680] Hardware and software used

[0681] The following hardware and software are used to realize this system.

[0682] Hardware: Smartphone (iOS / Android compatible), smartwatch (health data acquisition)

[0683] Software: Mobile applications (Swift, Kotlin), server side (Node.js, Python), databases (MongoDB, PostgreSQL)

[0684] Specific examples

[0685] 1. A user searches for "healthy restaurants near me."

[0686] 2. The system will recommend the best restaurant and menu based on the user's current location and health information.

[0687] 3. Example prompt: "Show restaurant suggestions based on the user's location and recommend calorie-friendly menu items based on their health data."

[0688] With the above configuration, users can receive a personalized food delivery service that is best suited to their health condition and lifestyle.

[0689] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0690] Step 1: Obtain and record location information

[0691] The device periodically acquires the user's location information using technologies such as GPS, Wi-Fi, and cell towers. The acquired location information is stored in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[0692] Input: User location information (GPS data)

[0693] Data processing: saving to local storage, using reverse geocoding API

[0694] Output: Specific address or place name

[0695] Step 2: Acquire and analyze healthcare information

[0696] The device collects health data (such as steps taken, heart rate, and sleep time) from the health app and built-in sensors. The collected data is stored in local storage and periodically sent to a server. The server analyzes the received health data, records it in a database, and updates the user's daily record.

[0697] Input: User's health information (step count, heart rate, sleep time, etc.)

[0698] Data processing: saving to local storage, data analysis

[0699] Output: Analysis results, recording to database

[0700] Step 3: Capture and record payment information

[0701] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[0702] Input: User's payment information (purchase history data)

[0703] Data processing: saving to local storage, data analysis

[0704] Output: Analysis results, user spending patterns, records in database

[0705] Step 4: Obtaining and analyzing search information

[0706] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[0707] Input: User search information (search keywords, date and time)

[0708] Data processing: saving to local storage, data analysis

[0709] Output: Analysis results, user interests and needs

[0710] Step 5: Obtaining and Correlating Weather Information

[0711] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[0712] Input: User location information, weather information API data

[0713] Data processing: Use of weather information API, association with location information, recording in database

[0714] Output: Weather data, recorded in database

[0715] Step 6: Offer a personalized food delivery service

[0716] The server uses the collected information to provide users with personalized food delivery services, and the device recommends menu items from nearby restaurants based on the user's current location and takes weather information into account to suggest appropriate meal choices.

[0717] Input: User location information, payment information, weather information, health information

[0718] Data processing: information integration, application of recommendation algorithms

[0719] Output: Personalized menu recommendations, suggesting appropriate meal choices

[0720] Step 7: Suggested meal plan based on health status

[0721] The server uses the collected health information to propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[0722] Input: User's healthcare information

[0723] Data processing: analysis of health information, generation of meal plans

[0724] Output: Meal plan suggestions based on health status

[0725] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0726] The present invention combines a system that automatically generates a user's life log based on location information, healthcare information, payment information, search information, and weather information with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system are described below.

[0727] System configuration

[0728] This system mainly consists of four components: a server, a device (e.g., a smartphone), a user, and an emotion engine. The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. The emotion engine also recognizes the user's emotional state and uses that information to personalize the lifestyle improvement ideas provided by the server.

[0729] Program processing

[0730] 1. Acquiring and recording location information

[0731] The device periodically acquires the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and temporarily stores it in local storage. At predetermined intervals, the acquired location information is sent to the server. The server then converts the received location information into specific addresses and facility names using a reverse geocoding API and stores them in a database.

[0732] 2. Acquisition and analysis of healthcare information

[0733] The device collects the user's health data from the healthcare app and built-in sensors and stores it in local storage. At regular intervals, the collected health data is sent to a server. The server analyzes the received health data, detects abnormal values ​​and identifies trends, records them in a database, and updates the user's lifestyle record.

[0734] 3. Acquisition and recording of payment information

[0735] The device collects purchase history data from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data to understand purchasing patterns and spending trends, and records them in a database.

[0736] 4. Acquisition and analysis of search information

[0737] The device collects browser and search engine history data and stores it in local storage. At regular intervals, the collected search data is sent to a server. The server analyzes the received search data to obtain information about the user's interests and needs, and records the analysis results in a database.

[0738] 5. Obtaining and linking weather information

[0739] The server periodically retrieves weather data based on each user's location using a weather information API. The retrieved weather data is stored in a database, associated with the user's location and a timestamp.

[0740] 6. Emotion recognition

[0741] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. This emotional data is also sent to the server.

[0742] 7. Automatic generation and conversion to diary format

[0743] The server automatically generates a daily life log based on the collected and analyzed data. The generated life log is output in diary format and can be viewed by the user. The user can view the automatically generated life log through the application and make additions or corrections as needed. Additions and corrections are saved on the server in real time.

[0744] 8. Presenting ideas for improving life

[0745] The server's AI analyzes the generated life log and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise may be advised to aim to walk more than 8,000 steps three days a week. Furthermore, depending on the user's emotional state, it can also suggest relaxation techniques and positive activities to reduce stress.

[0746] Specific examples

[0747] Day 1

[0748] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0749] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[0750] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0751] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0752] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[0753] The device uses an emotion engine to analyze the user's emotional state from their voice and facial expressions and transmits the results to the server.

[0754] The server analyzes the user's emotional data and provides emotion-based advice such as, "You seem to be feeling stressed lately. Why not try 15 minutes of relaxation?"

[0755] Such a system would allow users to gain a deeper understanding of their lives, learn concrete ways to improve them, and receive advice that takes into account their emotional state.

[0756] The processing flow will be explained below.

[0757] Step 1:

[0758] The device periodically obtains the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and the obtained location information is temporarily stored in local storage in latitude and longitude format.

[0759] Step 2:

[0760] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[0761] Step 3:

[0762] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[0763] Step 4:

[0764] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor). The collected data (e.g., number of steps, heart rate, sleep time) is stored in local storage.

[0765] Step 5:

[0766] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0767] Step 6:

[0768] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[0769] Step 7:

[0770] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. The terminal receives a real-time notification at the time of payment, which triggers data collection.

[0771] Step 8:

[0772] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[0773] Step 9:

[0774] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[0775] Step 10:

[0776] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage.

[0777] Step 11:

[0778] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[0779] Step 12:

[0780] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[0781] Step 13:

[0782] The server uses a weather information API based on each user's location information to periodically obtain relevant weather data (temperature, probability of precipitation, etc.).

[0783] Step 14:

[0784] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[0785] Step 15:

[0786] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. The emotion data is stored in local storage.

[0787] Step 16:

[0788] The device also transmits emotion data to the server at regular intervals, using a secure communication protocol such as HTTPS.

[0789] Step 17:

[0790] The server automatically generates daily life records based on the life record data and emotion data, and outputs the generated life records in diary format for users to view.

[0791] Step 18:

[0792] Users can check the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved on the server in real time.

[0793] Step 19:

[0794] Based on the collected and analyzed data, the server's AI analyzes the user's behavioral patterns and health status, and generates ideas for improving lifestyles.

[0795] Step 20:

[0796] The server notifies the user of the generated lifestyle improvement ideas. For example, if the user's step count is insufficient, the server will suggest, "Today's step count was 7,000. You are 1,000 steps away from your goal of 8,000." The server will also suggest relaxation techniques and positive activities to reduce stress based on the user's emotional state.

[0797] Example 2

[0798] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0799] While existing technologies exist for automatically generating user life logs, they simply collect and record data and lack the flexibility to include specific suggestions for improving the user's emotional state or lifestyle. Furthermore, they lack a mechanism for providing personalized suggestions to improve quality of life. This makes it difficult for users to gain a deeper understanding of their own lives and identify specific behavioral improvements.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, health information, purchase information, search information, and weather information, means for automatically generating a user's life record based on the acquired information, and means for recognizing the user's emotional state and storing the information in a database. This allows the user to understand their own life record in detail and receive specific suggestions for improving their lifestyle based on their emotional state.

[0801] "Location information" is data that indicates a user's current physical location and is obtained from GPS sensors, Wi-Fi, cell tower data, etc.

[0802] "Health information" is data that indicates the user's health condition and physical activity, and includes information such as the number of steps taken, heart rate, and sleep time.

[0803] "Purchase information" refers to transaction data when a user purchases a product or service, and includes the purchase date and time, amount, and store information.

[0804] "Search information" is data that indicates a user's interests and needs based on the search queries and browsing history that the user performs on the Internet.

[0805] "Weather information" refers to environmental data such as the weather, temperature, and humidity at the user's location, and is information obtained from weather APIs, etc.

[0806] "Life records" are data that show the activities and trends of a user's daily life, integrating the user's location information, health information, purchase information, search information, and weather information.

[0807] "Emotional state" is data that indicates a user's psychological or emotional state, and is information analyzed from voice, facial expressions, text input, etc.

[0808] A "database" is an information system for systematically storing, managing, and analyzing data such as a user's daily records and emotional state.

[0809] "Lifestyle Improvement Suggestions" are specific suggestions and advice based on the user's lifestyle records and emotional state, with the aim of improving the user's quality of life.

[0810] The present invention combines a system that automatically generates a user's life log based on location information, health information, purchase information, search information, and weather information with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system are described below.

[0811] System configuration

[0812] This system mainly consists of four components: a server, a terminal (e.g., a smart device), a user, and an emotion engine. The terminal collects various data about the user's life, and the server analyzes the data to generate a life record and provide it to the user. The emotion engine also recognizes the user's emotional state and uses that information to personalize the life improvement ideas provided by the server.

[0813] Program processing

[0814] Acquiring and recording location information

[0815] The device uses GPS sensors, Wi-Fi, and cell tower data to obtain the user's current location information and temporarily stores it in local storage. At regular intervals, the device sends the obtained location information to the server. The server then uses a reverse geocoding API to convert the received location information into specific addresses and facility names and stores them in a database.

[0816] Acquisition and analysis of health information

[0817] The device collects the user's health data from the healthcare app and built-in sensors and stores it in local storage. At regular intervals, the device sends the collected health data to a server. The server analyzes the received health data, detects abnormal values ​​and identifies trends, records them in a database, and updates the user's lifestyle record.

[0818] Acquisition and recording of purchasing information

[0819] The device collects purchase history data from the payment app and stores it in local storage. At regular intervals, the device sends the collected payment data to the server. The server analyzes the received payment data, identifies purchasing patterns and spending trends, and records them in a database.

[0820] Acquiring and analyzing search information

[0821] The device collects browser and search engine history data and stores it in local storage. At regular intervals, the device sends the collected search data to the server. The server analyzes the received search data, obtains information about the user's interests and needs, and records the analysis results in a database.

[0822] Obtaining and linking weather information

[0823] The server periodically retrieves relevant weather data using a weather information API based on each user's location information. The retrieved weather data is stored in a database, associated with the user's location information and a timestamp.

[0824] emotion recognition

[0825] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. This emotional data is also sent to the server.

[0826] Automatic generation and conversion to journal format

[0827] The server automatically generates a daily life record based on the collected and analyzed data. The generated life record is output in a diary format that can be viewed by the user. The user can view the automatically generated life record through the application and make additions or corrections as needed. Additions and corrections are saved on the server in real time.

[0828] Presenting ideas for improving lifestyles

[0829] The server's AI analyzes the generated life log and emotional data and makes lifestyle improvement suggestions based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise might be advised to aim to walk more than 8,000 steps three days a week. Furthermore, depending on the user's emotional state, it can also suggest relaxation techniques and positive activities to reduce stress.

[0830] Specific examples

[0831] Day 1

[0832] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0833] The device collects data from the health app, showing that the user walked 7,000 steps and slept for seven hours.

[0834] The terminal collects purchasing information when a user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0835] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0836] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[0837] The device uses an emotion engine to analyze the user's emotional state from their voice and facial expressions and transmits the results to the server.

[0838] The server analyzes the user's emotional data and provides emotion-based advice such as, "You seem to be feeling stressed lately. Why not try 15 minutes of relaxation?"

[0839] Such a system would allow users to gain a deeper understanding of their lives, learn concrete ways to improve them, and receive advice that takes into account their emotional state.

[0840] Example prompts for generative AI models

[0841] "Generate a user's life record based on location, health, purchase, search, and weather information."

[0842] "Analyze users' emotions and provide emotion-based advice for improving their lives."

[0843] "Convert the user's daily activity record into a diary format and provide it via push notification."

[0844] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0845] Step 1:

[0846] The device obtains the user's current location information using GPS sensors, Wi-Fi, and cell tower data. This allows the device to determine the user's location and temporarily store the obtained location information in local storage. For example, the device can determine the user's specific location using latitude and longitude information obtained from the GPS sensor and Wi-Fi connection information. The input is location data from the sensor, and the output is location information stored in local storage.

[0847] Step 2:

[0848] The device sends the acquired location information to the server at a predetermined interval (e.g., 30 minutes). Specifically, the location information is uploaded to the server at regular intervals using batch processing. The input is the location information saved in the local storage, and the output is the location information sent to the server.

[0849] Step 3:

[0850] The server converts the received location information into a specific address or facility name using a reverse geocoding API (e.g., map information API). In this conversion process, the coordinate information is input into the API to obtain detailed address information. The input is the transmitted location information, and the output is the converted address or facility name.

[0851] Step 4:

[0852] The server saves the converted address and facility name in a database. At this time, the location information is saved with a timestamp, making it possible to track where the user was at a later date. The input is the converted address data, and the output is the information saved in the database.

[0853] Step 5:

[0854] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate sensor). This data includes the number of steps, heart rate, sleep time, etc. The input is the health data measured by the sensors, and the output is the health data stored in local storage.

[0855] Step 6:

[0856] The device sends the collected health data to the server at a predetermined interval (e.g., once a day). The input is the health data stored in the local storage, and the output is the health data sent to the server.

[0857] Step 7:

[0858] The server analyzes the received health data to detect outliers and identify trends. For example, it uses an AI model to detect sudden fluctuations in heart rate or lack of sleep. The input is the transmitted health data, and the output is the analysis results.

[0859] Step 8:

[0860] The server records the analysis results in a database and updates the user's life record. This update process continuously stores daily health data for future trend analysis. The input is the analysis results, and the output is the updated life record.

[0861] Step 9:

[0862] The terminal collects purchase history data from a payment app (e.g., an electronic payment app). This includes the purchase date and time, amount, and store information. The input is transaction data from the payment app, and the output is purchase information stored in local storage.

[0863] Step 10:

[0864] The terminal sends the collected purchase data to the server at regular intervals (e.g., one week). The input is the purchase data stored in the local storage, and the output is the purchase data sent to the server.

[0865] Step 11:

[0866] The server analyzes the received purchase data to understand purchasing patterns and spending trends. For example, it uses machine learning algorithms to detect specific consumption habits and wasteful spending. The input is the submitted purchase data, and the output is the analysis results.

[0867] Step 12:

[0868] The server records the analysis results in a database and updates the user's purchase history. The input is the analysis results, and the output is the updated purchase history data.

[0869] Step 13:

[0870] The device collects browser and search engine history data, including user search queries and browsing history. The input is the history data from the browser and search engine, and the output is search information stored in local storage.

[0871] Step 14:

[0872] The device sends the search data collected at regular intervals (e.g., once a month) to the server. The input is the search data stored in the local storage, and the output is the search data sent to the server.

[0873] Step 15:

[0874] The server analyzes the received search data to obtain information about the user's interests and needs. For example, it uses natural language processing to analyze the search query and identify specific topics or issues of interest. The input is the submitted search data, and the output is the analysis results.

[0875] Step 16:

[0876] The server records the analysis results in a database and updates the user's interests and needs. The input is the analysis results and the output is the updated interest data.

[0877] Step 17:

[0878] The server periodically obtains the relevant weather data using a weather information API based on each user's location information. The input is the user's location information, and the output is the obtained weather data.

[0879] Step 18:

[0880] The server associates the acquired weather data with location information and a timestamp and stores them in a database. The input is the acquired weather data, and the output is the weather information stored in the database.

[0881] Step 19:

[0882] The device analyzes the user's emotional state through an emotion engine based on the user's voice, facial expressions, and text input. For example, it analyzes the tone of the voice and evaluates the emotion using facial expression recognition software. The input is voice and facial expression data, and the output is analyzed emotional data.

[0883] Step 20:

[0884] The device sends the analyzed emotion data to the server. The input is the data analyzed by the emotion engine, and the output is the emotion data sent to the server.

[0885] Step 21:

[0886] The server stores the received emotion data in a database. The input is the transmitted emotion data, and the output is the emotion information stored in the database.

[0887] Step 22:

[0888] The server automatically generates a daily life log based on the collected and analyzed data. It uses a generative AI model to integrate multiple data sources and record the user's daily activities in detail. The input is multiple data sources (location information, health information, purchase information, search information, weather information, and emotion data), and the output is an automatically generated life log.

[0889] Step 23:

[0890] The server converts the generated life records into a diary format and outputs them in a viewable format for users. The input is the automatically generated life records, and the output is the life records converted into diary format.

[0891] Step 24:

[0892] The user can view the automatically generated life log through the application and add or modify it as needed. The input is the life log converted into a diary format, and the output is the added or modified record.

[0893] Step 25:

[0894] The server saves the additions and corrections in real time. The input is the life log updated by the user, and the output is the updated information stored in the database.

[0895] Step 26:

[0896] The server's AI analyzes the generated life records and emotional data and makes lifestyle improvement suggestions based on the user's behavioral patterns and health status. For example, it makes specific suggestions such as "aim to walk more than 8,000 steps three days a week." The input is the life records and emotional data to be analyzed, and the output is lifestyle improvement suggestions.

[0897] Step 27:

[0898] The server presents the generated lifestyle improvement suggestions to the user. The input is the lifestyle improvement suggestions, and the output is a notification or display to the user.

[0899] (Application example 2)

[0900] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0901] Conventional food delivery systems are unable to make suggestions that take into account the user's health information or emotional state, making it difficult to provide a service that meets the diverse needs of users. Furthermore, because they are unable to make personalized suggestions based on the user's emotions, weather, or health status, there is a need to improve service satisfaction. A new system that solves these issues is needed.

[0902] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring location information, health care information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, means including an emotion engine for analyzing the user's emotional state, means for providing personalized advice or suggestions to the user based on the acquired emotion information, means for the user to view, add to, and modify the life log, means for converting the automatically generated life log into a diary format, and means for analyzing the life log and emotion information and presenting ideas for improving the user's lifestyle. This makes it possible to suggest optimal food delivery options based on the user's emotions, health condition, and weather.

[0903] "Location information" is information that indicates the user's current location, obtained using GPS sensors, Wi-Fi, cell tower data, etc.

[0904] "Healthcare information" refers to data about a user's health status collected through health apps, built-in sensors, etc.

[0905] "Payment information" refers to information about a user's purchase history and expenditures obtained from the payment app.

[0906] "Search information" is information that indicates a user's interests and needs and is collected as browser and search engine history data.

[0907] "Weather information" refers to information about weather conditions at a specific location, obtained through a weather information API or the like.

[0908] An "emotion engine" is a system for analyzing a user's emotional state from their voice, facial expressions, text input, etc.

[0909] "User's life record" is data that records the user's daily activities and status, created based on location information, health information, payment information, search information, weather information, and emotional information.

[0910] "Personalized advice or suggestions" means specific advice or suggestions tailored to a user's particular needs or circumstances, based on the user's individual data and emotional state.

[0911] The "means for converting into diary format" is a function for converting the collected and analyzed data into a diary format that can be easily viewed and understood by the user.

[0912] "Ideas for improving lifestyle" is an approach that analyzes lifestyle records and emotional information to provide specific suggestions for improving users' behavior and health.

[0913] The system embodying the present invention collects and analyzes various data of users to provide personalized food delivery suggestions. This system acquires location information, health care information, payment information, search information, weather information, and emotion information, automatically generates a lifestyle log, and provides suggestions to users.

[0914] System configuration

[0915] The system mainly consists of the following components:

[0916] Server: The central element responsible for analyzing data and creating life records.

[0917] Device: A device that captures and transmits user data, such as a smartphone or tablet.

[0918] Emotion Engine: A system for analyzing the user's emotional state.

[0919] User: Receive advice and suggestions provided by the system and use them to improve their lives.

[0920] Data collection and analysis

[0921] The device acquires location information using GPS sensors, Wi-Fi, and cell tower data, and sends it to the server at regular intervals. The server then uses a reverse geocoding API to convert the information into addresses and facility names, which are then stored in a database.

[0922] The device collects user health data from the health app and built-in sensors and sends it to a server, which then analyzes the data to detect abnormalities and identify trends.

[0923] The device collects purchase history data from payment apps and sends it to a server, which analyzes purchase patterns and spending trends. Additionally, the device collects browser and search engine history data and sends it to a server to analyze user interests and needs.

[0924] The server periodically retrieves weather information based on the user's location using a weather information API, and stores the retrieved weather information in a database, associated with the location and a timestamp.

[0925] Analysis and utilization of emotional information

[0926] The device analyzes the user's emotional state through an emotion engine based on their voice, facial expressions, and text input. This emotion data is also sent to the server. For example, if a user inputs, "I am feeling a bit stressed and tired today," the emotion engine will determine that the user is in a "stressed" state.

[0927] Auto-generate and provide suggestions

[0928] The server automatically generates a daily life record based on the collected and analyzed data. The generated life record is displayed in diary format, and users can view it through the application and make additions or corrections as needed.

[0929] The server's AI analyzes the generated life log and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, if the user's emotional state is "stressed" and health data indicates a lack of exercise, it will suggest "eating foods that have a relaxing effect." It will also recommend "drinking warm soup" on rainy days.

[0930] As described above, the system of the present invention can comprehensively analyze a variety of user data and provide optimal food delivery suggestions based on emotions, health status, and weather. Users can refer to these suggestions and make more effective lifestyle improvements.

[0931] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0932] Step 1:

[0933] The device obtains the user's location information. Specifically, it uses GPS sensors, Wi-Fi, and cell tower data to determine the user's current location. This location data is temporarily stored in local storage and sent to the server at regular intervals. The input is the current location information, and the output is a data point containing the location information.

[0934] Step 2:

[0935] The server converts the received location information into a specific address or facility name using a reverse geocoding API. The input is location data, and the output is data containing the converted address or facility name. The server stores this data in a database.

[0936] Step 3:

[0937] The device collects the user's health data from the healthcare app and built-in sensors. The collected data includes the number of steps taken, heart rate, and sleep time. This data is also temporarily stored in local storage and sent to the server at regular intervals. The input is health data, and the output is the collected health data.

[0938] Step 4:

[0939] The server analyzes the received health data to detect abnormal values ​​and grasp trends. Specifically, it uses logic that issues a warning if the number of steps is below a certain level or if the heart rate is abnormal. The input is the received health data, and the output is the analysis results and trend information. This information is also stored in a database.

[0940] Step 5:

[0941] The terminal collects purchase history data from the payment app. The acquired purchase history data is temporarily stored in local storage and periodically sent to the server. The input is payment information, and the output is the collected purchase history data.

[0942] Step 6:

[0943] The server analyzes the received payment data to understand purchasing patterns and spending trends. For example, it analyzes what products are being purchased frequently and which areas of spending are increasing. The input is payment data, and the output is analyzed purchasing patterns and spending information. This data is also stored in a database.

[0944] Step 7:

[0945] The device collects browser and search engine history data. This data is collected as data for analyzing what information the user has searched for. This data is also temporarily stored in local storage and sent to the server at regular intervals. The input is search history data, and the output is the collected search history data.

[0946] Step 8:

[0947] The server analyzes the received search data to obtain information about the user's interests and needs. The input is search history data, and the output is analyzed information about the user's interests and needs. This data is also stored in a database.

[0948] Step 9:

[0949] The server periodically retrieves weather information based on the user's location using a weather API. The input is the location, and the output is the current weather data. This weather data is stored in a database, associated with the location and a timestamp.

[0950] Step 10:

[0951] The device analyzes the user's emotional state through an emotion engine based on their voice, facial expressions, text input, etc. For example, if a user inputs "I am feeling a bit stressed and tired today," the emotion engine analyzes the emotional state as "stressed." This emotion data is sent to the server. The input is the user input data for emotion analysis, and the output is the analyzed emotional state.

[0952] Step 11:

[0953] The server automatically generates a daily life log based on the collected and analyzed data, including location information, health information, payment information, search information, weather information, and emotion information. The input is these multiple data sources, and the output is the generated life log.

[0954] Step 12:

[0955] The server's AI analyzes the generated life records and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, it suggests "eating foods that have a relaxing effect" to a user who is not getting enough exercise. These suggestions are provided to the user via their device. The input is the life records and emotional data, and the output is ideas for lifestyle improvement.

[0956] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0957] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0958] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0959] [Third embodiment]

[0960] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0961] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0962] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0963] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0964] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0965] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0966] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0967] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0968] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0969] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0970] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0971] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0972] The present invention relates to a system that acquires location information, healthcare information, payment information, search information, and weather information and automatically generates a user's life log. Specific embodiments for implementing this system will be described below.

[0973] System configuration

[0974] This system mainly consists of three components: a server, a device (e.g., a smartphone), and a user. The device collects various data about the user's life, and the server analyzes the data to generate a life record and provide it to the user.

[0975] Program processing

[0976] 1. Acquiring and recording location information

[0977] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc., which records the user's current location and past movement history. The acquired location information is saved in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[0978] 2. Acquisition and analysis of healthcare information

[0979] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[0980] 3. Acquisition and recording of payment information

[0981] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[0982] 4. Acquisition and analysis of search information

[0983] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[0984] 5. Obtaining and linking weather information

[0985] The server uses a weather information API to obtain weather data (temperature, probability of precipitation, etc.) based on each user's location information. The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[0986] Automatic generation and conversion to diary format

[0987] The server automatically generates a daily life log based on the collected and analyzed data. Users can view this log through the application and add or modify it as needed. A function is also provided to convert the automatically generated life log into a diary format for easier viewing by users.

[0988] Presenting ideas for improving lifestyles

[0989] The server's AI analyzes the generated life log and suggests lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, it can provide specific suggestions to users who are not getting enough exercise, such as "walking at least 8,000 steps three days a week."

[0990] Specific examples

[0991] Day 1

[0992] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[0993] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[0994] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[0995] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[0996] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[0997] Such a system allows users to reflect on their lives and find concrete ways to improve them.

[0998] The processing flow will be explained below.

[0999] Step 1:

[1000] The device periodically obtains the user's current location information using the GPS sensor, Wi-Fi location information, and cell tower data. The obtained location information is temporarily stored in local storage in latitude and longitude format.

[1001] Step 2:

[1002] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[1003] Step 3:

[1004] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[1005] Step 4:

[1006] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor), and stores the collected data in local storage in the form of steps, heart rate, sleep time, etc.

[1007] Step 5:

[1008] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1009] Step 6:

[1010] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[1011] Step 7:

[1012] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app. The terminal receives a real-time notification at the time of payment, which triggers data collection. The collected data is saved in local storage.

[1013] Step 8:

[1014] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[1015] Step 9:

[1016] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[1017] Step 10:

[1018] The device collects browser and search engine history data (search keywords, date and time), and the collected search data is stored in local storage.

[1019] Step 11:

[1020] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1021] Step 12:

[1022] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[1023] Step 13:

[1024] The server uses a weather information API based on each user's location information to periodically obtain weather data (temperature, probability of precipitation, etc.) for the relevant area.

[1025] Step 14:

[1026] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[1027] Step 15:

[1028] The server automatically generates a daily life log from the collected and analyzed data, and outputs the log in diary format for viewing by the user.

[1029] Step 16:

[1030] Users can view the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved on the server in real time.

[1031] Step 17:

[1032] The server's AI analyzes the generated lifestyle records and generates lifestyle improvement ideas based on the user's behavioral patterns and health condition.

[1033] Step 18:

[1034] Based on the analysis results, the server notifies the user of lifestyle improvement suggestions (for example, "Aim to take 8,000 steps or more three days a week.") Notifications are sent via the application.

[1035] Example 1

[1036] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1037] Conventional lifestyle record systems only collect individual pieces of information and lack the functionality to integrate and analyze them comprehensively, making it difficult to provide specific suggestions that directly lead to improvements in users' lifestyles. Furthermore, there are limited ways to convert the collected information into a format that users can easily view and edit. This creates the challenge of preventing users from gaining a detailed understanding of their own lifestyles and making specific improvements.

[1038] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1039] In this invention, the server includes means for acquiring location information, health status information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, and means for the user to view, add to, and modify the life log. This makes it possible to integrate various information that has previously been collected individually and perform comprehensive analysis. It also makes it possible to provide specific lifestyle improvement suggestions to the user, and converting the log into a diary format makes it easier to view and modify.

[1040] "Location Information" means data that indicates a user's current geographic location and movement history obtained using technologies such as GPS, Wi-Fi, and cell towers.

[1041] "Health Information" refers to data related to your physical health collected from built-in sensors, such as pedometers, heart rate monitors, and sleep trackers, and from health apps.

[1042] "Payment information" refers to data related to the purchase history (store name, purchase amount, purchase date and time) made by the user through a payment app, etc.

[1043] "Search Information" refers to data regarding the search keywords and the date and time of the search performed by a user using a browser or search engine.

[1044] "Weather information" refers to weather-related data such as temperature and precipitation probability at a specific location, obtained through a weather information API.

[1045] "Life Log" is a record of a user's daily activities and status that is automatically generated based on location information, health status information, payment information, search information, and weather information.

[1046] "Reverse geocoding" is a technology that converts location information such as latitude and longitude into specific addresses or place names.

[1047] A "prompt sentence" is an instruction sentence that a generative AI model uses as input when generating natural language.

[1048] The present invention relates to a system that automatically generates a user's life log by integrating location information, health status information, payment information, search information, and weather information. This system mainly consists of three entities: a server, a terminal (e.g., a smartphone), and a user. Specific embodiments for implementing this system are described below.

[1049] System configuration

[1050] The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. Specifically, the device collects data using GPS sensors, healthcare apps, payment apps, browsers, etc. and periodically sends it to the server. The server processes the received data and runs software to automatically generate the life record.

[1051] Acquiring and recording location information

[1052] The device periodically acquires location information using GPS sensors, Wi-Fi, and cell towers. This information is stored in the device's local storage and periodically sent to a server. The server then sends the received location data to a reverse geocoding API, which converts it into a specific address or place name. The converted data is then recorded in a database.

[1053] Acquisition and analysis of healthcare information

[1054] The device uses built-in sensors and a health app to collect health status information from the pedometer, heart rate monitor, and sleep tracker. This data is stored in local storage and periodically sent to a server. The server analyzes the received data and records it in a database. Analysis methods include averaging the data and filtering outliers.

[1055] Acquisition and recording of payment information

[1056] The device collects purchase history data from the payment app. This information is stored in local storage and periodically sent to the server. The server analyzes the received payment data to automatically classify spending categories and understand spending patterns. The analysis results are recorded in a database.

[1057] Acquiring and analyzing search information

[1058] The device collects browser and search engine history data (search keywords, date and time). This information is stored in local storage and periodically sent to the server. The server analyzes the received search data to help understand the user's interests and needs. It also provides a content recommendation function based on this data.

[1059] Obtaining and linking weather information

[1060] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the location information and reflected in the user's daily life record.

[1061] Automatic generation and conversion to diary format

[1062] The server runs a program to automatically generate daily life logs based on the collected and analyzed data. This program uses a generative AI model and generates a life log in natural language by inputting prompt sentences. The generated life log is converted into a diary format and displayed in the application in a format that is easy for users to view.

[1063] Presenting ideas for improving lifestyles

[1064] The server's AI analyzes the generated lifestyle records and makes suggestions for lifestyle improvements based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise will receive specific suggestions such as "walk at least 8,000 steps three days a week." This allows users to reflect on their lifestyle in detail and identify areas for improvement.

[1065] Specific examples

[1066] The following are possible specific scenarios:

[1067] The device collects location information when the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[1068] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[1069] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[1070] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[1071] The server generates a lifestyle record based on this information and makes suggestions for improving your lifestyle, such as, "Today you took 7,000 steps. You're 1,000 steps away from your goal of 8,000 steps."

[1072] As described above, the present invention allows users to record their own lifestyle in detail and obtain specific methods for improving their lifestyle.

[1073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1074] Step 1: Obtain and record location information

[1075] The device acquires location information using the GPS sensor, Wi-Fi, and cell towers. The acquired location information is saved in the device's local storage. Specifically, the GPS sensor is activated every 60 seconds, and the acquired latitude and longitude information is written to the local storage. Every certain period (for example, every hour), the collected location data is compiled in JSON format and sent to the server using the HTTPS protocol.

[1076] Input: Latitude and longitude information obtained from the GPS sensor

[1077] Output: Location information stored in the device's local storage and location packets sent to the server

[1078] Step 2: Reverse geocoding and database recording

[1079] The server sends the received location information to a reverse geocoding API, which converts the latitude and longitude into specific addresses and place names. The information returned by the API is analyzed and recorded in a database. Specifically, multiple latitudes and longitudes are requested from the API, and the corresponding addresses and place names are obtained. The obtained geographic information is written to the database.

[1080] Input: Location information packet (latitude and longitude) sent from the device

[1081] Output: Specific geographic information recorded in a database

[1082] Step 3: Capture and record healthcare information

[1083] The device uses built-in sensors and the healthcare app to collect health status information such as heart rate, number of steps, and sleep time. The collected data is stored in the device's local storage. Specifically, the device queries the healthcare API every 30 minutes and adds the obtained data to the local storage. The collected health information is then sent to the server at regular intervals (for example, once a day).

[1084] Input: Health status information obtained from the health app or built-in sensors

[1085] Output: Health status information stored in the device's local storage and health information packets sent to the server

[1086] Step 4: Analyze health information and record it in the database

[1087] The server analyzes the received health status information and records it in a database. Specifically, it normalizes the received data, filters out abnormal values, and then aggregates the data for each user and writes it to the database.

[1088] Input: Health information packet sent from the device

[1089] Output: Health status information recorded in a database

[1090] Step 5: Capture and record payment information

[1091] The device collects purchase history data from the payment app and saves it in local storage. Specifically, it receives a notification every time the payment app updates the purchase history and writes the details to local storage. The collected data is sent to the server at regular intervals (for example, once a day).

[1092] Input: Purchase history data from payment app

[1093] Output: Payment information stored in the device's local storage and the payment information packet sent to the server

[1094] Step 6: Analyze payment information and record it in the database

[1095] The server analyzes the received payment information and records it in a database. The analysis method involves automatically classifying expenditure categories and understanding expenditure patterns. The analysis results are written to the database.

[1096] Input: Payment information packet sent from the terminal

[1097] Output: Spending pattern information recorded in a database

[1098] Step 7: Capture and record search information

[1099] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. Specifically, every time a user performs a search, that information is recorded in local storage. The collected search data is sent to the server at regular intervals (for example, once a day).

[1100] Input: Search history data from browsers and search engines

[1101] Output: Search information stored in the device's local storage and search information packets sent to the server

[1102] Step 8: Analyze search results and record database information

[1103] The server analyzes the received search information and records it in a database. The analysis method involves analyzing the frequency of search keywords and trends in search time periods. The analysis results are written to the database.

[1104] Input: Search information packet sent from the terminal

[1105] Output: Search interest information recorded in a database

[1106] Step 9: Retrieving and correlating weather information

[1107] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is associated with the location information and recorded in a database.

[1108] Input: Location information recorded on the server

[1109] Output: Weather data obtained from the weather information API, weather information associated with location information recorded in the database

[1110] Step 10: Automatic generation and conversion to diary format

[1111] The server automatically generates daily life logs based on the collected and analyzed data. Using a generative AI model, the server generates a life log in natural language by inputting prompts. The generated life log is converted into a diary format and displayed for users to view, add to, and edit through the application.

[1112] Input: Various information recorded in the database (location information, health information, payment information, search information, weather information)

[1113] Output: Automatically generated diary-formatted daily records

[1114] Step 11: Present ideas for improving your life

[1115] The server's AI analyzes the generated life log and presents lifestyle improvement suggestions based on the user's behavioral patterns and health status. Specific suggestions are displayed to the user as notifications.

[1116] Input: Automatically generated life log

[1117] Output: Suggestions for improving the user's lifestyle

[1118] (Application example 1)

[1119] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1120] In modern life, a variety of information is generated, including location information, healthcare information, payment information, search information, and weather information. However, there are only a limited number of systems that integrate this information to provide personalized services. In particular, food delivery services lack suggestions tailored to the user's health condition and lifestyle. This makes it difficult for users to select the optimal meal for their health condition and lifestyle.

[1121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1122] In this invention, the server includes means for acquiring location information, health care information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, means for the user to view, add to, and modify the life log, means for converting the automatically generated life log into a diary format, means for analyzing the life log and presenting ideas for improving lifestyle, means for providing a personalized food delivery service to the user based on the acquired information, means for proposing a meal plan tailored to the user's health condition based on the acquired health care information, and means for recommending optimal meal options based on the acquired location information and weather information. This allows the user to receive personalized meal suggestions tailored to their health condition and lifestyle.

[1123] "Location information" is data that indicates a user's current location or history, and is obtained using technologies such as GPS, Wi-Fi, and cell towers.

[1124] "Health information" refers to data that indicates a user's health and activity status, and is obtained through built-in sensors and applications, such as pedometers, heart rate monitors, and sleep trackers.

[1125] "Payment information" is data that indicates a user's purchase history and expenditures, and includes information such as the store name, purchase amount, and purchase date and time.

[1126] "Search information" is data that indicates the search keywords and browsing history used by a user on the Internet, and is obtained through a browser or search engine.

[1127] "Weather information" is data that indicates the weather conditions in a specific area and time, and includes elements such as temperature, probability of precipitation, and wind speed.

[1128] "Life Log" is a record of a user's daily life that is created by integrating location information, health information, payment information, search information, and weather information.

[1129] The "diary format" is a format in which daily events and data are written in chronological order to make the record of daily life easier for users to understand.

[1130] A "food delivery service" is a service that delivers meals ordered online by a user to a specified location.

[1131] A "meal plan" is a meal suggestion that takes into account the user's health condition and nutritional balance.

[1132] "Meal selection" refers to the act or process of a user selecting the optimal meal based on specific criteria (health status, location, weather, etc.).

[1133] This invention provides a system that collects a user's location information, healthcare information, payment information, search information, and weather information, and automatically generates a user's life log based on this data. The system is mainly composed of three entities: a server, a terminal (e.g., a smartphone), and the user.

[1134] Acquiring and recording location information

[1135] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc. The acquired location information is stored in local storage and periodically sent to the server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[1136] Acquisition and analysis of healthcare information

[1137] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[1138] Acquisition and recording of payment information

[1139] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[1140] Acquiring and analyzing search information

[1141] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[1142] Obtaining and linking weather information

[1143] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[1144] Providing personalized food delivery services

[1145] Based on the acquired information, the system can provide users with personalized food delivery services, such as recommending menu items from nearby restaurants based on the user's current location and taking weather information into account to suggest suitable meal choices.

[1146] Meal plan suggestions based on your health status

[1147] Based on the acquired health information, the system will propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[1148] Hardware and software used

[1149] The following hardware and software are used to realize this system.

[1150] Hardware: Smartphone (iOS / Android compatible), smartwatch (health data acquisition)

[1151] Software: Mobile applications (Swift, Kotlin), server side (Node.js, Python), databases (MongoDB, PostgreSQL)

[1152] Specific examples

[1153] 1. A user searches for "healthy restaurants near me."

[1154] 2. The system will recommend the best restaurant and menu based on the user's current location and health information.

[1155] 3. Example prompt: "Show restaurant suggestions based on the user's location and recommend calorie-friendly menu items based on their health data."

[1156] With the above configuration, users can receive a personalized food delivery service that is best suited to their health condition and lifestyle.

[1157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1158] Step 1: Obtain and record location information

[1159] The device periodically acquires the user's location information using technologies such as GPS, Wi-Fi, and cell towers. The acquired location information is stored in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[1160] Input: User location information (GPS data)

[1161] Data processing: saving to local storage, using reverse geocoding API

[1162] Output: Specific address or place name

[1163] Step 2: Acquire and analyze healthcare information

[1164] The device collects health data (such as steps taken, heart rate, and sleep time) from the health app and built-in sensors. The collected data is stored in local storage and periodically sent to a server. The server analyzes the received health data, records it in a database, and updates the user's daily record.

[1165] Input: User's health information (step count, heart rate, sleep time, etc.)

[1166] Data processing: saving to local storage, data analysis

[1167] Output: Analysis results, recording to database

[1168] Step 3: Capture and record payment information

[1169] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[1170] Input: User's payment information (purchase history data)

[1171] Data processing: saving to local storage, data analysis

[1172] Output: Analysis results, user spending patterns, records in database

[1173] Step 4: Obtaining and analyzing search information

[1174] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[1175] Input: User search information (search keywords, date and time)

[1176] Data processing: saving to local storage, data analysis

[1177] Output: Analysis results, user interests and needs

[1178] Step 5: Obtaining and Correlating Weather Information

[1179] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[1180] Input: User location information, weather information API data

[1181] Data processing: Use of weather information API, association with location information, recording in database

[1182] Output: Weather data, recorded in database

[1183] Step 6: Offer a personalized food delivery service

[1184] The server uses the collected information to provide users with personalized food delivery services, and the device recommends menu items from nearby restaurants based on the user's current location and takes weather information into account to suggest appropriate meal choices.

[1185] Input: User location information, payment information, weather information, health information

[1186] Data processing: information integration, application of recommendation algorithms

[1187] Output: Personalized menu recommendations, suggesting appropriate meal choices

[1188] Step 7: Suggested meal plan based on health status

[1189] The server uses the collected health information to propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[1190] Input: User's healthcare information

[1191] Data processing: analysis of health information, generation of meal plans

[1192] Output: Meal plan suggestions based on health status

[1193] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1194] The present invention combines a system that automatically generates a user's life log based on location information, healthcare information, payment information, search information, and weather information with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system are described below.

[1195] System configuration

[1196] This system is mainly composed of four components: a server, a device (e.g., a smartphone), a user, and an emotion engine. The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. The emotion engine also recognizes the user's emotional state and uses that information to personalize the lifestyle improvement ideas provided by the server.

[1197] Program processing

[1198] 1. Acquiring and recording location information

[1199] The device periodically acquires the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and temporarily stores it in local storage. At predetermined intervals, the acquired location information is sent to the server. The server then converts the received location information into specific addresses and facility names using a reverse geocoding API and stores them in a database.

[1200] 2. Acquisition and analysis of healthcare information

[1201] The device collects the user's health data from the healthcare app and built-in sensors and stores it in local storage. At regular intervals, the collected health data is sent to a server. The server analyzes the received health data, detects abnormal values ​​and identifies trends, records them in a database, and updates the user's lifestyle record.

[1202] 3. Acquisition and recording of payment information

[1203] The device collects purchase history data from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data to understand purchasing patterns and spending trends, and records them in a database.

[1204] 4. Acquisition and analysis of search information

[1205] The device collects browser and search engine history data and stores it in local storage. At regular intervals, the collected search data is sent to a server. The server analyzes the received search data to obtain information about the user's interests and needs, and records the analysis results in a database.

[1206] 5. Obtaining and linking weather information

[1207] The server periodically retrieves weather data based on each user's location using a weather information API. The retrieved weather data is stored in a database, associated with the user's location and a timestamp.

[1208] 6. Emotion recognition

[1209] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. This emotional data is also sent to the server.

[1210] 7. Automatic generation and conversion to diary format

[1211] The server automatically generates a daily life log based on the collected and analyzed data. The generated life log is output in diary format and can be viewed by the user. The user can view the automatically generated life log through the application and make additions or corrections as needed. Additions and corrections are saved on the server in real time.

[1212] 8. Presenting ideas for improving life

[1213] The server's AI analyzes the generated life log and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise may be advised to aim to walk more than 8,000 steps three days a week. Furthermore, depending on the user's emotional state, it can also suggest relaxation techniques and positive activities to reduce stress.

[1214] Specific examples

[1215] Day 1

[1216] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[1217] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[1218] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[1219] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[1220] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[1221] The device uses an emotion engine to analyze the user's emotional state from their voice and facial expressions and transmits the results to the server.

[1222] The server analyzes the user's emotional data and provides emotion-based advice such as, "You seem to be feeling stressed lately. Why not try 15 minutes of relaxation?"

[1223] Such a system would allow users to gain a deeper understanding of their lives, learn concrete ways to improve them, and receive advice that takes into account their emotional state.

[1224] The processing flow will be explained below.

[1225] Step 1:

[1226] The device periodically obtains the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and the obtained location information is temporarily stored in local storage in latitude and longitude format.

[1227] Step 2:

[1228] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[1229] Step 3:

[1230] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[1231] Step 4:

[1232] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor). The collected data (e.g., number of steps, heart rate, sleep time) is stored in local storage.

[1233] Step 5:

[1234] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1235] Step 6:

[1236] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[1237] Step 7:

[1238] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. The terminal receives a real-time notification at the time of payment, which triggers data collection.

[1239] Step 8:

[1240] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[1241] Step 9:

[1242] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[1243] Step 10:

[1244] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage.

[1245] Step 11:

[1246] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1247] Step 12:

[1248] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[1249] Step 13:

[1250] The server uses a weather information API based on each user's location information to periodically obtain relevant weather data (temperature, probability of precipitation, etc.).

[1251] Step 14:

[1252] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[1253] Step 15:

[1254] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. The emotion data is stored in local storage.

[1255] Step 16:

[1256] The device also transmits emotion data to the server at regular intervals, using a secure communication protocol such as HTTPS.

[1257] Step 17:

[1258] The server automatically generates daily life records based on the life record data and emotion data, and outputs the records in diary format for viewing by users.

[1259] Step 18:

[1260] Users can check the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved to the server in real time.

[1261] Step 19:

[1262] Based on the collected and analyzed data, the server's AI analyzes the user's behavioral patterns and health status, and generates ideas for improving lifestyles.

[1263] Step 20:

[1264] The server notifies the user of the generated lifestyle improvement ideas. For example, if the user's step count is insufficient, the server will suggest, "Today's step count was 7,000. You are 1,000 steps away from your goal of 8,000." The server will also suggest relaxation techniques and positive activities to reduce stress based on the user's emotional state.

[1265] Example 2

[1266] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1267] While existing technologies exist for automatically generating user life logs, they simply collect and record data and lack the flexibility to include specific suggestions for improving the user's emotional state or lifestyle. Furthermore, they lack a mechanism for providing personalized suggestions to improve quality of life. This makes it difficult for users to gain a deeper understanding of their own lives and identify specific behavioral improvements.

[1268] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, health information, purchase information, search information, and weather information, means for automatically generating a user's life record based on the acquired information, and means for recognizing the user's emotional state and storing the information in a database. This allows the user to understand their own life record in detail and receive specific suggestions for improving their lifestyle based on their emotional state.

[1269] "Location information" is data that indicates a user's current physical location and is obtained from GPS sensors, Wi-Fi, cell tower data, etc.

[1270] "Health information" is data that indicates the user's health condition and physical activity, and includes information such as the number of steps taken, heart rate, and sleep time.

[1271] "Purchase information" refers to transaction data when a user purchases a product or service, and includes the purchase date and time, amount, and store information.

[1272] "Search information" is data that indicates a user's interests and needs based on the search queries and browsing history that the user performs on the Internet.

[1273] "Weather information" refers to environmental data such as the weather, temperature, and humidity at the user's location, and is information obtained from weather APIs, etc.

[1274] "Life records" are data that show the activities and trends of a user's daily life, integrating the user's location information, health information, purchase information, search information, and weather information.

[1275] "Emotional state" is data that indicates a user's psychological or emotional state, and is information analyzed from voice, facial expressions, text input, etc.

[1276] A "database" is an information system for systematically storing, managing, and analyzing data such as a user's daily records and emotional state.

[1277] "Lifestyle Improvement Suggestions" are specific suggestions and advice based on the user's lifestyle records and emotional state, with the aim of improving the user's quality of life.

[1278] The present invention combines a system that automatically generates a user's life log based on location information, health information, purchase information, search information, and weather information with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system are described below.

[1279] System configuration

[1280] This system mainly consists of four components: a server, a terminal (e.g., a smart device), a user, and an emotion engine. The terminal collects various data about the user's life, and the server analyzes the data to generate a life record and provide it to the user. The emotion engine also recognizes the user's emotional state and uses that information to personalize the life improvement ideas provided by the server.

[1281] Program processing

[1282] Acquiring and recording location information

[1283] The device uses GPS sensors, Wi-Fi, and cell tower data to obtain the user's current location information and temporarily stores it in local storage. At regular intervals, the device sends the obtained location information to the server. The server then uses a reverse geocoding API to convert the received location information into specific addresses and facility names and stores them in a database.

[1284] Acquisition and analysis of health information

[1285] The device collects the user's health data from the healthcare app and built-in sensors and stores it in local storage. At regular intervals, the device sends the collected health data to a server. The server analyzes the received health data, detects abnormal values ​​and identifies trends, records them in a database, and updates the user's lifestyle record.

[1286] Acquisition and recording of purchasing information

[1287] The device collects purchase history data from the payment app and stores it in local storage. At regular intervals, the device sends the collected payment data to the server. The server analyzes the received payment data, identifies purchasing patterns and spending trends, and records them in a database.

[1288] Acquiring and analyzing search information

[1289] The device collects browser and search engine history data and stores it in local storage. At regular intervals, the device sends the collected search data to the server. The server analyzes the received search data, obtains information about the user's interests and needs, and records the analysis results in a database.

[1290] Obtaining and linking weather information

[1291] The server periodically retrieves relevant weather data using a weather information API based on each user's location information. The retrieved weather data is stored in a database, associated with the user's location information and a timestamp.

[1292] emotion recognition

[1293] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. This emotional data is also sent to the server.

[1294] Automatic generation and conversion to journal format

[1295] The server automatically generates a daily life record based on the collected and analyzed data. The generated life record is output in a diary format that can be viewed by the user. The user can view the automatically generated life record through the application and make additions or corrections as needed. Additions and corrections are saved on the server in real time.

[1296] Presenting ideas for improving lifestyles

[1297] The server's AI analyzes the generated life log and emotional data and makes lifestyle improvement suggestions based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise might be advised to aim to walk more than 8,000 steps three days a week. Furthermore, depending on the user's emotional state, it can also suggest relaxation techniques and positive activities to reduce stress.

[1298] Specific examples

[1299] Day 1

[1300] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[1301] The device collects data from the health app, showing that the user walked 7,000 steps and slept for seven hours.

[1302] The terminal collects purchasing information when a user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[1303] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[1304] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[1305] The device uses an emotion engine to analyze the user's emotional state from their voice and facial expressions and transmits the results to the server.

[1306] The server analyzes the user's emotional data and provides emotion-based advice such as, "You seem to be feeling stressed lately. Why not try 15 minutes of relaxation?"

[1307] Such a system would allow users to gain a deeper understanding of their lives, learn concrete ways to improve them, and receive advice that takes into account their emotional state.

[1308] Example prompts for generative AI models

[1309] "Generate a user's life record based on location, health, purchase, search, and weather information."

[1310] "Analyze users' emotions and provide emotion-based advice for improving their lives."

[1311] "Convert the user's daily activity record into a diary format and provide it via push notification."

[1312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1313] Step 1:

[1314] The device obtains the user's current location information using GPS sensors, Wi-Fi, and cell tower data. This allows the device to determine the user's location and temporarily store the obtained location information in local storage. For example, the device can determine the user's specific location using latitude and longitude information obtained from the GPS sensor and Wi-Fi connection information. The input is location data from the sensor, and the output is location information stored in local storage.

[1315] Step 2:

[1316] The device sends the acquired location information to the server at a predetermined interval (e.g., 30 minutes). Specifically, the location information is uploaded to the server at regular intervals using batch processing. The input is the location information saved in the local storage, and the output is the location information sent to the server.

[1317] Step 3:

[1318] The server converts the received location information into a specific address or facility name using a reverse geocoding API (e.g., map information API). In this conversion process, the coordinate information is input into the API to obtain detailed address information. The input is the transmitted location information, and the output is the converted address or facility name.

[1319] Step 4:

[1320] The server saves the converted address and facility name in a database. At this time, the location information is saved with a timestamp, making it possible to track where the user was at a later date. The input is the converted address data, and the output is the information saved in the database.

[1321] Step 5:

[1322] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate sensor). This data includes the number of steps, heart rate, sleep time, etc. The input is the health data measured by the sensors, and the output is the health data stored in local storage.

[1323] Step 6:

[1324] The device sends the collected health data to the server at a predetermined interval (e.g., once a day). The input is the health data stored in the local storage, and the output is the health data sent to the server.

[1325] Step 7:

[1326] The server analyzes the received health data to detect outliers and identify trends. For example, it uses an AI model to detect sudden fluctuations in heart rate or lack of sleep. The input is the transmitted health data, and the output is the analysis results.

[1327] Step 8:

[1328] The server records the analysis results in a database and updates the user's life record. This update process continuously stores daily health data for future trend analysis. The input is the analysis results, and the output is the updated life record.

[1329] Step 9:

[1330] The terminal collects purchase history data from a payment app (e.g., an electronic payment app). This includes the purchase date and time, amount, and store information. The input is transaction data from the payment app, and the output is purchase information stored in local storage.

[1331] Step 10:

[1332] The terminal sends the collected purchase data to the server at regular intervals (e.g., one week). The input is the purchase data stored in the local storage, and the output is the purchase data sent to the server.

[1333] Step 11:

[1334] The server analyzes the received purchase data to understand purchasing patterns and spending trends. For example, it uses machine learning algorithms to detect specific consumption habits and wasteful spending. The input is the submitted purchase data, and the output is the analysis results.

[1335] Step 12:

[1336] The server records the analysis results in a database and updates the user's purchase history. The input is the analysis results, and the output is the updated purchase history data.

[1337] Step 13:

[1338] The device collects browser and search engine history data, including user search queries and browsing history. The input is the history data from the browser and search engine, and the output is search information stored in local storage.

[1339] Step 14:

[1340] The device sends the search data collected at regular intervals (e.g., once a month) to the server. The input is the search data stored in the local storage, and the output is the search data sent to the server.

[1341] Step 15:

[1342] The server analyzes the received search data to obtain information about the user's interests and needs. For example, it uses natural language processing to analyze the search query and identify specific topics or issues of interest. The input is the submitted search data, and the output is the analysis results.

[1343] Step 16:

[1344] The server records the analysis results in a database and updates the user's interests and needs. The input is the analysis results and the output is updated interest data.

[1345] Step 17:

[1346] The server periodically obtains the relevant weather data using a weather information API based on each user's location information. The input is the user's location information, and the output is the obtained weather data.

[1347] Step 18:

[1348] The server associates the acquired weather data with location information and a timestamp and stores them in a database. The input is the acquired weather data, and the output is the weather information stored in the database.

[1349] Step 19:

[1350] The device analyzes the user's emotional state through an emotion engine based on the user's voice, facial expressions, and text input. For example, it analyzes the tone of the voice and evaluates the emotion using facial expression recognition software. The input is voice and facial expression data, and the output is analyzed emotional data.

[1351] Step 20:

[1352] The device sends the analyzed emotion data to the server. The input is the data analyzed by the emotion engine, and the output is the emotion data sent to the server.

[1353] Step 21:

[1354] The server stores the received emotion data in a database. The input is the transmitted emotion data, and the output is the emotion information stored in the database.

[1355] Step 22:

[1356] The server automatically generates a daily life log based on the collected and analyzed data. It uses a generative AI model to integrate multiple data sources and record the user's daily activities in detail. The input is multiple data sources (location information, health information, purchase information, search information, weather information, and emotion data), and the output is an automatically generated life log.

[1357] Step 23:

[1358] The server converts the generated life records into a diary format and outputs them in a viewable format for users. The input is the automatically generated life records, and the output is the life records converted into diary format.

[1359] Step 24:

[1360] The user can view the automatically generated life log through the application and add or modify it as needed. The input is the life log converted into a diary format, and the output is the added or modified record.

[1361] Step 25:

[1362] The server saves the additions and corrections in real time. The input is the life log updated by the user, and the output is the updated information stored in the database.

[1363] Step 26:

[1364] The server's AI analyzes the generated life records and emotional data and makes lifestyle improvement suggestions based on the user's behavioral patterns and health status. For example, it makes specific suggestions such as "aim to walk more than 8,000 steps three days a week." The input is the life records and emotional data to be analyzed, and the output is lifestyle improvement suggestions.

[1365] Step 27:

[1366] The server presents the generated lifestyle improvement suggestions to the user. The input is the lifestyle improvement suggestions, and the output is a notification or display to the user.

[1367] (Application example 2)

[1368] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1369] Conventional food delivery systems are unable to make suggestions that take into account the user's health information or emotional state, making it difficult to provide a service that meets the diverse needs of users. Furthermore, because they are unable to make personalized suggestions based on the user's emotions, weather, or health status, there is a need to improve service satisfaction. A new system that solves these issues is needed.

[1370] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring location information, health care information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, means including an emotion engine for analyzing the user's emotional state, means for providing personalized advice or suggestions to the user based on the acquired emotion information, means for the user to view, add to, and modify the life log, means for converting the automatically generated life log into a diary format, and means for analyzing the life log and emotion information and presenting ideas for improving the user's lifestyle. This makes it possible to suggest optimal food delivery options based on the user's emotions, health condition, and weather.

[1371] "Location information" is information that indicates the user's current location, obtained using GPS sensors, Wi-Fi, cell tower data, etc.

[1372] "Healthcare information" refers to data about a user's health status collected through health apps, built-in sensors, etc.

[1373] "Payment information" refers to information about a user's purchase history and expenditures obtained from the payment app.

[1374] "Search information" is information that indicates a user's interests and needs and is collected as browser and search engine history data.

[1375] "Weather information" refers to information about weather conditions at a specific location, obtained through a weather information API or the like.

[1376] An "emotion engine" is a system for analyzing a user's emotional state from their voice, facial expressions, text input, etc.

[1377] "User's life record" is data that records the user's daily activities and status, created based on location information, health information, payment information, search information, weather information, and emotional information.

[1378] "Personalized advice or suggestions" means specific advice or suggestions tailored to a user's particular needs or circumstances, based on the user's individual data and emotional state.

[1379] The "means for converting into diary format" is a function for converting the collected and analyzed data into a diary format that can be easily viewed and understood by the user.

[1380] "Ideas for improving lifestyle" is an approach that analyzes lifestyle records and emotional information to provide specific suggestions for improving users' behavior and health.

[1381] The system embodying the present invention collects and analyzes various data of users to provide personalized food delivery suggestions. This system acquires location information, health care information, payment information, search information, weather information, and emotion information, automatically generates a lifestyle log, and provides suggestions to users.

[1382] System configuration

[1383] The system mainly consists of the following components:

[1384] Server: The central element responsible for analyzing data and creating life records.

[1385] Device: A device that captures and transmits user data, such as a smartphone or tablet.

[1386] Emotion Engine: A system for analyzing the user's emotional state.

[1387] User: Receive advice and suggestions provided by the system and use them to improve their lives.

[1388] Data collection and analysis

[1389] The device acquires location information using GPS sensors, Wi-Fi, and cell tower data, and sends it to the server at regular intervals. The server then uses a reverse geocoding API to convert the information into addresses and facility names, which are then stored in a database.

[1390] The device collects user health data from the health app and built-in sensors and sends it to a server, which then analyzes the data to detect abnormalities and identify trends.

[1391] The device collects purchase history data from payment apps and sends it to a server, which analyzes purchase patterns and spending trends. Additionally, the device collects browser and search engine history data and sends it to a server to analyze user interests and needs.

[1392] The server periodically retrieves weather information based on the user's location using a weather information API, and stores the retrieved weather information in a database, associated with the location and a timestamp.

[1393] Analysis and utilization of emotional information

[1394] The device analyzes the user's emotional state through an emotion engine based on their voice, facial expressions, and text input. This emotion data is also sent to the server. For example, if a user inputs, "I am feeling a bit stressed and tired today," the emotion engine will determine that the user is in a "stressed" state.

[1395] Auto-generate and provide suggestions

[1396] The server automatically generates a daily life record based on the collected and analyzed data. The generated life record is displayed in diary format, and users can view it through the application and make additions or corrections as needed.

[1397] The server's AI analyzes the generated life log and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, if the user's emotional state is "stressed" and health data indicates a lack of exercise, it will suggest "eating foods that have a relaxing effect." It will also recommend "drinking warm soup" on rainy days.

[1398] As described above, the system of the present invention can comprehensively analyze a variety of user data and provide optimal food delivery suggestions based on emotions, health status, and weather. Users can refer to these suggestions and make more effective lifestyle improvements.

[1399] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1400] Step 1:

[1401] The device obtains the user's location information. Specifically, it uses GPS sensors, Wi-Fi, and cell tower data to determine the user's current location. This location data is temporarily stored in local storage and sent to the server at regular intervals. The input is the current location information, and the output is a data point containing the location information.

[1402] Step 2:

[1403] The server converts the received location information into a specific address or facility name using a reverse geocoding API. The input is location data, and the output is data containing the converted address or facility name. The server stores this data in a database.

[1404] Step 3:

[1405] The device collects the user's health data from the healthcare app and built-in sensors. The collected data includes the number of steps taken, heart rate, and sleep time. This data is also temporarily stored in local storage and sent to the server at regular intervals. The input is health data, and the output is the collected health data.

[1406] Step 4:

[1407] The server analyzes the received health data to detect abnormal values ​​and grasp trends. Specifically, it uses logic that issues a warning if the number of steps is below a certain level or if the heart rate is abnormal. The input is the received health data, and the output is the analysis results and trend information. This information is also stored in a database.

[1408] Step 5:

[1409] The terminal collects purchase history data from the payment app. The acquired purchase history data is temporarily stored in local storage and periodically sent to the server. The input is payment information, and the output is the collected purchase history data.

[1410] Step 6:

[1411] The server analyzes the received payment data to understand purchasing patterns and spending trends. For example, it analyzes what products are being purchased frequently and which areas of spending are increasing. The input is payment data, and the output is analyzed purchasing patterns and spending information. This data is also stored in a database.

[1412] Step 7:

[1413] The device collects browser and search engine history data. This data is collected as data for analyzing what information the user has searched for. This data is also temporarily stored in local storage and sent to the server at regular intervals. The input is search history data, and the output is the collected search history data.

[1414] Step 8:

[1415] The server analyzes the received search data to obtain information about the user's interests and needs. The input is search history data, and the output is analyzed information about the user's interests and needs. This data is also stored in a database.

[1416] Step 9:

[1417] The server periodically retrieves weather information based on the user's location using a weather API. The input is the location, and the output is the current weather data. This weather data is stored in a database, associated with the location and a timestamp.

[1418] Step 10:

[1419] The device analyzes the user's emotional state through an emotion engine based on their voice, facial expressions, text input, etc. For example, if a user inputs "I am feeling a bit stressed and tired today," the emotion engine analyzes the emotional state as "stressed." This emotion data is sent to the server. The input is the user input data for emotion analysis, and the output is the analyzed emotional state.

[1420] Step 11:

[1421] The server automatically generates a daily life log based on the collected and analyzed data, including location information, health information, payment information, search information, weather information, and emotion information. The input is these multiple data sources, and the output is the generated life log.

[1422] Step 12:

[1423] The server's AI analyzes the generated life records and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, it suggests "eating foods that have a relaxing effect" to a user who is not getting enough exercise. These suggestions are provided to the user via their device. The input is the life records and emotional data, and the output is ideas for lifestyle improvement.

[1424] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1425] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1426] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1427] [Fourth embodiment]

[1428] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1429] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1430] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1431] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1432] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1434] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1435] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1436] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1437] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1438] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1439] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1440] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1441] The present invention relates to a system that acquires location information, healthcare information, payment information, search information, and weather information and automatically generates a user's life log. Specific embodiments for implementing this system will be described below.

[1442] System configuration

[1443] This system mainly consists of three components: a server, a device (e.g., a smartphone), and a user. The device collects various data about the user's life, and the server analyzes the data to generate a life record and provide it to the user.

[1444] Program processing

[1445] 1. Acquiring and recording location information

[1446] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc., which records the user's current location and past movement history. The acquired location information is saved in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[1447] 2. Acquisition and analysis of healthcare information

[1448] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[1449] 3. Acquisition and recording of payment information

[1450] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[1451] 4. Acquisition and analysis of search information

[1452] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[1453] 5. Obtaining and linking weather information

[1454] The server uses a weather information API to obtain weather data (temperature, probability of precipitation, etc.) based on each user's location information. The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[1455] Automatic generation and conversion to diary format

[1456] The server automatically generates a daily life log based on the collected and analyzed data. Users can view this log through the application and add or modify it as needed. A function is also provided to convert the automatically generated life log into a diary format for easier viewing by users.

[1457] Presenting ideas for improving lifestyles

[1458] The server's AI analyzes the generated life log and suggests lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, it can provide specific suggestions to users who are not getting enough exercise, such as "walking at least 8,000 steps three days a week."

[1459] Specific examples

[1460] Day 1

[1461] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[1462] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[1463] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[1464] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[1465] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[1466] Such a system allows users to reflect on their lives and find concrete ways to improve them.

[1467] The processing flow will be explained below.

[1468] Step 1:

[1469] The device periodically obtains the user's current location information using the GPS sensor, Wi-Fi location information, and cell tower data. The obtained location information is temporarily stored in local storage in latitude and longitude format.

[1470] Step 2:

[1471] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[1472] Step 3:

[1473] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[1474] Step 4:

[1475] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor), and stores the collected data in local storage in the form of steps, heart rate, sleep time, etc.

[1476] Step 5:

[1477] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1478] Step 6:

[1479] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[1480] Step 7:

[1481] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app. The terminal receives a real-time notification at the time of payment, which triggers data collection. The collected data is saved in local storage.

[1482] Step 8:

[1483] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[1484] Step 9:

[1485] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[1486] Step 10:

[1487] The device collects browser and search engine history data (search keywords, date and time), and the collected search data is stored in local storage.

[1488] Step 11:

[1489] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1490] Step 12:

[1491] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[1492] Step 13:

[1493] The server uses a weather information API based on each user's location information to periodically obtain weather data (temperature, probability of precipitation, etc.) for the relevant area.

[1494] Step 14:

[1495] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[1496] Step 15:

[1497] The server automatically generates a daily life log from the collected and analyzed data, and outputs the log in diary format for viewing by the user.

[1498] Step 16:

[1499] Users can view the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved on the server in real time.

[1500] Step 17:

[1501] The server's AI analyzes the generated lifestyle records and generates lifestyle improvement ideas based on the user's behavioral patterns and health condition.

[1502] Step 18:

[1503] Based on the analysis results, the server notifies the user of lifestyle improvement suggestions (for example, "Aim to take 8,000 steps or more three days a week.") Notifications are sent via the application.

[1504] Example 1

[1505] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1506] Conventional lifestyle record systems only collect individual pieces of information and lack the functionality to integrate and analyze them comprehensively, making it difficult to provide specific suggestions that directly lead to improvements in users' lifestyles. Furthermore, there are limited ways to convert the collected information into a format that users can easily view and edit. This creates the challenge of preventing users from gaining a detailed understanding of their own lifestyles and making specific improvements.

[1507] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1508] In this invention, the server includes means for acquiring location information, health status information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, and means for the user to view, add to, and modify the life log. This makes it possible to integrate various information that has previously been collected individually and perform comprehensive analysis. It also makes it possible to provide specific lifestyle improvement suggestions to the user, and converting the log into a diary format makes it easier to view and modify.

[1509] "Location Information" means data that indicates a user's current geographic location and movement history obtained using technologies such as GPS, Wi-Fi, and cell towers.

[1510] "Health Information" refers to data related to your physical health collected from built-in sensors, such as pedometers, heart rate monitors, and sleep trackers, and from health apps.

[1511] "Payment information" refers to data related to the purchase history (store name, purchase amount, purchase date and time) made by the user through a payment app, etc.

[1512] "Search Information" refers to data regarding the search keywords and the date and time of the search performed by a user using a browser or search engine.

[1513] "Weather information" refers to weather-related data such as temperature and precipitation probability at a specific location, obtained through a weather information API.

[1514] "Life Log" is a record of a user's daily activities and status that is automatically generated based on location information, health status information, payment information, search information, and weather information.

[1515] "Reverse geocoding" is a technology that converts location information such as latitude and longitude into specific addresses or place names.

[1516] A "prompt sentence" is an instruction sentence that a generative AI model uses as input when generating natural language.

[1517] The present invention relates to a system that automatically generates a user's life log by integrating location information, health status information, payment information, search information, and weather information. This system mainly consists of three entities: a server, a terminal (e.g., a smartphone), and a user. Specific embodiments for implementing this system are described below.

[1518] System configuration

[1519] The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. Specifically, the device collects data using GPS sensors, healthcare apps, payment apps, browsers, etc. and periodically sends it to the server. The server processes the received data and runs software to automatically generate the life record.

[1520] Acquiring and recording location information

[1521] The device periodically acquires location information using GPS sensors, Wi-Fi, and cell towers. This information is stored in the device's local storage and periodically sent to a server. The server then sends the received location data to a reverse geocoding API, which converts it into a specific address or place name. The converted data is then recorded in a database.

[1522] Acquisition and analysis of healthcare information

[1523] The device uses built-in sensors and a health app to collect health status information from the pedometer, heart rate monitor, and sleep tracker. This data is stored in local storage and periodically sent to a server. The server analyzes the received data and records it in a database. Analysis methods include averaging the data and filtering outliers.

[1524] Acquisition and recording of payment information

[1525] The device collects purchase history data from the payment app. This information is stored in local storage and periodically sent to the server. The server analyzes the received payment data to automatically classify spending categories and understand spending patterns. The analysis results are recorded in a database.

[1526] Acquiring and analyzing search information

[1527] The device collects browser and search engine history data (search keywords, date and time). This information is stored in local storage and periodically sent to the server. The server analyzes the received search data to help understand the user's interests and needs. It also provides a content recommendation function based on this data.

[1528] Obtaining and linking weather information

[1529] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the location information and reflected in the user's daily life record.

[1530] Automatic generation and conversion to diary format

[1531] The server runs a program to automatically generate daily life logs based on the collected and analyzed data. This program uses a generative AI model and generates a life log in natural language by inputting prompt sentences. The generated life log is converted into a diary format and displayed in the application in a format that is easy for users to view.

[1532] Presenting ideas for improving lifestyles

[1533] The server's AI analyzes the generated lifestyle records and makes suggestions for lifestyle improvements based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise will receive specific suggestions such as "walk at least 8,000 steps three days a week." This allows users to reflect on their lifestyle in detail and identify areas for improvement.

[1534] Specific examples

[1535] The following specific scenarios are possible:

[1536] The device collects location information when the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[1537] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[1538] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[1539] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[1540] The server generates a lifestyle record based on this information and makes suggestions for improving your lifestyle, such as, "Today you took 7,000 steps. You're 1,000 steps away from your goal of 8,000 steps."

[1541] As described above, the present invention allows users to record their own lifestyle in detail and obtain specific methods for improving their lifestyle.

[1542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1543] Step 1: Obtain and record location information

[1544] The device acquires location information using the GPS sensor, Wi-Fi, and cell towers. The acquired location information is saved in the device's local storage. Specifically, the GPS sensor is activated every 60 seconds, and the acquired latitude and longitude information is written to the local storage. Every certain period (for example, every hour), the collected location data is compiled in JSON format and sent to the server using the HTTPS protocol.

[1545] Input: Latitude and longitude information obtained from the GPS sensor

[1546] Output: Location information stored in the device's local storage and location packets sent to the server

[1547] Step 2: Reverse geocoding and database recording

[1548] The server sends the received location information to a reverse geocoding API, which converts the latitude and longitude into specific addresses and place names. The information returned by the API is analyzed and recorded in a database. Specifically, multiple latitudes and longitudes are requested from the API, and the corresponding addresses and place names are obtained. The obtained geographic information is written to the database.

[1549] Input: Location information packet (latitude and longitude) sent from the device

[1550] Output: Specific geographic information recorded in a database

[1551] Step 3: Capture and record healthcare information

[1552] The device uses built-in sensors and the healthcare app to collect health status information such as heart rate, number of steps, and sleep time. The collected data is stored in the device's local storage. Specifically, the device queries the healthcare API every 30 minutes and adds the obtained data to the local storage. The collected health information is then sent to the server at regular intervals (for example, once a day).

[1553] Input: Health status information obtained from the health app or built-in sensors

[1554] Output: Health status information stored in the device's local storage and health information packets sent to the server

[1555] Step 4: Analyze health information and record it in the database

[1556] The server analyzes the received health status information and records it in a database. Specifically, it normalizes the received data, filters out outliers, and then aggregates the data for each user and writes it to the database.

[1557] Input: Health information packet sent from the device

[1558] Output: Health status information recorded in a database

[1559] Step 5: Capture and record payment information

[1560] The device collects purchase history data from the payment app and stores it in local storage. Specifically, it receives a notification every time the payment app updates the purchase history and writes the details to local storage. The collected data is sent to the server at regular intervals (for example, once a day).

[1561] Input: Purchase history data from payment app

[1562] Output: Payment information stored in the device's local storage and the payment information packet sent to the server

[1563] Step 6: Analyze payment information and record it in the database

[1564] The server analyzes the received payment information and records it in a database. The analysis method involves automatically classifying expenditure categories and understanding expenditure patterns. The analysis results are written to the database.

[1565] Input: Payment information packet sent from the terminal

[1566] Output: Spending pattern information recorded in a database

[1567] Step 7: Capture and record search information

[1568] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. Specifically, every time a user performs a search, the information is recorded in local storage. The collected search data is sent to the server at regular intervals (for example, once a day).

[1569] Input: Search history data from browsers and search engines

[1570] Output: Search information stored in the device's local storage and search information packets sent to the server

[1571] Step 8: Analyze search results and record database information

[1572] The server analyzes the received search information and records it in a database. The analysis method involves analyzing the frequency of search keywords and trends in search time periods. The analysis results are written to the database.

[1573] Input: Search information packet sent from the terminal

[1574] Output: Search interest information recorded in a database

[1575] Step 9: Retrieving and correlating weather information

[1576] The server calls the weather information API based on each user's location information and obtains the relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is associated with the location information and recorded in a database.

[1577] Input: Location information recorded on the server

[1578] Output: Weather data obtained from the weather information API, weather information associated with location information recorded in the database

[1579] Step 10: Automatic generation and conversion to diary format

[1580] The server automatically generates daily life logs based on the collected and analyzed data. Using a generative AI model, the server generates a life log in natural language by inputting prompts. The generated life log is converted into a diary format and displayed for users to view, add to, and edit through the application.

[1581] Input: Various information recorded in the database (location information, health information, payment information, search information, weather information)

[1582] Output: Automatically generated diary-formatted daily records

[1583] Step 11: Present ideas for improving your life

[1584] The server's AI analyzes the generated life log and presents lifestyle improvement suggestions based on the user's behavioral patterns and health status. Specific suggestions are displayed to the user as notifications.

[1585] Input: Automatically generated life log

[1586] Output: Suggestions for improving the user's lifestyle

[1587] (Application example 1)

[1588] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1589] In modern life, a variety of information is generated, including location information, healthcare information, payment information, search information, and weather information. However, there are only a limited number of systems that integrate this information to provide personalized services. In particular, food delivery services lack suggestions tailored to the user's health condition and lifestyle. This makes it difficult for users to select the optimal meal for their health condition and lifestyle.

[1590] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1591] In this invention, the server includes means for acquiring location information, health care information, payment information, search information, and weather information, means for automatically generating a user's life log based on the acquired information, means for the user to view, add to, and modify the life log, means for converting the automatically generated life log into a diary format, means for analyzing the life log and presenting ideas for improving lifestyle, means for providing a personalized food delivery service to the user based on the acquired information, means for proposing a meal plan tailored to the user's health condition based on the acquired health care information, and means for recommending optimal meal options based on the acquired location information and weather information. This allows the user to receive personalized meal suggestions tailored to their health condition and lifestyle.

[1592] "Location information" is data that indicates a user's current location or history, and is obtained using technologies such as GPS, Wi-Fi, and cell towers.

[1593] "Health information" refers to data that indicates a user's health and activity status, and is obtained through built-in sensors and applications, such as pedometers, heart rate monitors, and sleep trackers.

[1594] "Payment information" is data that indicates a user's purchase history and expenditures, and includes information such as the store name, purchase amount, and purchase date and time.

[1595] "Search information" is data that indicates the search keywords and browsing history used by a user on the Internet, and is obtained through a browser or search engine.

[1596] "Weather information" is data that indicates the weather conditions in a specific area and time, and includes elements such as temperature, probability of precipitation, and wind speed.

[1597] "Life Log" is a record of a user's daily life that is created by integrating location information, health information, payment information, search information, and weather information.

[1598] The "diary format" is a format in which daily events and data are written in chronological order to make the record of daily life easier for users to understand.

[1599] A "food delivery service" is a service that delivers meals ordered online by a user to a specified location.

[1600] A "meal plan" is a meal suggestion that takes into account the user's health condition and nutritional balance.

[1601] "Meal selection" refers to the act or process of a user selecting the optimal meal based on specific criteria (health status, location, weather, etc.).

[1602] This invention provides a system that collects a user's location information, healthcare information, payment information, search information, and weather information, and automatically generates a user's life log based on this data. The system is mainly composed of three entities: a server, a terminal (e.g., a smartphone), and the user.

[1603] Acquiring and recording location information

[1604] The device periodically acquires location information using GPS, Wi-Fi, cell towers, etc. The acquired location information is stored in local storage and periodically sent to the server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[1605] Acquisition and analysis of healthcare information

[1606] The device collects health data from the healthcare app and built-in sensors (e.g., pedometer and heart rate monitor). The collected data (number of steps, heart rate, sleep time, etc.) is stored in local storage and periodically sent to a server. The server analyzes the received healthcare data, records it in a database, and updates the user's daily record.

[1607] Acquisition and recording of payment information

[1608] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[1609] Acquiring and analyzing search information

[1610] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[1611] Obtaining and linking weather information

[1612] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[1613] Providing personalized food delivery services

[1614] Based on the acquired information, the system can provide users with personalized food delivery services, such as recommending menu items from nearby restaurants based on the user's current location and taking weather information into account to suggest suitable meal choices.

[1615] Meal plan suggestions based on your health status

[1616] Based on the acquired health information, the system will propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[1617] Hardware and software used

[1618] The following hardware and software are used to realize this system.

[1619] Hardware: Smartphone (iOS / Android compatible), smartwatch (health data acquisition)

[1620] Software: Mobile applications (Swift, Kotlin), server side (Node.js, Python), databases (MongoDB, PostgreSQL)

[1621] Specific examples

[1622] 1. A user searches for "healthy restaurants near me."

[1623] 2. The system will recommend the best restaurant and menu based on the user's current location and health information.

[1624] 3. Example prompt: "Show restaurant suggestions based on the user's location and recommend calorie-friendly menu items based on their health data."

[1625] With the above configuration, users can receive a personalized food delivery service that is best suited to their health condition and lifestyle.

[1626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1627] Step 1: Obtain and record location information

[1628] The device periodically acquires the user's location information using technologies such as GPS, Wi-Fi, and cell towers. The acquired location information is stored in local storage and periodically sent to a server. The server converts the received location information into a specific address or place name using a reverse geocoding API and records it in a database.

[1629] Input: User location information (GPS data)

[1630] Data processing: saving to local storage, using reverse geocoding API

[1631] Output: Specific address or place name

[1632] Step 2: Acquire and analyze healthcare information

[1633] The device collects health data (such as steps taken, heart rate, and sleep time) from the health app and built-in sensors. The collected data is stored in local storage and periodically sent to a server. The server analyzes the received health data, records it in a database, and updates the user's daily record.

[1634] Input: User's health information (step count, heart rate, sleep time, etc.)

[1635] Data processing: saving to local storage, data analysis

[1636] Output: Analysis results, recording to database

[1637] Step 3: Capture and record payment information

[1638] The device collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data, understands the user's spending patterns and tendencies, and records them in a database.

[1639] Input: User's payment information (purchase history data)

[1640] Data processing: saving to local storage, data analysis

[1641] Output: Analysis results, user spending patterns, records in database

[1642] Step 4: Obtaining and analyzing search information

[1643] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage. At regular intervals, the collected search data is sent to the server. The server analyzes the received search data and provides information to understand the user's interests and needs.

[1644] Input: User search information (search keywords, date and time)

[1645] Data processing: saving to local storage, data analysis

[1646] Output: Analysis results, user interests and needs

[1647] Step 5: Obtaining and Correlating Weather Information

[1648] The server uses a weather information API based on each user's location information to obtain relevant weather data (temperature, probability of precipitation, etc.). The obtained weather data is recorded in a database along with the user's location information and reflected in the user's daily life log.

[1649] Input: User location information, weather information API data

[1650] Data processing: Use of weather information API, association with location information, recording in database

[1651] Output: Weather data, recorded in database

[1652] Step 6: Offer a personalized food delivery service

[1653] The server uses the collected information to provide users with personalized food delivery services, and the device recommends menu items from nearby restaurants based on the user's current location and takes weather information into account to suggest appropriate meal choices.

[1654] Input: User location information, payment information, weather information, health information

[1655] Data processing: information integration, application of recommendation algorithms

[1656] Output: Personalized menu recommendations, suggesting appropriate meal choices

[1657] Step 7: Suggested meal plan based on health status

[1658] The server uses the collected health information to propose a meal plan tailored to the user's health condition, recommending lighter meals on days when exercise is low and higher protein meals on days when exercise is high.

[1659] Input: User's healthcare information

[1660] Data processing: analysis of health information, generation of meal plans

[1661] Output: Meal plan suggestions based on health status

[1662] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1663] The present invention combines a system that automatically generates a user's life log based on location information, healthcare information, payment information, search information, and weather information with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this system are described below.

[1664] System configuration

[1665] This system is mainly composed of four components: a server, a device (e.g., a smartphone), a user, and an emotion engine. The device collects various data about the user's life, and the server analyzes that data to generate a life record and provide it to the user. The emotion engine also recognizes the user's emotional state and uses that information to personalize the lifestyle improvement ideas provided by the server.

[1666] Program processing

[1667] 1. Acquiring and recording location information

[1668] The device periodically acquires the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and temporarily stores it in local storage. At predetermined intervals, the acquired location information is sent to the server. The server then converts the received location information into specific addresses and facility names using a reverse geocoding API and stores them in a database.

[1669] 2. Acquisition and analysis of healthcare information

[1670] The device collects the user's health data from the healthcare app and built-in sensors and stores it in local storage. At regular intervals, the collected health data is sent to a server. The server analyzes the received health data, detects abnormal values ​​and identifies trends, records them in a database, and updates the user's lifestyle record.

[1671] 3. Acquisition and recording of payment information

[1672] The device collects purchase history data from the payment app and stores it in local storage. At regular intervals, the collected payment data is sent to the server. The server analyzes the received payment data to understand purchasing patterns and spending trends, and records them in a database.

[1673] 4. Acquisition and analysis of search information

[1674] The device collects browser and search engine history data and stores it in local storage. At regular intervals, the collected search data is sent to a server. The server analyzes the received search data to obtain information about the user's interests and needs, and records the analysis results in a database.

[1675] 5. Obtaining and linking weather information

[1676] The server periodically retrieves weather data based on each user's location using a weather information API. The retrieved weather data is stored in a database, associated with the user's location and a timestamp.

[1677] 6. Emotion recognition

[1678] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. This emotional data is also sent to the server.

[1679] 7. Automatic generation and conversion to diary format

[1680] The server automatically generates a daily life log based on the collected and analyzed data. The generated life log is output in diary format and can be viewed by the user. The user can view the automatically generated life log through the application and make additions or corrections as needed. Additions and corrections are saved on the server in real time.

[1681] 8. Presenting ideas for improving life

[1682] The server's AI analyzes the generated life log and emotional data and provides lifestyle improvement ideas based on the user's behavioral patterns and health status. For example, a user who is not getting enough exercise may be advised to aim to walk more than 8,000 steps three days a week. Furthermore, depending on the user's emotional state, it can also suggest relaxation techniques and positive activities to reduce stress.

[1683] Specific examples

[1684] Day 1

[1685] The device collects location information as the user leaves home at 9:00, arrives at the cafe at 9:30, and arrives at work at 10:00.

[1686] The device collects data from a health app, showing that the user walked 7,000 steps and slept for seven hours.

[1687] The terminal collects payment information when the user spends 500 yen at a convenience store at 12:00 and 1,500 yen at a supermarket at 19:00.

[1688] The device collects data on users' searches for "healthy lunch recipes" at 11:00 and "nearby running spots" at 16:00.

[1689] The server generates a lifestyle record based on this information and makes suggestions to the user for lifestyle improvements, such as, "Today's steps were 7,000. You're 1,000 steps away from your goal of 8,000 steps."

[1690] The device uses an emotion engine to analyze the user's emotional state from their voice and facial expressions and transmits the results to the server.

[1691] The server analyzes the user's emotional data and provides emotion-based advice such as, "You seem to be feeling stressed lately. Why not try 15 minutes of relaxation?"

[1692] Such a system would allow users to gain a deeper understanding of their lives, learn concrete ways to improve them, and receive advice that takes into account their emotional state.

[1693] The processing flow will be explained below.

[1694] Step 1:

[1695] The device periodically obtains the user's current location information using GPS sensors, Wi-Fi, and cell tower data, and the obtained location information is temporarily stored in local storage in latitude and longitude format.

[1696] Step 2:

[1697] The device sends the acquired location information to the server at regular intervals, using a secure communication protocol such as HTTPS.

[1698] Step 3:

[1699] The server analyzes the received location information and converts the latitude and longitude information into specific addresses and facility names using a reverse geocoding API. The converted data is then stored in a database.

[1700] Step 4:

[1701] The device collects user health data from the health app and built-in sensors (e.g., pedometer, heart rate monitor). The collected data (e.g., number of steps, heart rate, sleep time) is stored in local storage.

[1702] Step 5:

[1703] The device transmits healthcare data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1704] Step 6:

[1705] The server analyzes the received healthcare data to detect abnormal values ​​and identify trends. The analysis results are recorded in a database, and each user's life log is updated.

[1706] Step 7:

[1707] The terminal collects purchase history data (store name, purchase amount, purchase date and time) from the payment app and stores it in local storage. The terminal receives a real-time notification at the time of payment, which triggers data collection.

[1708] Step 8:

[1709] The terminal transmits the collected payment data to the server at predetermined intervals, using a secure communication protocol such as HTTPS.

[1710] Step 9:

[1711] The server analyzes the received payment data to understand purchasing patterns and spending trends, and records the analysis results in a database, updating each user's life log.

[1712] Step 10:

[1713] The device collects browser and search engine history data (search keywords, date and time) and stores it in local storage.

[1714] Step 11:

[1715] The device sends the collected search data to the server at regular intervals using a secure communication protocol such as HTTPS.

[1716] Step 12:

[1717] The server analyzes the received search data to understand information about the user's interests and needs. The analysis results are recorded in a database, and a life log is updated for each user.

[1718] Step 13:

[1719] The server uses a weather information API based on each user's location information to periodically obtain relevant weather data (temperature, probability of precipitation, etc.).

[1720] Step 14:

[1721] The server associates the acquired weather data with the user's location and timestamp, and stores it in a database. This information is then reflected in the user's daily life log.

[1722] Step 15:

[1723] The device uses an emotion engine to analyze the user's emotional state based on their voice, facial expressions, text input, etc. The emotion data is stored in local storage.

[1724] Step 16:

[1725] The device also transmits emotion data to the server at regular intervals, using a secure communication protocol such as HTTPS.

[1726] Step 17:

[1727] The server automatically generates daily life records based on the life record data and emotion data, and outputs the records in diary format for viewing by users.

[1728] Step 18:

[1729] Users can check the automatically generated life log through the application and add or modify it as needed. Additions and modifications are saved to the server in real time.

[1730] Step 19:

[1731] Based on the collected and analyzed data, the server's AI analyzes the user's behavioral patterns and health status, and generates ideas for improving lifestyles.

[1732] Step 20:

[1733] The server notifies the user of the generated lifestyle improvement ideas. For example, if the user's step count is insufficient, the server will suggest, "Today's step count was 7,000. You are 1,000 steps away from your goal of 8,000." The server will also suggest relaxation techniques and positive activities to reduce stress based on the user's emotional state.

[1734] Example 2

[1735] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1736] While existing technologies exist for automatically generating user life logs, they simply collect and record data and lack the flexibility to include specific suggestions for improving the user's emotional state or lifestyle. Furthermore, they lack a mechanism for providing personalized suggestions to improve quality of life. This makes it difficult for users to gain a deeper understanding of their own lives and identify specific behavioral improvements.

[1737] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring location information, health information, purchase information, search information, and weather information, means for automatically generating a user's life record based on the acquired information, and means for recognizing the user's emotional state and storing the information in a database. This allows the user to understand their own life record in detail and receive specific suggestions for improving their lifestyle based on their emotional state.

[1738] "Location information" is data that indicates a user's current physical location and is obtained from GPS sensors, W...

Claims

1. a means for obtaining location information, health care information, payment information, search information, and weather information; A means for automatically generating a user's life record based on the acquired information; A means for a user to view, add, and modify the life log; A means to convert the automatically generated life records into a diary format, A means for analyzing the life record and presenting ideas for improving life; A system including:

2. The system according to claim 1, wherein the system records the user's movement history based on the acquired location information.

3. The system according to claim 1, further comprising: analyzing the user's health condition based on the acquired healthcare information; and recording the information.

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