System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving user input data; means for using an artificial intelligence algorithm to analyze the received data; means for generating user-customized habits and action plans based on the analysis; means for displaying the generated habits and action plans to the user; means for receiving and monitoring user progress data; and means for generating and providing feedback to the user based on the progress data.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, many people find it difficult to put the knowledge they gain from self-improvement and business books into practice. Furthermore, the advice in these books is often abstract and difficult to translate into concrete actions. Furthermore, there are limited tools available that provide customized advice tailored to individual lifestyles and work situations. This invention aims to solve these problems and more effectively support individuals in achieving their goals. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving user input data, a means for using an artificial intelligence algorithm to analyze the received data, a means for generating a habit and action plan customized for the user based on the analysis results, a means for displaying the generated habit and action plan to the user, a means for receiving and monitoring the user's progress data, and a means for generating and providing feedback to the user based on the progress data. The analysis means can perform a detailed analysis of the user's daily life, work situation, and personal goals to propose an optimal action plan for the user. The generation means also generates feedback to suggest specific actions and habit modifications based on the progress. This system allows the user to obtain a specific and customized action plan to effectively achieve their goals.

[0006] "User-input data" is information entered by users about their daily lives, work situations, and personal goals.

[0007] "Means for receiving" refers to the technical means for acquiring user input data and progress data and incorporating them into the system.

[0008] "Artificial intelligence algorithms for analysis" refers to artificial intelligence technologies used to analyze user input data and continuously generate optimal advice and action plans.

[0009] A "customized habit and action plan" is a set of specific actions to be performed that are specially designed based on the user's individual goals and lifestyle patterns.

[0010] The "display means" refers to a display or interface for visually presenting the generated habits and behavioral plans to the user.

[0011] "Progress Data" is information that users report on their daily activities and the progress of their plans.

[0012] "Monitoring means" means the technical means for continuously tracking a user's progress data and understanding their progress.

[0013] "Means for generating and providing feedback" refers to the technical means for generating and communicating corrective or additional advice to the user based on the user's progress. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates a customized habit and action plan for the user, displays it to the user, and monitors progress and provides feedback.

[0036] Explaining program processing in natural language

[0037] 1. Enter user data

[0038] The device presents the user with a form prompting them to enter data about their daily life, work situation, and personal goals.

[0039] The user enters this data and sends it to the terminal.

[0040] The terminal transmits the input data to the server.

[0041] 2. Data Receipt and Analysis

[0042] The server receives the user data from the terminal.

[0043] The server analyzes the received data using artificial intelligence algorithms.

[0044] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[0045] 3. Generate customized habits and action plans

[0046] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle and goals.

[0047] For example, the server might suggest things like "walking for 30 minutes every morning at 7:30" or "training at the gym three times a week."

[0048] The server sends the generated plan to the terminal, which displays it to the user.

[0049] 4. Entering and monitoring progress data

[0050] The user inputs progress data into the device regarding the activities performed and the degree of achievement.

[0051] The device sends progress data to the server.

[0052] The server receives and monitors the progress data.

[0053] 5. Generating and Providing Feedback

[0054] The server generates corrections and additional advice for the user based on their progress.

[0055] For example, the server provides feedback such as "keep walking" or "record your meals for the next week."

[0056] The server sends the feedback to the device, which displays it to the user.

[0057] Specific examples

[0058] For example, if a user sets a goal to lose 5 kg in 3 months:

[0059] 1. Enter user data

[0060] When a user inputs "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the device sends this to the server.

[0061] 2. Data Receipt and Analysis

[0062] The server receives this data and analyzes it using AI algorithms, which may determine, for example, that morning exercise is more effective based on a user's activity patterns and work hours.

[0063] 3. Generate customized habits and action plans

[0064] The server generates a specific plan such as "walk for 30 minutes every morning at 7:30," "train at the gym three times a week," and "record your food intake after each meal," and the device displays this to the user.

[0065] 4. Entering and monitoring progress data

[0066] When the user enters progress data such as "I have completed today's walk" or "I have recorded today's meals," the device sends this to the server.

[0067] The server monitors the progress data and evaluates the percentage of completion and any necessary corrections.

[0068] 5. Generating and Providing Feedback

[0069] Based on the progress data, the server generates feedback such as "continue walking for the next week" or "improve the quality of your diet," and the device displays this to the user.

[0070] In this way, the present invention provides a specific action plan tailored to the user's individual needs and supports them in achieving their goals.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[0074] Step 2:

[0075] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[0076] Step 3:

[0077] The terminal transmits the entered user data to the server.

[0078] Step 4:

[0079] The server receives the user data from the device for analysis.

[0080] Step 5:

[0081] The server uses artificial intelligence algorithms to analyze the received data, including the user's daily life patterns, work schedule, and actions required to achieve goals.

[0082] Step 6:

[0083] Based on the analysis results, the server generates optimal habits and action plans for the user, such as suggesting a 30-minute walk every morning at 7:30 and going to the gym three times a week.

[0084] Step 7:

[0085] The server transmits the generated customized habits and action plan to the terminal.

[0086] Step 8:

[0087] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[0088] Step 9:

[0089] The user inputs daily progress data into the device, such as "I completed today's walk" or "I recorded today's meals."

[0090] Step 10:

[0091] The device sends progress data to the server.

[0092] Step 11:

[0093] The server receives the progress data and monitors the user's progress. Progress evaluation includes the degree of goal achievement and any necessary corrections.

[0094] Step 12:

[0095] The server uses the progress data to generate feedback and additional advice for the user, such as suggestions like "continue walking for the next week" or "improve the quality of your diet."

[0096] Step 13:

[0097] The server generates feedback and sends it to the terminal, which displays it to the user.

[0098] Example 1

[0099] 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."

[0100] Conventional methods have the problem of making customized action plans based on a user's lifestyle and personal goals, monitoring progress, and providing appropriate feedback. Because users have diverse lifestyles and general advice cannot create effective action plans, an individually adapted support system is needed.

[0101] 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.

[0102] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring the user's progress data, means for generating and providing feedback to the user based on the progress data, means for inputting the user's input to the terminal through an interface, means for the terminal to transmit the input data to the server via the Internet, means for the artificial intelligence algorithm to identify a customized action plan based on the user's lifestyle and goals, means for the terminal to display the specific action plan to the user, means for inputting the user's activity progress to the terminal and transmitting it to the server, and means for the server to analyze the progress data and generate necessary improvements and additional advice, thereby providing a specific action plan tailored to the user's individual needs and supporting them in achieving their goals.

[0103] "User" refers to the individual person or entity who uses the System.

[0104] "Input data" is information users provide to the system, including information about their daily lives, work situations, personal goals, etc.

[0105] "Means for receiving" refers to the mechanism by which the system obtains input data from the user.

[0106] "Artificial intelligence algorithm" refers to an intelligent processing method that uses a computer program to analyze data and automatically perform a specific task.

[0107] "Customized Habits and Action Plans" refers to individually adapted instructions for actions and habits generated based on user input data.

[0108] "Means for displaying" refers to a mechanism for visually conveying the generated habits and action plans to the user.

[0109] "Progress Data" means information about the activities you have undertaken and your progress towards achieving them.

[0110] "Means for monitoring" refers to the mechanism by which the system tracks and evaluates user progress data.

[0111] "Means for generating feedback" refers to a mechanism for providing corrective or additional advice to the user based on progress data.

[0112] "Interface" refers to the means or devices by which a user interacts with a system.

[0113] "Terminal" refers to a device such as a computer or smartphone used by a user.

[0114] "Means for transmitting to a server via the Internet" refers to the communication function for transmitting data from a terminal to a server.

[0115] "Lifestyle" refers to a user's daily life patterns and habits.

[0116] A "goal" is a specific outcome or objective that a user wants to achieve.

[0117] "Activity progress" refers to the degree to which a user has performed planned actions or habits.

[0118] "Means for generating necessary improvements or additional advice" refers to a mechanism for generating improvement suggestions or supplemental advice to the user based on progress data.

[0119] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates and displays customized habits and action plans to the user, monitoring their progress and providing feedback.

[0120] System configuration

[0121] 1. Hardware Configuration

[0122] Device: A device for user input and display, such as a smartphone, computer, or tablet.

[0123] Server: A high-performance computer system for receiving, analyzing, and managing data.

[0124] 2. Software Configuration

[0125] Generative AI models: Algorithms that analyze data and make predictions using Python and TensorFlow.

[0126] Interface: Acts as a web browser or mobile application and interacts with the user.

[0127] Processing flow and specific examples

[0128] Entering User Data

[0129] Users access a form on their smartphone or computer to enter data about their daily life, work situation, and personal goals, such as "I wake up at 7am," "I go to bed at 11pm," "I work five days a week," and "I want to lose 5kg in three months."

[0130] Receiving and analyzing data

[0131] The device sends the input data over the internet to a server, which then analyzes it using artificial intelligence algorithms to identify the optimal plan of action based on the user's lifestyle and goals.

[0132] Generate customized habits and action plans

[0133] Based on the analysis results, the server generates a specific action plan tailored to the user's lifestyle and goals. For example, it might suggest "walking for 30 minutes every day at 7:30 a.m." or "training at the gym three times a week." This plan is then sent back to the device and displayed to the user.

[0134] Entering and monitoring progress data

[0135] As the user performs the planned activity, they enter their progress into the device, which then sends it to the server, which monitors the progress data. For example, if the user enters "I completed today's walk," the server records the data and evaluates the activity's achievement.

[0136] Generating and Providing Feedback

[0137] Based on the progress data, the server generates necessary improvements and additional advice. For example, it generates feedback such as "continue walking for the next week" or "improve the quality of your diet." The generated feedback is sent to the device and displayed to the user.

[0138] Examples of prompt statements

[0139] The following is an example of a prompt sentence to be input to the generative AI model used in this invention:

[0140] "Generate an action plan to lose 5 kg in 3 months. The user's lifestyle is 'Wake up at 7 am, go to bed at 11 pm, work 5 days a week.'"

[0141] "The user's goal is to live a healthy life. Please suggest a customized activity plan based on their daily schedule."

[0142] In this way, the present invention can provide a specific action plan tailored to the individual needs of the user and support them in achieving their goals.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1: Entering User Data

[0145] 1. The device presents the user with a form to fill out, displaying questions about the user's daily life, work situation, and personal goals.

[0146] Input: The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work 5 days a week," and "lose 5kg in 3 months."

[0147] Specific operation: The user enters each item on the device screen and presses the send button.

[0148] Output: Correctly entered data is saved on the device and ready to be sent to the server.

[0149] Step 2: Receiving and analyzing data

[0150] 1. The device sends user data to a server via the Internet.

[0151] Input: Data entered by the terminal and sent to the server.

[0152] Specific operation: The device transmits user data over a secure channel via an Internet connection.

[0153] Output: The server receives the user data.

[0154] 2. The server analyzes the data using artificial intelligence algorithms.

[0155] Input: Received user data.

[0156] How it works: The server analyzes the data using a generative AI model built in Python and generates analytical results based on the user's lifestyle and goals.

[0157] Output: Information about the appropriate habits and action plans as a result of the analysis.

[0158] Step 3: Generate a customized habit and action plan

[0159] 1. Based on the analysis results, the server generates habits and action plans tailored to the user's lifestyle and goals.

[0160] Input: Analysis results.

[0161] Specific Action: The generated action plan is specified through a generative AI model.

[0162] Output: A customized action plan.

[0163] 2. The server sends the generated plan to the terminal.

[0164] Input: The generated action plan.

[0165] Specific operation: The server sends the action plan to the terminal via the Internet.

[0166] Output: The action plan arrives on the terminal.

[0167] 3. The device displays the action plan to the user.

[0168] Input: Action plan sent by the server.

[0169] Specific action: The device visually displays the action plan on the screen.

[0170] Output: User can see the action plan.

[0171] Step 4: Enter and monitor progress data

[0172] 1. The user enters the progress of the activity into the terminal.

[0173] Input: User activity progress data (e.g., "Completed today's walk").

[0174] What it does: Users report their progress within the app and save their data.

[0175] Output: Progress data saved on the device.

[0176] 2. The device sends the progress data to the server.

[0177] Input: Progress data stored on the device.

[0178] Specific operation: The device sends progress data to the server via the Internet.

[0179] Output: The server receives the progress data.

[0180] 3. The server monitors the progress data and evaluates the progress.

[0181] Input: Received progress data.

[0182] Specific operation: Analyzes progress data on the server and evaluates achievement status.

[0183] Output: Assessment data on progress.

[0184] Step 5: Generate and provide feedback

[0185] 1. The server generates advice on necessary improvements and additions based on progress data.

[0186] Input: Assessment data on progress.

[0187] Specific behavior: A generative AI model analyzes progress data and generates appropriate feedback.

[0188] Output: The generated feedback.

[0189] 2. The server sends the generated feedback to the device.

[0190] Input: Generated feedback.

[0191] Specific operation: The server sends feedback to the device via the Internet.

[0192] Output: Feedback data received on the device.

[0193] 3. The device displays feedback to the user.

[0194] Input: Feedback sent by the server.

[0195] Specific behavior: The device will visually display feedback on the screen.

[0196] Output: The user checks the feedback and uses it to guide their next action.

[0197] In this way, the system provides specific action plans and feedback adapted to the user's individual needs, supporting them in achieving their goals.

[0198] (Application example 1)

[0199] 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."

[0200] Conventional shopping systems struggle to effectively reflect users' individual preferences and purchasing patterns, making it difficult to provide personalized product recommendations and promotions. Furthermore, users have to spend time searching for products in physical stores, which makes it difficult to have an efficient shopping experience. Furthermore, efforts to improve user satisfaction are lacking because user progress data and feedback are not utilized.

[0201] 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.

[0202] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's purchasing history, preferences, and purchasing patterns and suggesting customized products and promotions, and means for providing navigation within a physical store using the user's location information. This enables a personalized shopping experience for each user, improves product search efficiency, and increases user satisfaction.

[0203] "User input data" refers to information about lifestyle habits, goals, preferences, purchasing history, etc. that users provide to the system.

[0204] An "artificial intelligence algorithm" is a computational method or model that analyzes input data and creates optimal action plans and product recommendations for users.

[0205] "Customized habits and action plans" are suggestions for specific actions and habits that are individually tailored based on the user's input data.

[0206] "User Progress Data" means information about actions and achievements performed by a User.

[0207] "Monitoring" means continuously tracking a user's progress data and evaluating their achievements.

[0208] "Feedback" refers to providing users with behavioral or habit modifications or additional advice based on their progress data.

[0209] "Purchase history" is information about products purchased by a user in the past.

[0210] "Preferences" refers to information that indicates a user's tendency to like certain brands or products.

[0211] "Purchase patterns" are data that indicate the tendency of users to purchase what products and when.

[0212] "Customized products and promotions" refers to individually optimized product recommendations and special offers provided based on an analysis of a user's purchasing history, preferences, and purchasing patterns.

[0213] "Location information" is data that indicates a user's current physical location.

[0214] "In-store navigation" refers to using a user's location to provide directions to specific products or sections within a store.

[0215] The invention is embodied in the form of a smartphone application for brick-and-mortar stores that analyzes user input data and provides customized product recommendations and promotions.

[0216] First, the server receives user input data: the user uses a smartphone to input data about their shopping history, preferences, and purchasing patterns, and the device that receives this data then sends it to the server.

[0217] The server then uses an artificial intelligence algorithm to analyze the received data. This algorithm is built using machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, product recommendations and promotional information tailored to the user are generated. For example, based on data such as "You recently purchased running shoes" or "Your favorite brand is Nike," related products and sale information will be recommended.

[0218] The generated product recommendations and promotion information are displayed to the user via the device, using a smartphone app, allowing the user to view the recommended products and promotions.

[0219] Furthermore, to help users efficiently find products in physical stores, the server provides navigation using the user's location information. Location information is acquired using the smartphone's GPS sensor and beacons installed in the store. This allows users to be guided to the desired shelf or section in the store.

[0220] The user's progress data (such as product purchase information and movement history within the store) is received and monitored by the server. Based on this, the server generates feedback and provides it to the user via the device. For example, "Next time you visit, there will be a sale on new Nike apparel."

[0221] As an example of implementation, if a user enters "My favorite brand is Nike," "I recently bought running shoes," and "I want to know about sales," the system will act as follows:

[0222] 1. Receiving user input data:

[0223] The user enters information into the app, such as "My favorite brand is Nike," and the device sends this to the server.

[0224] 2. Data Receipt and Analysis:

[0225] The server then analyzes this data using AI algorithms to identify the best product recommendations and promotions for the user.

[0226] 3. Generate customized product recommendations:

[0227] Based on the analysis results, related products such as Nike sportswear are listed.

[0228] 4. Product List Display:

[0229] Display a list of recommended products to the user.

[0230] 5. Obtaining user location information:

[0231] The user's location within the store is obtained using GPS sensors and beacons.

[0232] 6. Route guidance:

[0233] Navigate users to Nike's sportswear section in a physical store.

[0234] Example prompt sentence:

[0235] "Users input their preferred brands and product purchase history. We then analyze the user data to provide relevant product recommendations."

[0236] Generative AI models can be used to create a personalized shopping experience for each user, improving product discovery efficiency while also increasing user satisfaction.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] The device receives user input data. The user uses a smartphone app to input information such as lifestyle habits, shopping history, preferences, and purchasing patterns. Examples of input data include "My favorite brand is Nike" and "I recently purchased a pair of running shoes." The device receives this data and sends it to the server.

[0240] Step 2:

[0241] The server receives user data sent from the device. The input data includes the user's personal preferences and purchasing history. The received data is temporarily stored to be fed directly into the AI ​​algorithm.

[0242] Step 3:

[0243] The server analyzes the received data using artificial intelligence algorithms. Here, models built using machine learning libraries such as TensorFlow and PyTorch are used. Specifically, user data is input into the AI ​​model, which then makes predictions. For example, if a user inputs information like "Nike" and "running shoes," related products are recommended. The output generated by the AI ​​model is a customized product list.

[0244] Step 4:

[0245] Based on the analysis results of the AI ​​algorithm, the server generates a product list and promotion information customized for the user. The generated information includes optimal products and sale information based on the user's preferences and purchasing patterns. For example, recommendations such as "Nike sportswear" and "sale on running shoes" are made. This information is sent to the device.

[0246] Step 5:

[0247] The device receives customized product lists and promotional information from the server and displays them to the user. Based on the information displayed, the user can access and check specific product and sale information. For example, the app screen might display "New Nike sportswear" or "Special sale on running shoes."

[0248] Step 6:

[0249] The server uses the user's location information to provide navigation within the physical store. Location information is obtained from the smartphone's GPS sensor and beacons installed in the store. The server identifies the user's current location and calculates the optimal route to the desired shelf or section. Navigation information is sent to the device.

[0250] Step 7:

[0251] The device provides the user with navigation information sent from the server. Users can check route guidance on the app screen and navigate efficiently within the physical store. For example, a detailed route to reach the "Nike sportswear section" is displayed.

[0252] Step 8:

[0253] The device records the user's actions (product purchase information, store movement history, etc.) and sends the progress data to the server. The progress data includes information on whether the user purchased the suggested products and how they moved around the store. The server receives this data and monitors the progress.

[0254] Step 9:

[0255] The server analyzes the progress data and generates feedback, such as "There will be a sale on new Nike apparel on your next visit." The generated feedback is sent to the device.

[0256] Step 10:

[0257] The device provides the user with the feedback it receives from the server. The user can check the feedback through the app and use it to make their next purchase. For example, a notification will appear on the app screen saying, "Get a great deal on new Nike apparel on your next visit."

[0258] 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.

[0259] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input data and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[0260] Explaining program processing in natural language

[0261] 1. Enter user data

[0262] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[0263] The user enters this data and sends it to the terminal.

[0264] The terminal transmits the input data to the server.

[0265] 2. Entering Emotion Data

[0266] The terminal or compatible device collects the user's emotional data in real time and transmits it to the server.

[0267] 3. Data Receipt and Analysis

[0268] The server receives user data and emotion data from the terminal.

[0269] The server analyzes the received data using artificial intelligence algorithms and emotion engines.

[0270] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[0271] The emotion engine also analyzes the user's emotional data and makes adjustments based on the user's emotional state.

[0272] 4. Generate customized habits and action plans

[0273] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[0274] For example, the server might suggest things like "walking 30 minutes at 7:30 every morning," "going to the gym three times a week," or "adding a short meditation session on days when stress levels are high."

[0275] The server sends the generated plan to the terminal, which displays it to the user.

[0276] 5. Entering and monitoring progress data

[0277] Progress data regarding the activities performed by the user and changes in emotions are entered into the device.

[0278] The terminal transmits the progress data and emotion data to the server.

[0279] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[0280] 6. Generating and Providing Feedback

[0281] The server generates corrective or additional advice for the user based on the progress and emotional data.

[0282] For example, the server provides feedback such as "keep walking," "record your food intake for the next week," or "do a short meditation when you feel stressed."

[0283] The server sends the feedback to the device, which displays it to the user.

[0284] Specific examples

[0285] Example of a user setting a goal to lose 5kg in 3 months and also prioritizing stress management:

[0286] 1. Enter user data

[0287] The user enters "Wake up at 7am every day, go to bed at 11pm," "Work five days a week," and "Lose 5kg in three months," and the device sends this to the server.

[0288] 2. Entering Emotion Data

[0289] The user inputs their daily emotional state (such as stress level and mood), and the device sends this to the server.

[0290] 3. Data Receipt and Analysis

[0291] The server receives this data and analyzes it using AI algorithms and an emotion engine. For example, based on a user's activity patterns, work hours, and emotional state, it may determine that morning exercise is beneficial, or that relaxation activities are needed on days when stress levels are high.

[0292] 4. Generate customized habits and action plans

[0293] The server generates a specific plan such as "30 minutes of walking every morning at 7:30," "go to the gym three times a week," or "10 minutes of meditation on stressful days," and the device displays this to the user.

[0294] 5. Entering and monitoring progress data

[0295] The user enters progress data such as "I have completed today's walk" or "Today's emotional state is stress level 3," and the device sends this to the server.

[0296] The server monitors the progress and emotional data to assess achievement and emotional state.

[0297] 6. Generating and Providing Feedback

[0298] The server generates feedback such as "continue walking for the next week," "improve the quality of your diet," or "engage in a short meditation session the next time you feel stressed," and the device displays this to the user.

[0299] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[0303] Step 2:

[0304] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[0305] Step 3:

[0306] The terminal transmits the entered user data to the server.

[0307] Step 4:

[0308] The terminal or a compatible device collects the user's emotional data in real time, for example, by using the user's voice tone, facial expression recognition, self-evaluation, etc.

[0309] Step 5:

[0310] The device transmits the collected emotion data to a server.

[0311] Step 6:

[0312] A server receives the user data and the emotion data.

[0313] Step 7:

[0314] The server uses artificial intelligence algorithms and emotion engines to analyze the received data, such as analyzing the user's daily life patterns, working hours, and emotional state.

[0315] Step 8:

[0316] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[0317] Step 9:

[0318] The server transmits the generated habits and action plans to the terminal.

[0319] Step 10:

[0320] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[0321] Step 11:

[0322] Users input data about their daily progress and emotional state into the device, such as "Completed today's walk" or "Today's stress level is 3."

[0323] Step 12:

[0324] The terminal transmits the progress data and emotion data to the server.

[0325] Step 13:

[0326] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[0327] Step 14:

[0328] Based on the progress and emotional data, the server generates corrective or additional advice for the user, such as "keep walking," "improve the quality of your diet," or "take a short meditation session when you feel stressed."

[0329] Step 15:

[0330] The server generates feedback and sends it to the device.

[0331] Step 16:

[0332] The device displays feedback to the user, allowing them to see their next steps.

[0333] Example 2

[0334] 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."

[0335] Conventional systems have difficulty generating specific habits and action plans tailored to a user's individual needs and emotional state, and providing appropriate feedback based on the user's progress and emotional changes. This leads to a lack of comprehensive support for users to achieve their goals and manage their emotional state.

[0336] 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.

[0337] In this invention, the server includes means for using an artificial intelligence algorithm and an emotion engine to receive and analyze user input data and emotion data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring the user's progress data and emotion data, and means for generating and providing feedback to the user based on the progress data and emotion data, thereby providing a specific action plan tailored to the user's individual needs and emotional state and enabling comprehensive support for goal achievement and emotion management.

[0338] "User input data" is data that a user provides to the system about their lifestyle, work situation, personal goals, etc.

[0339] An "artificial intelligence algorithm" is a program that has computational methods for analyzing data, recognizing patterns, and making predictions.

[0340] An "emotion engine" is an algorithm or model that analyzes a user's emotional data and understands their emotional state.

[0341] "Customized habits and action plans" are specific action plans that are individually generated based on the user's input data and analysis results.

[0342] "Means for displaying the generated habits and behavioral plans to the user" refers to techniques or methods for visually presenting the generated behavioral plans to the user through a terminal or device.

[0343] "User progress data" refers to data that a user records and provides to the system regarding their daily activities and progress toward achieving their goals.

[0344] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes information such as heart rate, stress level, and mood.

[0345] "Means for monitoring" refers to the functions and methods by which the system continuously monitors and records the user's progress data and emotional data.

[0346] "Means for generating and providing feedback to the user" refers to the method by which the system makes useful advice or adjustments based on progress data and emotional data and communicates them to the user.

[0347] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[0348] The program of this system uses the following specific hardware and software to carry out a series of processes such as data entry, data analysis, generation of action plans, progress monitoring, and provision of feedback.

[0349] (Hardware and software used)

[0350] Hardware: Servers, devices (PCs, smartphones, tablets, etc.), biosensors (smartwatches, etc.)

[0351] Software: Artificial intelligence algorithms (TensorFlow, PyTorch, etc.), emotion engines, database management systems (MySQL, PostgreSQL, etc.)

[0352] As an example of specific behavior, the following prompt sentence is input into the generative AI model:

[0353] "I'm aiming to lose 5 kg in 3 months, but my work schedule is busy and I'm under a lot of stress. Could you please give me some advice on my daily activity schedule and stress management?"

[0354] (Example)

[0355] 1. Enter user data

[0356] The device presents the user with a form, which could be a web form or an app prompt, to enter data about their daily life, work situation, or personal goals.

[0357] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[0358] The security of the data is ensured by the device encrypting the data entered and sending it to the server.

[0359] 2. Entering Emotion Data

[0360] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time to understand the user's emotional state.

[0361] Users may also manually enter their mood or stress level, for example, entering "3 (high)" in response to the question, "What is your stress level today?"

[0362] The device transmits the collected and input emotion data to the server as needed.

[0363] 3. Data Receipt and Analysis

[0364] The server receives the user data and emotion data transmitted from the terminal.

[0365] The server first stores the received data in a database, then analyzes it using artificial intelligence algorithms and an emotion engine to generate a customized plan based on the user's activity patterns and goals.

[0366] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[0367] 4. Generate customized habits and action plans

[0368] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[0369] The server formats the generated action plan and sends it to the device in a format such as JSON.

[0370] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[0371] 5. Entering and monitoring progress data

[0372] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[0373] The device periodically sends this progress data to a server, and may also have an automatic synchronization function.

[0374] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[0375] 6. Generating and Providing Feedback

[0376] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[0377] The server generates feedback, formats it, and sends it to the device, where notifications can be used to instantly notify the user.

[0378] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[0379] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[0380] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0381] Step 1:

[0382] Entering User Data

[0383] The device will prompt the user to enter information about their daily life, work situation, and personal goals, either through a web form or an app input screen.

[0384] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[0385] The device sends the user's input data in JSON format to the server, which encrypts the data before sending it to ensure its security.

[0386] Input: Data about lifestyle habits and goals that users enter into forms

[0387] Output: User data sent to the server in JSON format.

[0388] Step 2:

[0389] Entering emotion data

[0390] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time, which can then be used to understand the user's emotional state.

[0391] Users may also manually enter their mood or stress level within the app, for example by entering "3 (high)" in response to the question "What is your stress level today?"

[0392] The device periodically sends emotion data in JSON format to the server.

[0393] Input: User's heart rate, sweat rate, self-input stress level, and other emotional data

[0394] Output: Emotion data in JSON format sent to the server

[0395] Step 3:

[0396] Receiving and analyzing data

[0397] The server receives the user data and emotion data sent from the device, and the received data is first stored in a database.

[0398] The server analyzes the data using artificial intelligence algorithms and emotion engines (using TensorFlow and PyTorch), and generates a customized plan based on the user's activity patterns and goals.

[0399] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[0400] Input: User data and emotion data in JSON format sent to the server

[0401] Output: Customized habits and action plans

[0402] Step 4:

[0403] Generate customized habits and action plans

[0404] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[0405] The server sends the generated action plan in JSON format to the terminal.

[0406] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[0407] Input: Output data of the AI ​​algorithm based on the analysis results

[0408] Output: A customized action plan in JSON format

[0409] Step 5:

[0410] Entering and monitoring progress data

[0411] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[0412] The device periodically sends this progress data to the server in JSON format, and may also have an automatic synchronization function.

[0413] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[0414] Input: Progress and emotion data entered by the user

[0415] Output: Progress and emotion data sent to the server in JSON format.

[0416] Step 6:

[0417] Generating and Providing Feedback

[0418] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[0419] The server generates feedback and sends it to the device in JSON format, allowing the user to be notified immediately using notifications.

[0420] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[0421] Input: User progress and emotion data

[0422] Output: Feedback in JSON format

[0423] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[0424] (Application example 2)

[0425] 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."

[0426] In modern brick-and-mortar stores, it is difficult to quickly and efficiently make customized product recommendations based on individual customer preferences and emotional states. In particular, there is a lack of technology that can analyze a customer's ongoing shopping experience in real time and make optimal product recommendations. This can result in lower customer satisfaction and a loss of purchasing motivation.

[0427] 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 receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's profile and emotional data in-store and generating product suggestions tailored to individual preferences, and means for displaying the suggested products to the user in-store. This makes it possible to customize the shopping experience in physical stores in real time and improve customer satisfaction and purchasing motivation.

[0428] "User Input Data" means information provided manually or automatically by a User regarding their profile, preferences, lifestyle, budget, etc.

[0429] "Artificial intelligence algorithms" refers to the machine learning models and other AI techniques used for analysis and interpretation of received data.

[0430] "Means for generating customized habits and action plans based on analysis results" refers to the process of setting and proposing optimal action plans and habits to users based on the analysis results.

[0431] "Means for displaying the generated habits and behavioral plan to the user" refers to a mechanism for visually presenting the proposed plan to the user using the user's device, an in-store display, etc.

[0432] "User progress data" refers to information about the actions taken by a user and the results achieved, and is data used to monitor progress.

[0433] "Means for generating and providing feedback to users based on progress data" refers to the process of generating improvements and additional advice based on the user's progress and notifying the user.

[0434] "Means for analyzing user profile and emotional data in-store to generate product suggestions tailored to individual preferences" refers to the process of analyzing data provided by users in-store in real time and suggesting products that suit their individual preferences.

[0435] "Means for displaying suggested products to users in-store" refers to a system that uses in-store displays and users' devices to visually provide product suggestions based on analysis.

[0436] In order to implement this invention, it is necessary to build a system that collects user input data and emotion data, analyzes the data, and generates customized habits and action plans. Specific embodiments will be described below.

[0437] System configuration

[0438] The system of the present invention comprises the following main components:

[0439] 1. User device: Smartphone or tablet, etc. Collects user input data and emotion data and sends them to the server.

[0440] 2. Server: Analyzes the received data, generates customized habits and action plans, and provides feedback.

[0441] 3. Display device: This can be a display in a store or a user's device. It displays the generated plans and product suggestions to the user.

[0442] Data entry and analysis

[0443] The device provides users with an input form about their daily life, work situation, and personal goals. This allows users to enter data such as "I like the outdoors," "My style is casual," and "My budget is between ¥5,000 and ¥15,000." It can also collect real-time emotional data (e.g., "Stress level 4," "I'm happy")

[0444] Generate customized proposals

[0445] The server uses an artificial intelligence algorithm to analyze the user's profile data and emotional data received from the device. Based on the results, it generates optimal product suggestions for the user, such as "relaxing outdoor wear" or "casual jeans." The suggestions are then displayed on displays in the store and on the user's device.

[0446] Progress data monitoring and feedback

[0447] When a user enters the results of purchasing or trying on a suggested product into the terminal, the server receives and monitors this as progress data. For example, data such as "Purchased items: T-shirt, jeans" and "Feedback: Satisfied" can be used. Based on this, the server generates additional feedback and provides advice to the user, such as "Try this color T-shirt next time."

[0448] Hardware and software used

[0449] EmotionAnalyzer: Software for analyzing emotional data.

[0450] Recommender: Software that makes product suggestions based on user profiles.

[0451] RESTful API: A protocol for data communication between a terminal and a server.

[0452] Specific examples

[0453] If User A likes "outdoor wear" and "casual style" and enters "stress level 4" as emotion data, the following process will occur:

[0454] The server analyzes the data received and suggests "casual, outdoor wear" that will help users relax.

[0455] Displays within the store include "casual outdoor jackets" and "relaxing items."

[0456] The user purchases or tries on the suggested product and then enters and submits their feedback on the device.

[0457] The server uses this progress data to improve its next suggestion and provides feedback such as, "Next time, try matching pants with this jacket."

[0458] Prompt Sentence Examples

[0459] Here are some example prompts for a generative AI model:

[0460] Suggest the best products to the user based on their profile (style: "casual", size: "M", budget: "¥5000 - ¥15000", lifestyle: "outdoor") and emotional data (emotion: "happy", intensity: 7).

[0461] In this way, the present invention can provide a specific action plan tailored to the user's individual needs and emotional state, enhancing the in-store shopping experience.

[0462] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0463] Step 1:

[0464] The user terminal collects user input data. It provides a form for the user to enter information about their daily life, preferences, budget, and lifestyle, and the user manually enters this data. The user terminal receives this data and sends it to the server. The input at this time is, for example, "Style: Casual," "Size: M," "Budget: ¥5,000 - ¥15,000," and "Lifestyle: Outdoor." The output is that this user data is sent to the server.

[0465] Step 2:

[0466] The user device also collects emotional data. It provides a means for users to input their emotional state in real time while shopping, collecting emotional data such as "Stress level: 4" or "Mood: happy." The user device receives this emotional data and sends it to the server. The input is data about the user's emotional state, and the output is the transmission of this data to the server.

[0467] Step 3:

[0468] The server analyzes the received user profile data and emotional data. The server uses EmotionAnalyzer software to analyze the emotional state and Recommender software to generate product suggestions based on the user profile. The input data is the user profile and emotional data, and specific product suggestions such as "relaxed outdoor jacket" and "casual jeans" are generated as a result of data analysis. The output is the analysis results and the suggested products.

[0469] Step 4:

[0470] The server sends the generated product suggestions to the user's terminal or a display device in the store. The user's terminal or display visually presents the suggested products to the user. For example, the display may show "Recommended product: casual outdoor jacket." The input is the suggested product data from the server, and the output is the display of this product information to the user.

[0471] Step 5:

[0472] The user tries on the suggested products and enters the results of their purchase into the terminal. For example, progress data such as "Purchased item: T-shirt" and "Feedback: Satisfied" is entered. The user terminal receives this data and sends it to the server. The input data is the user's progress information, and the output is that this is sent to the server.

[0473] Step 6:

[0474] The server receives the progress data and monitors the user's behavior. Based on the progress data, the server generates the next feedback and provides it to the user. For example, personalized advice such as "Try this color T-shirt next time" is generated. The input is the progress data, and the feedback is generated as a result of data analysis. The output is the feedback provided to the user.

[0475] Step 7:

[0476] The server tracks the feedback provided to the user and continuously evaluates the user's satisfaction and behavioral improvements, allowing it to continue optimizing the user's shopping experience. The input is the user's response to the feedback and additional progress data, and the output is the next suggestion or improvement of the feedback.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] [Second embodiment]

[0481] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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).

[0487] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] In the smart glasses 214, the 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.

[0492] 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."

[0493] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates a customized habit and action plan for the user, displays it to the user, and monitors progress and provides feedback.

[0494] Explaining program processing in natural language

[0495] 1. Enter user data

[0496] The device presents the user with a form prompting them to enter data about their daily life, work situation, and personal goals.

[0497] The user enters this data and sends it to the terminal.

[0498] The terminal transmits the input data to the server.

[0499] 2. Data Receipt and Analysis

[0500] The server receives the user data from the terminal.

[0501] The server analyzes the received data using artificial intelligence algorithms.

[0502] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[0503] 3. Generate customized habits and action plans

[0504] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle and goals.

[0505] For example, the server might suggest things like "walking for 30 minutes every morning at 7:30" or "training at the gym three times a week."

[0506] The server sends the generated plan to the terminal, which displays it to the user.

[0507] 4. Entering and monitoring progress data

[0508] The user inputs progress data into the device regarding the activities performed and the degree of achievement.

[0509] The device sends progress data to the server.

[0510] The server receives and monitors the progress data.

[0511] 5. Generating and Providing Feedback

[0512] The server generates corrections and additional advice for the user based on their progress.

[0513] For example, the server provides feedback such as "keep walking" or "record your meals for the next week."

[0514] The server sends the feedback to the device, which displays it to the user.

[0515] Specific examples

[0516] For example, if a user sets a goal to lose 5 kg in 3 months:

[0517] 1. Enter user data

[0518] When a user inputs "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the device sends this to the server.

[0519] 2. Data Receipt and Analysis

[0520] The server receives this data and analyzes it using AI algorithms, which may determine, for example, that morning exercise is more effective based on a user's activity patterns and work hours.

[0521] 3. Generate customized habits and action plans

[0522] The server generates a specific plan such as "walk for 30 minutes every morning at 7:30," "train at the gym three times a week," and "record your food intake after each meal," and the device displays this to the user.

[0523] 4. Entering and monitoring progress data

[0524] When the user enters progress data such as "I have completed today's walk" or "I have recorded today's meals," the device sends this to the server.

[0525] The server monitors the progress data and evaluates the percentage of completion and any necessary corrections.

[0526] 5. Generating and Providing Feedback

[0527] Based on the progress data, the server generates feedback such as "continue walking for the next week" or "improve the quality of your diet," and the device displays this to the user.

[0528] In this way, the present invention provides a specific action plan tailored to the user's individual needs and supports them in achieving their goals.

[0529] The processing flow will be explained below.

[0530] Step 1:

[0531] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[0532] Step 2:

[0533] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[0534] Step 3:

[0535] The terminal transmits the entered user data to the server.

[0536] Step 4:

[0537] The server receives the user data from the device for analysis.

[0538] Step 5:

[0539] The server uses artificial intelligence algorithms to analyze the received data, including the user's daily life patterns, work schedule, and actions required to achieve goals.

[0540] Step 6:

[0541] Based on the analysis results, the server generates optimal habits and action plans for the user, such as suggesting a 30-minute walk every morning at 7:30 and going to the gym three times a week.

[0542] Step 7:

[0543] The server transmits the generated customized habits and action plan to the terminal.

[0544] Step 8:

[0545] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[0546] Step 9:

[0547] The user inputs daily progress data into the device, such as "I completed today's walk" or "I recorded today's meals."

[0548] Step 10:

[0549] The device sends progress data to the server.

[0550] Step 11:

[0551] The server receives the progress data and monitors the user's progress. Progress evaluation includes the degree of goal achievement and any necessary corrections.

[0552] Step 12:

[0553] The server uses the progress data to generate feedback and additional advice for the user, such as suggestions like "continue walking for the next week" or "improve the quality of your diet."

[0554] Step 13:

[0555] The server generates feedback and sends it to the terminal, which displays it to the user.

[0556] Example 1

[0557] 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."

[0558] Conventional methods have the problem of making customized action plans based on a user's lifestyle and personal goals, monitoring progress, and providing appropriate feedback. Because users have diverse lifestyles and general advice cannot create effective action plans, an individually adapted support system is needed.

[0559] 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.

[0560] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring the user's progress data, means for generating and providing feedback to the user based on the progress data, means for inputting the user's input to the terminal through an interface, means for the terminal to transmit the input data to the server via the Internet, means for the artificial intelligence algorithm to identify a customized action plan based on the user's lifestyle and goals, means for the terminal to display the specific action plan to the user, means for inputting the user's activity progress to the terminal and transmitting it to the server, and means for the server to analyze the progress data and generate necessary improvements and additional advice, thereby providing a specific action plan tailored to the user's individual needs and supporting them in achieving their goals.

[0561] "User" refers to the individual person or entity who uses the System.

[0562] "Input data" is information users provide to the system, including information about their daily lives, work situations, personal goals, etc.

[0563] "Means for receiving" refers to the mechanism by which the system obtains input data from the user.

[0564] "Artificial intelligence algorithm" refers to an intelligent processing method that uses a computer program to analyze data and automatically perform a specific task.

[0565] "Customized Habits and Action Plans" refers to individually adapted instructions for actions and habits generated based on user input data.

[0566] "Means for displaying" refers to a mechanism for visually conveying the generated habits and action plans to the user.

[0567] "Progress Data" means information about the activities you have undertaken and your progress towards achieving them.

[0568] "Means for monitoring" refers to the mechanism by which the system tracks and evaluates user progress data.

[0569] "Means for generating feedback" refers to a mechanism for providing corrective or additional advice to the user based on progress data.

[0570] "Interface" refers to the means or devices by which a user interacts with a system.

[0571] "Terminal" refers to a device such as a computer or smartphone used by a user.

[0572] "Means for transmitting to a server via the Internet" refers to the communication function for transmitting data from a terminal to a server.

[0573] "Lifestyle" refers to a user's daily life patterns and habits.

[0574] A "goal" is a specific outcome or objective that a user wants to achieve.

[0575] "Activity progress" refers to the degree to which a user has performed planned actions or habits.

[0576] "Means for generating necessary improvements or additional advice" refers to a mechanism for generating improvement suggestions or supplemental advice to the user based on progress data.

[0577] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates and displays customized habits and action plans to the user, monitoring their progress and providing feedback.

[0578] System configuration

[0579] 1. Hardware Configuration

[0580] Device: A device for user input and display, such as a smartphone, computer, or tablet.

[0581] Server: A high-performance computer system for receiving, analyzing, and managing data.

[0582] 2. Software Configuration

[0583] Generative AI models: Algorithms that analyze data and make predictions using Python and TensorFlow.

[0584] Interface: Acts as a web browser or mobile application and interacts with the user.

[0585] Processing flow and specific examples

[0586] Entering User Data

[0587] Users access a form on their smartphone or computer to enter data about their daily life, work situation, and personal goals, such as "I wake up at 7am," "I go to bed at 11pm," "I work five days a week," and "I want to lose 5kg in three months."

[0588] Receiving and analyzing data

[0589] The device sends the input data over the internet to a server, which then analyzes it using artificial intelligence algorithms to identify the optimal plan of action based on the user's lifestyle and goals.

[0590] Generate customized habits and action plans

[0591] Based on the analysis results, the server generates a specific action plan tailored to the user's lifestyle and goals. For example, it might suggest "walking for 30 minutes every day at 7:30 a.m." or "training at the gym three times a week." This plan is then sent back to the device and displayed to the user.

[0592] Entering and monitoring progress data

[0593] As the user performs the planned activity, they enter their progress into the device, which then sends it to the server, which monitors the progress data. For example, if the user enters "I completed today's walk," the server records the data and evaluates the activity's achievement.

[0594] Generating and Providing Feedback

[0595] Based on the progress data, the server generates necessary improvements and additional advice. For example, it generates feedback such as "continue walking for the next week" or "improve the quality of your diet." The generated feedback is sent to the device and displayed to the user.

[0596] Examples of prompt statements

[0597] The following is an example of a prompt sentence to be input to the generative AI model used in this invention:

[0598] "Generate an action plan to lose 5 kg in 3 months. The user's lifestyle is 'Wake up at 7 am, go to bed at 11 pm, work 5 days a week.'"

[0599] "The user's goal is to live a healthy life. Please suggest a customized activity plan based on their daily schedule."

[0600] In this way, the present invention can provide a specific action plan tailored to the individual needs of the user and support them in achieving their goals.

[0601] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0602] Step 1: Entering User Data

[0603] 1. The device presents the user with a form to fill out, displaying questions about the user's daily life, work situation, and personal goals.

[0604] Input: The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work 5 days a week," and "lose 5kg in 3 months."

[0605] Specific operation: The user enters each item on the device screen and presses the send button.

[0606] Output: Correctly entered data is saved on the device and ready to be sent to the server.

[0607] Step 2: Receiving and analyzing data

[0608] 1. The device sends user data to a server via the Internet.

[0609] Input: Data entered by the terminal and sent to the server.

[0610] Specific operation: The device transmits user data over a secure channel via an Internet connection.

[0611] Output: The server receives the user data.

[0612] 2. The server analyzes the data using artificial intelligence algorithms.

[0613] Input: Received user data.

[0614] How it works: The server analyzes the data using a generative AI model built in Python and generates analytical results based on the user's lifestyle and goals.

[0615] Output: Information about the appropriate habits and action plans as a result of the analysis.

[0616] Step 3: Generate a customized habit and action plan

[0617] 1. Based on the analysis results, the server generates habits and action plans tailored to the user's lifestyle and goals.

[0618] Input: Analysis results.

[0619] Specific Action: The generated action plan is specified through a generative AI model.

[0620] Output: A customized action plan.

[0621] 2. The server sends the generated plan to the terminal.

[0622] Input: The generated action plan.

[0623] Specific operation: The server sends the action plan to the terminal via the Internet.

[0624] Output: The action plan arrives on the terminal.

[0625] 3. The device displays the action plan to the user.

[0626] Input: Action plan sent by the server.

[0627] Specific action: The device visually displays the action plan on the screen.

[0628] Output: User can see the action plan.

[0629] Step 4: Enter and monitor progress data

[0630] 1. The user enters the progress of the activity into the terminal.

[0631] Input: User activity progress data (e.g., "Completed today's walk").

[0632] What it does: Users report their progress within the app and save their data.

[0633] Output: Progress data saved on the device.

[0634] 2. The device sends the progress data to the server.

[0635] Input: Progress data stored on the device.

[0636] Specific operation: The device sends progress data to the server via the Internet.

[0637] Output: The server receives the progress data.

[0638] 3. The server monitors the progress data and evaluates the progress.

[0639] Input: Received progress data.

[0640] Specific operation: Analyzes progress data on the server and evaluates achievement status.

[0641] Output: Assessment data on progress.

[0642] Step 5: Generate and provide feedback

[0643] 1. The server generates advice on necessary improvements and additions based on progress data.

[0644] Input: Assessment data on progress.

[0645] Specific behavior: A generative AI model analyzes progress data and generates appropriate feedback.

[0646] Output: The generated feedback.

[0647] 2. The server sends the generated feedback to the device.

[0648] Input: Generated feedback.

[0649] Specific operation: The server sends feedback to the device via the Internet.

[0650] Output: Feedback data received on the device.

[0651] 3. The device displays feedback to the user.

[0652] Input: Feedback sent by the server.

[0653] Specific behavior: The device will visually display feedback on the screen.

[0654] Output: The user checks the feedback and uses it to guide their next action.

[0655] In this way, the system provides specific action plans and feedback adapted to the user's individual needs, supporting them in achieving their goals.

[0656] (Application example 1)

[0657] 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."

[0658] Conventional shopping systems struggle to effectively reflect users' individual preferences and purchasing patterns, making it difficult to provide personalized product recommendations and promotions. Furthermore, users have to spend time searching for products in physical stores, which makes it difficult to have an efficient shopping experience. Furthermore, efforts to improve user satisfaction are lacking because user progress data and feedback are not utilized.

[0659] 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.

[0660] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's purchasing history, preferences, and purchasing patterns and suggesting customized products and promotions, and means for providing navigation within a physical store using the user's location information. This enables a personalized shopping experience for each user, improves product search efficiency, and increases user satisfaction.

[0661] "User input data" refers to information about lifestyle habits, goals, preferences, purchasing history, etc. that users provide to the system.

[0662] An "artificial intelligence algorithm" is a computational method or model that analyzes input data and creates optimal action plans and product recommendations for users.

[0663] "Customized habits and action plans" are suggestions for specific actions and habits that are individually tailored based on the user's input data.

[0664] "User Progress Data" means information about actions and achievements performed by a User.

[0665] "Monitoring" means continuously tracking a user's progress data and evaluating their achievements.

[0666] "Feedback" refers to providing users with behavioral or habit modifications or additional advice based on their progress data.

[0667] "Purchase history" is information about products purchased by a user in the past.

[0668] "Preferences" refers to information that indicates a user's tendency to like certain brands or products.

[0669] "Purchase patterns" are data that indicate the tendency of users to purchase what products and when.

[0670] "Customized products and promotions" refers to individually optimized product recommendations and special offers provided based on an analysis of a user's purchasing history, preferences, and purchasing patterns.

[0671] "Location information" is data that indicates a user's current physical location.

[0672] "In-store navigation" refers to using a user's location to provide directions to specific products or sections within a store.

[0673] The invention is embodied in the form of a smartphone application for brick-and-mortar stores that analyzes user input data and provides customized product recommendations and promotions.

[0674] First, the server receives user input data: the user uses a smartphone to input data about their shopping history, preferences, and purchasing patterns, and the device that receives this data then sends it to the server.

[0675] The server then uses an artificial intelligence algorithm to analyze the received data. This algorithm is built using machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, product recommendations and promotional information tailored to the user are generated. For example, based on data such as "You recently purchased running shoes" or "Your favorite brand is Nike," related products and sale information will be recommended.

[0676] The generated product recommendations and promotion information are displayed to the user via the device, using a smartphone app, allowing the user to view the recommended products and promotions.

[0677] Furthermore, to help users efficiently find products in physical stores, the server provides navigation using the user's location information. Location information is acquired using the smartphone's GPS sensor and beacons installed in the store. This allows users to be guided to the desired shelf or section in the store.

[0678] The user's progress data (such as product purchase information and movement history within the store) is received and monitored by the server. Based on this, the server generates feedback and provides it to the user via the device. For example, "Next time you visit, there will be a sale on new Nike apparel."

[0679] As an example of implementation, if a user enters "My favorite brand is Nike," "I recently bought running shoes," and "I want to know about sales," the system will act as follows:

[0680] 1. Receiving user input data:

[0681] The user enters information into the app, such as "My favorite brand is Nike," and the device sends this to the server.

[0682] 2. Data Receipt and Analysis:

[0683] The server then analyzes this data using AI algorithms to identify the best product recommendations and promotions for the user.

[0684] 3. Generate customized product recommendations:

[0685] Based on the analysis results, related products such as Nike sportswear are listed.

[0686] 4. Product List Display:

[0687] Display a list of recommended products to the user.

[0688] 5. Obtaining user location information:

[0689] The user's location within the store is obtained using GPS sensors and beacons.

[0690] 6. Route guidance:

[0691] Navigate users to Nike's sportswear section in a physical store.

[0692] Example prompt sentence:

[0693] "Users input their preferred brands and product purchase history. We then analyze the user data to provide relevant product recommendations."

[0694] Generative AI models can be used to create a personalized shopping experience for each user, improving product discovery efficiency while also increasing user satisfaction.

[0695] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0696] Step 1:

[0697] The device receives user input data. The user uses a smartphone app to input information such as lifestyle habits, shopping history, preferences, and purchasing patterns. Examples of input data include "My favorite brand is Nike" and "I recently purchased a pair of running shoes." The device receives this data and sends it to the server.

[0698] Step 2:

[0699] The server receives user data sent from the device. The input data includes the user's personal preferences and purchasing history. The received data is temporarily stored to be fed directly into the AI ​​algorithm.

[0700] Step 3:

[0701] The server analyzes the received data using artificial intelligence algorithms. Here, models built using machine learning libraries such as TensorFlow and PyTorch are used. Specifically, user data is input into the AI ​​model, which then makes predictions. For example, if a user inputs "Nike" and "running shoes," related product recommendations are made. The output generated by the AI ​​model is a customized product list.

[0702] Step 4:

[0703] Based on the analysis results of the AI ​​algorithm, the server generates a product list and promotion information customized for the user. The generated information includes optimal products and sale information based on the user's preferences and purchasing patterns. For example, recommendations such as "Nike sportswear" and "sale on running shoes" are made. This information is sent to the device.

[0704] Step 5:

[0705] The device receives customized product lists and promotional information from the server and displays them to the user. Based on the information displayed, the user can access and check specific product and sale information. For example, the app screen might display "New Nike sportswear" or "Special sale on running shoes."

[0706] Step 6:

[0707] The server uses the user's location information to provide navigation within the physical store. Location information is obtained from the smartphone's GPS sensor and beacons installed in the store. The server identifies the user's current location and calculates the optimal route to the desired shelf or section. Navigation information is sent to the device.

[0708] Step 7:

[0709] The device provides the user with navigation information sent from the server. Users can check route guidance on the app screen and navigate efficiently within the physical store. For example, a detailed route to reach the "Nike sportswear section" is displayed.

[0710] Step 8:

[0711] The device records the user's actions (product purchase information, store movement history, etc.) and sends the progress data to the server. The progress data includes information on whether the user purchased the suggested products and how they moved around the store. The server receives this data and monitors the progress.

[0712] Step 9:

[0713] The server analyzes the progress data and generates feedback, such as "There will be a sale on new Nike apparel on your next visit." The generated feedback is sent to the device.

[0714] Step 10:

[0715] The device provides the user with the feedback it receives from the server. The user can check the feedback through the app and use it to make their next purchase. For example, a notification will appear on the app screen saying, "Get a great deal on new Nike apparel on your next visit."

[0716] 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.

[0717] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input data and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[0718] Explaining program processing in natural language

[0719] 1. Enter user data

[0720] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[0721] The user enters this data and sends it to the terminal.

[0722] The terminal transmits the input data to the server.

[0723] 2. Entering Emotion Data

[0724] The terminal or compatible device collects the user's emotional data in real time and transmits it to the server.

[0725] 3. Data Receipt and Analysis

[0726] The server receives user data and emotion data from the terminal.

[0727] The server analyzes the received data using artificial intelligence algorithms and emotion engines.

[0728] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[0729] The emotion engine also analyzes the user's emotional data and makes adjustments based on the user's emotional state.

[0730] 4. Generate customized habits and action plans

[0731] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[0732] For example, the server might suggest things like "walking 30 minutes at 7:30 every morning," "going to the gym three times a week," or "adding a short meditation session on days when stress levels are high."

[0733] The server sends the generated plan to the terminal, which displays it to the user.

[0734] 5. Entering and monitoring progress data

[0735] Progress data regarding the activities performed by the user and changes in emotions are entered into the device.

[0736] The terminal transmits the progress data and emotion data to the server.

[0737] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[0738] 6. Generating and Providing Feedback

[0739] The server generates corrective and additional advice for the user based on the progress and emotional data.

[0740] For example, the server provides feedback such as "keep walking," "record your food intake for the next week," or "do a short meditation when you feel stressed."

[0741] The server sends the feedback to the device, which displays it to the user.

[0742] Specific examples

[0743] Example of a user setting a goal to lose 5kg in 3 months and also prioritizing stress management:

[0744] 1. Enter user data

[0745] The user enters "Wake up at 7am every day, go to bed at 11pm," "Work five days a week," and "Lose 5kg in three months," and the device sends this to the server.

[0746] 2. Entering Emotion Data

[0747] The user inputs their daily emotional state (such as stress level and mood), and the device sends this to the server.

[0748] 3. Data Receipt and Analysis

[0749] The server receives this data and analyzes it using AI algorithms and an emotion engine. For example, based on a user's activity patterns, work hours, and emotional state, it may determine that morning exercise is beneficial, or that relaxation activities are needed on days when stress levels are high.

[0750] 4. Generate customized habits and action plans

[0751] The server generates a specific plan such as "30 minutes of walking every morning at 7:30," "go to the gym three times a week," or "10 minutes of meditation on stressful days," and the device displays this to the user.

[0752] 5. Entering and monitoring progress data

[0753] The user enters progress data such as "I have completed today's walk" or "Today's emotional state is stress level 3," and the device sends this to the server.

[0754] The server monitors the progress and emotional data to assess achievement and emotional state.

[0755] 6. Generating and Providing Feedback

[0756] The server generates feedback such as "continue walking for the next week," "improve the quality of your diet," or "engage in a short meditation session the next time you feel stressed," and the device displays this to the user.

[0757] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[0758] The processing flow will be explained below.

[0759] Step 1:

[0760] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[0761] Step 2:

[0762] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[0763] Step 3:

[0764] The terminal transmits the entered user data to the server.

[0765] Step 4:

[0766] The terminal or a compatible device collects the user's emotional data in real time, for example, by using the user's voice tone, facial expression recognition, self-evaluation, etc.

[0767] Step 5:

[0768] The device transmits the collected emotion data to a server.

[0769] Step 6:

[0770] A server receives the user data and the emotion data.

[0771] Step 7:

[0772] The server uses artificial intelligence algorithms and emotion engines to analyze the received data, such as analyzing the user's daily life patterns, working hours, and emotional state.

[0773] Step 8:

[0774] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[0775] Step 9:

[0776] The server transmits the generated habits and action plans to the terminal.

[0777] Step 10:

[0778] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[0779] Step 11:

[0780] Users input data about their daily progress and emotional state into the device, such as "Completed today's walk" or "Today's stress level is 3."

[0781] Step 12:

[0782] The terminal transmits the progress data and emotion data to the server.

[0783] Step 13:

[0784] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[0785] Step 14:

[0786] Based on the progress and emotional data, the server generates corrective or additional advice for the user, such as "keep walking," "improve the quality of your diet," or "take a short meditation session when you feel stressed."

[0787] Step 15:

[0788] The server generates feedback and sends it to the device.

[0789] Step 16:

[0790] The device displays feedback to the user, allowing them to see their next steps.

[0791] Example 2

[0792] 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."

[0793] Conventional systems have difficulty generating specific habits and action plans tailored to a user's individual needs and emotional state, and providing appropriate feedback based on the user's progress and emotional changes. This leads to a lack of comprehensive support for users to achieve their goals and manage their emotional state.

[0794] 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.

[0795] In this invention, the server includes means for using an artificial intelligence algorithm and an emotion engine to receive and analyze user input data and emotion data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring the user's progress data and emotion data, and means for generating and providing feedback to the user based on the progress data and emotion data, thereby providing a specific action plan tailored to the user's individual needs and emotional state and enabling comprehensive support for goal achievement and emotion management.

[0796] "User input data" is data that a user provides to the system about their lifestyle, work situation, personal goals, etc.

[0797] An "artificial intelligence algorithm" is a program that has computational methods for analyzing data, recognizing patterns, and making predictions.

[0798] An "emotion engine" is an algorithm or model that analyzes a user's emotional data and understands their emotional state.

[0799] "Customized habits and action plans" are specific action plans that are individually generated based on the user's input data and analysis results.

[0800] "Means for displaying the generated habits and behavioral plans to the user" refers to techniques or methods for visually presenting the generated behavioral plans to the user through a terminal or device.

[0801] "User progress data" refers to data that a user records and provides to the system regarding their daily activities and progress toward achieving their goals.

[0802] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes information such as heart rate, stress level, and mood.

[0803] "Means for monitoring" refers to the functions and methods by which the system continuously monitors and records the user's progress data and emotional data.

[0804] "Means for generating and providing feedback to the user" refers to the method by which the system makes useful advice or adjustments based on progress data and emotional data and communicates them to the user.

[0805] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[0806] The program of this system uses the following specific hardware and software to carry out a series of processes such as data entry, data analysis, generation of action plans, progress monitoring, and provision of feedback.

[0807] (Hardware and software used)

[0808] Hardware: Servers, devices (PCs, smartphones, tablets, etc.), biosensors (smartwatches, etc.)

[0809] Software: Artificial intelligence algorithms (TensorFlow, PyTorch, etc.), emotion engines, database management systems (MySQL, PostgreSQL, etc.)

[0810] As an example of specific behavior, the following prompt sentence is input into the generative AI model:

[0811] "I'm aiming to lose 5 kg in 3 months, but my work schedule is busy and I'm under a lot of stress. Could you please give me some advice on my daily activity schedule and stress management?"

[0812] (Example)

[0813] 1. Enter user data

[0814] The device presents the user with a form, which could be a web form or an app prompt, to enter data about their daily life, work situation, or personal goals.

[0815] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[0816] The security of the data is ensured by the device encrypting the data entered and sending it to the server.

[0817] 2. Entering Emotion Data

[0818] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time to understand the user's emotional state.

[0819] Users may also manually enter their mood or stress level, for example, entering "3 (high)" in response to the question, "What is your stress level today?"

[0820] The device transmits the collected and input emotion data to the server as needed.

[0821] 3. Data Receipt and Analysis

[0822] The server receives the user data and emotion data transmitted from the terminal.

[0823] The server first stores the received data in a database, then analyzes it using artificial intelligence algorithms and an emotion engine to generate a customized plan based on the user's activity patterns and goals.

[0824] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[0825] 4. Generate customized habits and action plans

[0826] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[0827] The server formats the generated action plan and sends it to the device in a format such as JSON.

[0828] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[0829] 5. Entering and monitoring progress data

[0830] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[0831] The device periodically sends this progress data to a server, and may also have an automatic synchronization function.

[0832] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[0833] 6. Generating and Providing Feedback

[0834] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[0835] The server generates feedback, formats it, and sends it to the device, where notifications can be used to instantly notify the user.

[0836] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[0837] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[0838] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0839] Step 1:

[0840] Entering User Data

[0841] The device will prompt the user to enter information about their daily life, work situation, and personal goals, either through a web form or an app input screen.

[0842] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[0843] The device sends the user's input data in JSON format to the server, which encrypts the data before sending it to ensure its security.

[0844] Input: Data about lifestyle habits and goals that users enter into forms

[0845] Output: User data sent to the server in JSON format.

[0846] Step 2:

[0847] Entering emotion data

[0848] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time, which can then be used to understand the user's emotional state.

[0849] Users may also manually enter their mood or stress level within the app, for example by entering "3 (high)" in response to the question "What is your stress level today?"

[0850] The device periodically sends emotion data in JSON format to the server.

[0851] Input: User's heart rate, sweat rate, self-input stress level, and other emotional data

[0852] Output: Emotion data in JSON format sent to the server

[0853] Step 3:

[0854] Receiving and analyzing data

[0855] The server receives the user data and emotion data sent from the device, and the received data is first stored in a database.

[0856] The server analyzes the data using artificial intelligence algorithms and emotion engines (using TensorFlow and PyTorch), and generates a customized plan based on the user's activity patterns and goals.

[0857] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[0858] Input: User data and emotion data in JSON format sent to the server

[0859] Output: Customized habits and action plans

[0860] Step 4:

[0861] Generate customized habits and action plans

[0862] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[0863] The server sends the generated action plan in JSON format to the terminal.

[0864] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[0865] Input: Output data of the AI ​​algorithm based on the analysis results

[0866] Output: A customized action plan in JSON format

[0867] Step 5:

[0868] Entering and monitoring progress data

[0869] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[0870] The device periodically sends this progress data to the server in JSON format, and may also have an automatic synchronization function.

[0871] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[0872] Input: Progress and emotion data entered by the user

[0873] Output: Progress and emotion data sent to the server in JSON format.

[0874] Step 6:

[0875] Generating and Providing Feedback

[0876] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[0877] The server generates feedback and sends it to the device in JSON format, allowing the user to be notified immediately using notifications.

[0878] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[0879] Input: User progress and emotion data

[0880] Output: Feedback in JSON format

[0881] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[0882] (Application example 2)

[0883] 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."

[0884] In modern brick-and-mortar stores, it is difficult to quickly and efficiently make customized product recommendations based on individual customer preferences and emotional states. In particular, there is a lack of technology that can analyze a customer's ongoing shopping experience in real time and make optimal product recommendations. This can result in lower customer satisfaction and a loss of purchasing motivation.

[0885] 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 receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's profile and emotional data in-store and generating product suggestions tailored to individual preferences, and means for displaying the suggested products to the user in-store. This makes it possible to customize the shopping experience in physical stores in real time and improve customer satisfaction and purchasing motivation.

[0886] "User Input Data" means information provided manually or automatically by a User regarding their profile, preferences, lifestyle, budget, etc.

[0887] "Artificial intelligence algorithms" refers to the machine learning models and other AI techniques used for analysis and interpretation of received data.

[0888] "Means for generating customized habits and action plans based on analysis results" refers to the process of setting and proposing optimal action plans and habits to users based on the analysis results.

[0889] "Means for displaying the generated habits and behavioral plan to the user" refers to a mechanism for visually presenting the proposed plan to the user using the user's device, an in-store display, etc.

[0890] "User progress data" refers to information about the actions taken by a user and the results achieved, and is data used to monitor progress.

[0891] "Means for generating and providing feedback to users based on progress data" refers to the process of generating improvements and additional advice based on the user's progress and notifying the user.

[0892] "Means for analyzing user profile and emotional data in-store to generate product suggestions tailored to individual preferences" refers to the process of analyzing data provided by users in-store in real time and suggesting products that suit their individual preferences.

[0893] "Means for displaying suggested products to users in-store" refers to a system that uses in-store displays and users' devices to visually provide product suggestions based on analysis.

[0894] In order to implement this invention, it is necessary to build a system that collects user input data and emotion data, analyzes the data, and generates customized habits and action plans. Specific embodiments will be described below.

[0895] System configuration

[0896] The system of the present invention comprises the following main components:

[0897] 1. User device: Smartphone or tablet, etc. Collects user input data and emotion data and sends them to the server.

[0898] 2. Server: Analyzes the received data, generates customized habits and action plans, and provides feedback.

[0899] 3. Display device: This can be a display in a store or a user's device. It displays the generated plans and product suggestions to the user.

[0900] Data entry and analysis

[0901] The device provides users with an input form about their daily life, work situation, and personal goals. This allows users to enter data such as "I like the outdoors," "My style is casual," and "My budget is between ¥5,000 and ¥15,000." It can also collect real-time emotional data (e.g., "Stress level 4," "I'm happy")

[0902] Generate customized proposals

[0903] The server uses an artificial intelligence algorithm to analyze the user's profile data and emotional data received from the device. Based on the results, it generates optimal product suggestions for the user, such as "relaxing outdoor wear" or "casual jeans." The suggestions are then displayed on displays in the store and on the user's device.

[0904] Progress data monitoring and feedback

[0905] When a user enters the results of purchasing or trying on a suggested product into the terminal, the server receives and monitors this as progress data. For example, data such as "Purchased items: T-shirt, jeans" and "Feedback: Satisfied" can be used. Based on this, the server generates additional feedback and provides advice to the user, such as "Try this color T-shirt next time."

[0906] Hardware and software used

[0907] EmotionAnalyzer: Software for analyzing emotional data.

[0908] Recommender: Software that makes product suggestions based on user profiles.

[0909] RESTful API: A protocol for data communication between a terminal and a server.

[0910] Specific examples

[0911] If User A likes "outdoor wear" and "casual style" and enters "stress level 4" as emotion data, the following process will occur:

[0912] The server analyzes the data received and suggests "casual, outdoor wear" that will help users relax.

[0913] Displays within the store include "casual outdoor jackets" and "relaxing items."

[0914] The user purchases or tries on the suggested product and then enters and submits their feedback on the device.

[0915] The server uses this progress data to improve its next suggestion and provides feedback such as, "Next time, try matching pants with this jacket."

[0916] Prompt Sentence Examples

[0917] Here are some example prompts for a generative AI model:

[0918] Suggest the best products to the user based on their profile (style: "casual", size: "M", budget: "¥5000 - ¥15000", lifestyle: "outdoor") and emotional data (emotion: "happy", intensity: 7).

[0919] In this way, the present invention can provide a specific action plan tailored to the user's individual needs and emotional state, enhancing the in-store shopping experience.

[0920] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0921] Step 1:

[0922] The user terminal collects user input data. It provides a form for the user to enter information about their daily life, preferences, budget, and lifestyle, and the user manually enters this data. The user terminal receives this data and sends it to the server. The input at this time is, for example, "Style: Casual," "Size: M," "Budget: ¥5,000 - ¥15,000," and "Lifestyle: Outdoor." The output is that this user data is sent to the server.

[0923] Step 2:

[0924] The user device also collects emotional data. It provides a means for users to input their emotional state in real time while shopping, collecting emotional data such as "Stress level: 4" or "Mood: happy." The user device receives this emotional data and sends it to the server. The input is data about the user's emotional state, and the output is the transmission of this data to the server.

[0925] Step 3:

[0926] The server analyzes the received user profile data and emotional data. The server uses EmotionAnalyzer software to analyze the emotional state and Recommender software to generate product suggestions based on the user profile. The input data is the user profile and emotional data, and specific product suggestions such as "relaxed outdoor jacket" and "casual jeans" are generated as a result of data analysis. The output is the analysis results and the suggested products.

[0927] Step 4:

[0928] The server sends the generated product suggestions to the user's terminal or a display device in the store. The user's terminal or display visually presents the suggested products to the user. For example, the display may show "Recommended product: casual outdoor jacket." The input is the suggested product data from the server, and the output is the display of this product information to the user.

[0929] Step 5:

[0930] The user tries on the suggested products and enters the results of their purchase into the terminal. For example, progress data such as "Purchased item: T-shirt" and "Feedback: Satisfied" is entered. The user terminal receives this data and sends it to the server. The input data is the user's progress information, and the output is that this is sent to the server.

[0931] Step 6:

[0932] The server receives the progress data and monitors the user's behavior. Based on the progress data, the server generates the next feedback and provides it to the user. For example, personalized advice such as "Try this color T-shirt next time" is generated. The input is the progress data, and the feedback is generated as a result of data analysis. The output is the feedback provided to the user.

[0933] Step 7:

[0934] The server tracks the feedback provided to the user and continuously evaluates the user's satisfaction and behavioral improvements, allowing it to continue optimizing the user's shopping experience. The input is the user's response to the feedback and additional progress data, and the output is the next suggestion or improvement of the feedback.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] [Third embodiment]

[0939] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0940] 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.

[0941] 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).

[0942] 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.

[0943] 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.

[0944] 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).

[0945] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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."

[0951] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates a customized habit and action plan for the user, displays it to the user, and monitors progress and provides feedback.

[0952] Explaining program processing in natural language

[0953] 1. Enter user data

[0954] The device presents the user with a form prompting them to enter data about their daily life, work situation, and personal goals.

[0955] The user enters this data and sends it to the terminal.

[0956] The terminal transmits the input data to the server.

[0957] 2. Data Receipt and Analysis

[0958] The server receives the user data from the terminal.

[0959] The server analyzes the received data using artificial intelligence algorithms.

[0960] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[0961] 3. Generate customized habits and action plans

[0962] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle and goals.

[0963] For example, the server might suggest things like "walking for 30 minutes every morning at 7:30" or "training at the gym three times a week."

[0964] The server sends the generated plan to the terminal, which displays it to the user.

[0965] 4. Entering and monitoring progress data

[0966] The user inputs progress data into the device regarding the activities performed and the degree of achievement.

[0967] The device sends progress data to the server.

[0968] The server receives and monitors the progress data.

[0969] 5. Generating and Providing Feedback

[0970] The server generates corrections and additional advice for the user based on their progress.

[0971] For example, the server provides feedback such as "keep walking" or "record your meals for the next week."

[0972] The server sends the feedback to the device, which displays it to the user.

[0973] Specific examples

[0974] For example, if a user sets a goal to lose 5 kg in 3 months:

[0975] 1. Enter user data

[0976] When a user inputs "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the device sends this to the server.

[0977] 2. Data Receipt and Analysis

[0978] The server receives this data and analyzes it using AI algorithms, which may determine, for example, that morning exercise is more effective based on a user's activity patterns and work hours.

[0979] 3. Generate customized habits and action plans

[0980] The server generates a specific plan such as "walk for 30 minutes every morning at 7:30," "train at the gym three times a week," and "record your food intake after each meal," and the device displays this to the user.

[0981] 4. Entering and monitoring progress data

[0982] When the user enters progress data such as "I have completed today's walk" or "I have recorded today's meals," the device sends this to the server.

[0983] The server monitors the progress data and evaluates the percentage of completion and any necessary corrections.

[0984] 5. Generating and Providing Feedback

[0985] Based on the progress data, the server generates feedback such as "continue walking for the next week" or "improve the quality of your diet," and the device displays this to the user.

[0986] In this way, the present invention provides a specific action plan tailored to the user's individual needs and supports them in achieving their goals.

[0987] The processing flow will be explained below.

[0988] Step 1:

[0989] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[0990] Step 2:

[0991] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[0992] Step 3:

[0993] The terminal transmits the entered user data to the server.

[0994] Step 4:

[0995] The server receives the user data from the device for analysis.

[0996] Step 5:

[0997] The server uses artificial intelligence algorithms to analyze the received data, including the user's daily life patterns, work schedule, and actions required to achieve goals.

[0998] Step 6:

[0999] Based on the analysis results, the server generates optimal habits and action plans for the user, such as suggesting a 30-minute walk every morning at 7:30 and going to the gym three times a week.

[1000] Step 7:

[1001] The server transmits the generated customized habits and action plan to the terminal.

[1002] Step 8:

[1003] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[1004] Step 9:

[1005] The user inputs daily progress data into the device, such as "I completed today's walk" or "I recorded today's meals."

[1006] Step 10:

[1007] The device sends progress data to the server.

[1008] Step 11:

[1009] The server receives the progress data and monitors the user's progress. Progress evaluation includes the degree of goal achievement and any necessary corrections.

[1010] Step 12:

[1011] The server uses the progress data to generate feedback and additional advice for the user, such as suggestions like "continue walking for the next week" or "improve the quality of your diet."

[1012] Step 13:

[1013] The server generates feedback and sends it to the terminal, which displays it to the user.

[1014] Example 1

[1015] 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."

[1016] Conventional methods have the problem of making customized action plans based on a user's lifestyle and personal goals, monitoring progress, and providing appropriate feedback. Because users have diverse lifestyles and general advice cannot create effective action plans, an individually adapted support system is needed.

[1017] 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.

[1018] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring the user's progress data, means for generating and providing feedback to the user based on the progress data, means for inputting the user's input to the terminal through an interface, means for the terminal to transmit the input data to the server via the Internet, means for the artificial intelligence algorithm to identify a customized action plan based on the user's lifestyle and goals, means for the terminal to display the specific action plan to the user, means for inputting the user's activity progress to the terminal and transmitting it to the server, and means for the server to analyze the progress data and generate necessary improvements and additional advice, thereby providing a specific action plan tailored to the user's individual needs and supporting them in achieving their goals.

[1019] "User" refers to the individual person or entity who uses the System.

[1020] "Input data" is information users provide to the system, including information about their daily lives, work situations, personal goals, etc.

[1021] "Means for receiving" refers to the mechanism by which the system obtains input data from the user.

[1022] "Artificial intelligence algorithm" refers to an intelligent processing method that uses a computer program to analyze data and automatically perform a specific task.

[1023] "Customized Habits and Action Plans" refers to individually adapted instructions for actions and habits generated based on user input data.

[1024] "Means for displaying" refers to a mechanism for visually conveying the generated habits and action plans to the user.

[1025] "Progress Data" means information about the activities you have undertaken and your progress towards achieving them.

[1026] "Means for monitoring" refers to the mechanism by which the system tracks and evaluates user progress data.

[1027] "Means for generating feedback" refers to a mechanism for providing corrective or additional advice to the user based on progress data.

[1028] "Interface" refers to the means or devices by which a user interacts with a system.

[1029] "Terminal" refers to a device such as a computer or smartphone used by a user.

[1030] "Means for transmitting to a server via the Internet" refers to the communication function for transmitting data from a terminal to a server.

[1031] "Lifestyle" refers to a user's daily life patterns and habits.

[1032] A "goal" is a specific outcome or objective that a user wants to achieve.

[1033] "Activity progress" refers to the degree to which a user has performed planned actions or habits.

[1034] "Means for generating necessary improvements or additional advice" refers to a mechanism for generating improvement suggestions or supplemental advice to the user based on progress data.

[1035] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates and displays customized habits and action plans to the user, monitoring their progress and providing feedback.

[1036] System configuration

[1037] 1. Hardware Configuration

[1038] Device: A device for user input and display, such as a smartphone, computer, or tablet.

[1039] Server: A high-performance computer system for receiving, analyzing, and managing data.

[1040] 2. Software Configuration

[1041] Generative AI models: Algorithms that analyze data and make predictions using Python and TensorFlow.

[1042] Interface: Acts as a web browser or mobile application and interacts with the user.

[1043] Processing flow and specific examples

[1044] Entering User Data

[1045] Users access a form on their smartphone or computer to enter data about their daily life, work situation, and personal goals, such as "I wake up at 7am," "I go to bed at 11pm," "I work five days a week," and "I want to lose 5kg in three months."

[1046] Receiving and analyzing data

[1047] The device sends the input data over the internet to a server, which then analyzes it using artificial intelligence algorithms to identify the optimal plan of action based on the user's lifestyle and goals.

[1048] Generate customized habits and action plans

[1049] Based on the analysis results, the server generates a specific action plan tailored to the user's lifestyle and goals. For example, it might suggest "walking for 30 minutes every day at 7:30 a.m." or "training at the gym three times a week." This plan is then sent back to the device and displayed to the user.

[1050] Entering and monitoring progress data

[1051] As the user performs the planned activity, they enter their progress into the device, which then sends it to the server, which monitors the progress data. For example, if the user enters "I completed today's walk," the server records the data and evaluates the activity's achievement.

[1052] Generating and Providing Feedback

[1053] Based on the progress data, the server generates necessary improvements and additional advice. For example, it generates feedback such as "continue walking for the next week" or "improve the quality of your diet." The generated feedback is sent to the device and displayed to the user.

[1054] Examples of prompt statements

[1055] The following is an example of a prompt sentence to be input to the generative AI model used in this invention:

[1056] "Generate an action plan to lose 5 kg in 3 months. The user's lifestyle is 'Wake up at 7 am, go to bed at 11 pm, work 5 days a week.'"

[1057] "The user's goal is to live a healthy life. Please suggest a customized activity plan based on their daily schedule."

[1058] In this way, the present invention can provide a specific action plan tailored to the individual needs of the user and support them in achieving their goals.

[1059] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1060] Step 1: Entering User Data

[1061] 1. The device presents the user with a form to fill out, displaying questions about the user's daily life, work situation, and personal goals.

[1062] Input: The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work 5 days a week," and "lose 5kg in 3 months."

[1063] Specific operation: The user enters each item on the device screen and presses the send button.

[1064] Output: Correctly entered data is saved on the device and ready to be sent to the server.

[1065] Step 2: Receiving and analyzing data

[1066] 1. The device sends user data to a server via the Internet.

[1067] Input: Data entered by the terminal and sent to the server.

[1068] Specific operation: The device transmits user data over a secure channel via an Internet connection.

[1069] Output: The server receives the user data.

[1070] 2. The server analyzes the data using artificial intelligence algorithms.

[1071] Input: Received user data.

[1072] How it works: The server analyzes the data using a generative AI model built in Python and generates analytical results based on the user's lifestyle and goals.

[1073] Output: Information about the appropriate habits and action plans as a result of the analysis.

[1074] Step 3: Generate a customized habit and action plan

[1075] 1. Based on the analysis results, the server generates habits and action plans tailored to the user's lifestyle and goals.

[1076] Input: Analysis results.

[1077] Specific Action: The generated action plan is specified through a generative AI model.

[1078] Output: A customized action plan.

[1079] 2. The server sends the generated plan to the terminal.

[1080] Input: The generated action plan.

[1081] Specific operation: The server sends the action plan to the terminal via the Internet.

[1082] Output: The action plan arrives on the terminal.

[1083] 3. The device displays the action plan to the user.

[1084] Input: Action plan sent by the server.

[1085] Specific action: The device visually displays the action plan on the screen.

[1086] Output: User can see the action plan.

[1087] Step 4: Enter and monitor progress data

[1088] 1. The user enters the progress of the activity into the terminal.

[1089] Input: User activity progress data (e.g., "Completed today's walk").

[1090] What it does: Users report their progress within the app and save their data.

[1091] Output: Progress data saved on the device.

[1092] 2. The device sends the progress data to the server.

[1093] Input: Progress data stored on the device.

[1094] Specific operation: The device sends progress data to the server via the Internet.

[1095] Output: The server receives the progress data.

[1096] 3. The server monitors the progress data and evaluates the progress.

[1097] Input: Received progress data.

[1098] Specific operation: Analyzes progress data on the server and evaluates achievement status.

[1099] Output: Assessment data on progress.

[1100] Step 5: Generate and provide feedback

[1101] 1. The server generates advice on necessary improvements and additions based on progress data.

[1102] Input: Assessment data on progress.

[1103] Specific behavior: A generative AI model analyzes progress data and generates appropriate feedback.

[1104] Output: The generated feedback.

[1105] 2. The server sends the generated feedback to the device.

[1106] Input: Generated feedback.

[1107] Specific operation: The server sends feedback to the device via the Internet.

[1108] Output: Feedback data received on the device.

[1109] 3. The device displays feedback to the user.

[1110] Input: Feedback sent by the server.

[1111] Specific behavior: The device will visually display feedback on the screen.

[1112] Output: The user checks the feedback and uses it to guide their next action.

[1113] In this way, the system provides specific action plans and feedback adapted to the user's individual needs, supporting them in achieving their goals.

[1114] (Application example 1)

[1115] 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."

[1116] Conventional shopping systems struggle to effectively reflect users' individual preferences and purchasing patterns, making it difficult to provide personalized product recommendations and promotions. Furthermore, users have to spend time searching for products in physical stores, which makes it difficult to have an efficient shopping experience. Furthermore, efforts to improve user satisfaction are lacking because user progress data and feedback are not utilized.

[1117] 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.

[1118] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's purchasing history, preferences, and purchasing patterns and suggesting customized products and promotions, and means for providing navigation within a physical store using the user's location information. This enables a personalized shopping experience for each user, improves product search efficiency, and increases user satisfaction.

[1119] "User input data" refers to information about lifestyle habits, goals, preferences, purchasing history, etc. that users provide to the system.

[1120] An "artificial intelligence algorithm" is a computational method or model that analyzes input data and creates optimal action plans and product recommendations for users.

[1121] "Customized habits and action plans" are suggestions for specific actions and habits that are individually tailored based on the user's input data.

[1122] "User Progress Data" means information about actions and achievements performed by a User.

[1123] "Monitoring" means continuously tracking a user's progress data and evaluating their achievements.

[1124] "Feedback" refers to providing users with behavioral or habit modifications or additional advice based on their progress data.

[1125] "Purchase history" is information about products purchased by a user in the past.

[1126] "Preferences" refers to information that indicates a user's tendency to like certain brands or products.

[1127] "Purchase patterns" are data that indicate the tendency of users to purchase what products and when.

[1128] "Customized products and promotions" refers to individually optimized product recommendations and special offers provided based on an analysis of a user's purchasing history, preferences, and purchasing patterns.

[1129] "Location information" is data that indicates a user's current physical location.

[1130] "In-store navigation" refers to using a user's location to provide directions to specific products or sections within a store.

[1131] The invention is embodied in the form of a smartphone application for brick-and-mortar stores that analyzes user input data and provides customized product recommendations and promotions.

[1132] First, the server receives user input data: the user uses a smartphone to input data about their shopping history, preferences, and purchasing patterns, and the device that receives this data then sends it to the server.

[1133] The server then uses an artificial intelligence algorithm to analyze the received data. This algorithm is built using machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, product recommendations and promotional information tailored to the user are generated. For example, based on data such as "You recently purchased running shoes" or "Your favorite brand is Nike," related products and sale information will be recommended.

[1134] The generated product recommendations and promotion information are displayed to the user via the device, using a smartphone app, allowing the user to view the recommended products and promotions.

[1135] Furthermore, to help users efficiently find products in physical stores, the server provides navigation using the user's location information. Location information is acquired using the smartphone's GPS sensor and beacons installed in the store. This allows users to be guided to the desired shelf or section in the store.

[1136] The user's progress data (such as product purchase information and movement history within the store) is received and monitored by the server. Based on this, the server generates feedback and provides it to the user via the device. For example, "Next time you visit, there will be a sale on new Nike apparel."

[1137] As an example of implementation, if a user enters "My favorite brand is Nike," "I recently bought running shoes," and "I want to know about sales," the system will act as follows:

[1138] 1. Receiving user input data:

[1139] The user enters information into the app, such as "My favorite brand is Nike," and the device sends this to the server.

[1140] 2. Data Receipt and Analysis:

[1141] The server then analyzes this data using AI algorithms to identify the best product recommendations and promotions for the user.

[1142] 3. Generate customized product recommendations:

[1143] Based on the analysis results, related products such as Nike sportswear are listed.

[1144] 4. Product List Display:

[1145] Display a list of recommended products to the user.

[1146] 5. Obtaining user location information:

[1147] The user's location within the store is obtained using GPS sensors and beacons.

[1148] 6. Route guidance:

[1149] Navigate users to Nike's sportswear section in a physical store.

[1150] Example prompt sentence:

[1151] "Users input their preferred brands and product purchase history. We then analyze the user data to provide relevant product recommendations."

[1152] Generative AI models can be used to create a personalized shopping experience for each user, improving product discovery efficiency while also increasing user satisfaction.

[1153] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1154] Step 1:

[1155] The device receives user input data. The user uses a smartphone app to input information such as lifestyle habits, shopping history, preferences, and purchasing patterns. Examples of input data include "My favorite brand is Nike" and "I recently purchased a pair of running shoes." The device receives this data and sends it to the server.

[1156] Step 2:

[1157] The server receives user data sent from the device. The input data includes the user's personal preferences and purchasing history. The received data is temporarily stored to be fed directly into the AI ​​algorithm.

[1158] Step 3:

[1159] The server analyzes the received data using artificial intelligence algorithms. Here, models built using machine learning libraries such as TensorFlow and PyTorch are used. Specifically, user data is input into the AI ​​model, which then makes predictions. For example, if a user inputs "Nike" and "running shoes," related product recommendations are made. The output generated by the AI ​​model is a customized product list.

[1160] Step 4:

[1161] Based on the analysis results of the AI ​​algorithm, the server generates a product list and promotion information customized for the user. The generated information includes optimal products and sale information based on the user's preferences and purchasing patterns. For example, recommendations such as "Nike sportswear" and "sale on running shoes" are made. This information is sent to the device.

[1162] Step 5:

[1163] The device receives customized product lists and promotional information from the server and displays them to the user. Based on the information displayed, the user can access and check specific product and sale information. For example, the app screen might display "New Nike sportswear" or "Special sale on running shoes."

[1164] Step 6:

[1165] The server uses the user's location information to provide navigation within the physical store. Location information is obtained from the smartphone's GPS sensor and beacons installed in the store. The server identifies the user's current location and calculates the optimal route to the desired shelf or section. Navigation information is sent to the device.

[1166] Step 7:

[1167] The device provides the user with navigation information sent from the server. Users can check route guidance on the app screen and navigate efficiently within the physical store. For example, a detailed route to reach the "Nike sportswear section" is displayed.

[1168] Step 8:

[1169] The device records the user's actions (product purchase information, store movement history, etc.) and sends the progress data to the server. The progress data includes information on whether the user purchased the suggested products and how they moved around the store. The server receives this data and monitors the progress.

[1170] Step 9:

[1171] The server analyzes the progress data and generates feedback, such as "There will be a sale on new Nike apparel on your next visit." The generated feedback is sent to the device.

[1172] Step 10:

[1173] The device provides the user with the feedback it receives from the server. The user can check the feedback through the app and use it to make their next purchase. For example, a notification will appear on the app screen saying, "Get a great deal on new Nike apparel on your next visit."

[1174] 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.

[1175] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input data and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[1176] Explaining program processing in natural language

[1177] 1. Enter user data

[1178] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[1179] The user enters this data and sends it to the terminal.

[1180] The terminal transmits the input data to the server.

[1181] 2. Entering Emotion Data

[1182] The terminal or compatible device collects the user's emotional data in real time and transmits it to the server.

[1183] 3. Data Receipt and Analysis

[1184] The server receives the user data and emotion data from the terminal.

[1185] The server analyzes the received data using artificial intelligence algorithms and emotion engines.

[1186] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[1187] The emotion engine also analyzes the user's emotional data and makes adjustments based on the user's emotional state.

[1188] 4. Generate customized habits and action plans

[1189] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[1190] For example, the server might suggest things like "walking 30 minutes at 7:30 every morning," "going to the gym three times a week," or "adding a short meditation session on days when stress levels are high."

[1191] The server sends the generated plan to the terminal, which displays it to the user.

[1192] 5. Entering and monitoring progress data

[1193] Progress data regarding the activities performed by the user and changes in emotions are entered into the device.

[1194] The terminal transmits the progress data and emotion data to the server.

[1195] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[1196] 6. Generating and Providing Feedback

[1197] The server generates corrective or additional advice for the user based on the progress and emotional data.

[1198] For example, the server provides feedback such as "keep walking," "record your food intake for the next week," or "do a short meditation when you feel stressed."

[1199] The server sends the feedback to the device, which displays it to the user.

[1200] Specific examples

[1201] Example of a user setting a goal to lose 5kg in 3 months and also prioritizing stress management:

[1202] 1. Enter user data

[1203] The user enters "Wake up at 7am every day, go to bed at 11pm," "Work five days a week," and "Lose 5kg in three months," and the device sends this to the server.

[1204] 2. Entering Emotion Data

[1205] The user inputs their daily emotional state (such as stress level and mood), and the device sends this to the server.

[1206] 3. Data Receipt and Analysis

[1207] The server receives this data and analyzes it using AI algorithms and an emotion engine. For example, based on a user's activity patterns, work hours, and emotional state, it may determine that morning exercise is beneficial, or that relaxation activities are needed on days when stress levels are high.

[1208] 4. Generate customized habits and action plans

[1209] The server generates a specific plan such as "30 minutes of walking every morning at 7:30," "go to the gym three times a week," or "10 minutes of meditation on stressful days," and the device displays this to the user.

[1210] 5. Entering and monitoring progress data

[1211] The user enters progress data such as "I have completed today's walk" or "Today's emotional state is stress level 3," and the device sends this to the server.

[1212] The server monitors the progress and emotional data to assess achievement and emotional state.

[1213] 6. Generating and Providing Feedback

[1214] The server generates feedback such as "continue walking for the next week," "improve the quality of your diet," or "engage in a short meditation session the next time you feel stressed," and the device displays this to the user.

[1215] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[1219] Step 2:

[1220] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[1221] Step 3:

[1222] The terminal transmits the entered user data to the server.

[1223] Step 4:

[1224] The terminal or a compatible device collects the user's emotional data in real time, for example, by using the user's voice tone, facial expression recognition, self-evaluation, etc.

[1225] Step 5:

[1226] The device transmits the collected emotion data to a server.

[1227] Step 6:

[1228] A server receives the user data and the emotion data.

[1229] Step 7:

[1230] The server uses artificial intelligence algorithms and emotion engines to analyze the received data, such as analyzing the user's daily life patterns, working hours, and emotional state.

[1231] Step 8:

[1232] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[1233] Step 9:

[1234] The server transmits the generated habits and action plans to the terminal.

[1235] Step 10:

[1236] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[1237] Step 11:

[1238] Users input data about their daily progress and emotional state into the device, such as "Completed today's walk" or "Today's stress level is 3."

[1239] Step 12:

[1240] The terminal transmits the progress data and emotion data to the server.

[1241] Step 13:

[1242] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[1243] Step 14:

[1244] Based on the progress and emotional data, the server generates corrective or additional advice for the user, such as "keep walking," "improve the quality of your diet," or "take a short meditation session when you feel stressed."

[1245] Step 15:

[1246] The server generates feedback and sends it to the device.

[1247] Step 16:

[1248] The device displays feedback to the user, allowing them to see their next steps.

[1249] Example 2

[1250] 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."

[1251] Conventional systems have difficulty generating specific habits and action plans tailored to a user's individual needs and emotional state, and providing appropriate feedback based on the user's progress and emotional changes. This leads to a lack of comprehensive support for users to achieve their goals and manage their emotional state.

[1252] 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.

[1253] In this invention, the server includes means for using an artificial intelligence algorithm and an emotion engine to receive and analyze user input data and emotion data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring the user's progress data and emotion data, and means for generating and providing feedback to the user based on the progress data and emotion data, thereby providing a specific action plan tailored to the user's individual needs and emotional state and enabling comprehensive support for goal achievement and emotion management.

[1254] "User input data" is data that a user provides to the system about their lifestyle, work situation, personal goals, etc.

[1255] An "artificial intelligence algorithm" is a program that has computational methods for analyzing data, recognizing patterns, and making predictions.

[1256] An "emotion engine" is an algorithm or model that analyzes a user's emotional data and understands their emotional state.

[1257] "Customized habits and action plans" are specific action plans that are individually generated based on the user's input data and analysis results.

[1258] "Means for displaying the generated habits and behavioral plans to the user" refers to techniques or methods for visually presenting the generated behavioral plans to the user through a terminal or device.

[1259] "User progress data" refers to data that a user records and provides to the system regarding their daily activities and progress toward achieving their goals.

[1260] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes information such as heart rate, stress level, and mood.

[1261] "Means for monitoring" refers to the functions and methods by which the system continuously monitors and records the user's progress data and emotional data.

[1262] "Means for generating and providing feedback to the user" refers to the method by which the system makes useful advice or adjustments based on progress data and emotional data and communicates them to the user.

[1263] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[1264] The program of this system uses the following specific hardware and software to carry out a series of processes such as data entry, data analysis, generation of action plans, progress monitoring, and provision of feedback.

[1265] (Hardware and software used)

[1266] Hardware: Servers, devices (PCs, smartphones, tablets, etc.), biosensors (smartwatches, etc.)

[1267] Software: Artificial intelligence algorithms (TensorFlow, PyTorch, etc.), emotion engines, database management systems (MySQL, PostgreSQL, etc.)

[1268] As an example of specific behavior, the following prompt sentence is input into the generative AI model:

[1269] "I'm aiming to lose 5 kg in 3 months, but my work schedule is busy and I'm under a lot of stress. Could you please give me some advice on my daily activity schedule and stress management?"

[1270] (Example)

[1271] 1. Enter user data

[1272] The device presents the user with a form, which could be a web form or an app prompt, to enter data about their daily life, work situation, or personal goals.

[1273] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[1274] The security of the data is ensured by the device encrypting the data entered and sending it to the server.

[1275] 2. Entering Emotion Data

[1276] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time to understand the user's emotional state.

[1277] Users may also manually enter their mood or stress level, for example, entering "3 (high)" in response to the question, "What is your stress level today?"

[1278] The device transmits the collected and input emotion data to the server as needed.

[1279] 3. Data Receipt and Analysis

[1280] The server receives the user data and emotion data transmitted from the terminal.

[1281] The server first stores the received data in a database, then analyzes it using artificial intelligence algorithms and an emotion engine to generate a customized plan based on the user's activity patterns and goals.

[1282] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[1283] 4. Generate customized habits and action plans

[1284] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[1285] The server formats the generated action plan and sends it to the device in a format such as JSON.

[1286] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[1287] 5. Entering and monitoring progress data

[1288] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[1289] The device periodically sends this progress data to a server, and may also have an automatic synchronization function.

[1290] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[1291] 6. Generating and Providing Feedback

[1292] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[1293] The server generates feedback, formats it, and sends it to the device, where notifications can be used to instantly notify the user.

[1294] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[1295] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[1296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1297] Step 1:

[1298] Entering User Data

[1299] The device will prompt the user to enter information about their daily life, work situation, and personal goals, either through a web form or an app input screen.

[1300] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[1301] The device sends the user's input data in JSON format to the server, which encrypts the data before sending it to ensure its security.

[1302] Input: Data about lifestyle habits and goals that users enter into forms

[1303] Output: User data sent to the server in JSON format.

[1304] Step 2:

[1305] Entering emotion data

[1306] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time, which can then be used to understand the user's emotional state.

[1307] Users may also manually enter their mood or stress level within the app, for example by entering "3 (high)" in response to the question "What is your stress level today?"

[1308] The device periodically sends emotion data in JSON format to the server.

[1309] Input: User's heart rate, sweat rate, self-input stress level, and other emotional data

[1310] Output: Emotion data in JSON format sent to the server

[1311] Step 3:

[1312] Receiving and analyzing data

[1313] The server receives the user data and emotion data sent from the device, and the received data is first stored in a database.

[1314] The server analyzes the data using artificial intelligence algorithms and emotion engines (using TensorFlow and PyTorch), and generates a customized plan based on the user's activity patterns and goals.

[1315] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[1316] Input: User data and emotion data in JSON format sent to the server

[1317] Output: Customized habits and action plans

[1318] Step 4:

[1319] Generate customized habits and action plans

[1320] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[1321] The server sends the generated action plan in JSON format to the terminal.

[1322] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[1323] Input: Output data of the AI ​​algorithm based on the analysis results

[1324] Output: A customized action plan in JSON format

[1325] Step 5:

[1326] Entering and monitoring progress data

[1327] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[1328] The device periodically sends this progress data to the server in JSON format, and may also have an automatic synchronization function.

[1329] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[1330] Input: Progress and emotion data entered by the user

[1331] Output: Progress and emotion data sent to the server in JSON format.

[1332] Step 6:

[1333] Generating and Providing Feedback

[1334] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[1335] The server generates feedback and sends it to the device in JSON format, allowing the user to be notified immediately using notifications.

[1336] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[1337] Input: User progress and emotion data

[1338] Output: Feedback in JSON format

[1339] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[1340] (Application example 2)

[1341] 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."

[1342] In modern brick-and-mortar stores, it is difficult to quickly and efficiently make customized product recommendations based on individual customer preferences and emotional states. In particular, there is a lack of technology that can analyze a customer's ongoing shopping experience in real time and make optimal product recommendations. This can result in lower customer satisfaction and a loss of purchasing motivation.

[1343] 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 receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's profile and emotional data in-store and generating product suggestions tailored to individual preferences, and means for displaying the suggested products to the user in-store. This makes it possible to customize the shopping experience in physical stores in real time and improve customer satisfaction and purchasing motivation.

[1344] "User Input Data" means information provided manually or automatically by a User regarding their profile, preferences, lifestyle, budget, etc.

[1345] "Artificial intelligence algorithms" refers to the machine learning models and other AI techniques used for analysis and interpretation of received data.

[1346] "Means for generating customized habits and action plans based on analysis results" refers to the process of setting and proposing optimal action plans and habits to users based on the analysis results.

[1347] "Means for displaying the generated habits and behavioral plan to the user" refers to a mechanism for visually presenting the proposed plan to the user using the user's device, an in-store display, etc.

[1348] "User progress data" refers to information about the actions taken by a user and the results achieved, and is data used to monitor progress.

[1349] "Means for generating and providing feedback to users based on progress data" refers to the process of generating improvements and additional advice based on the user's progress and notifying the user.

[1350] "Means for analyzing user profile and emotional data in-store to generate product suggestions tailored to individual preferences" refers to the process of analyzing data provided by users in-store in real time and suggesting products that suit their individual preferences.

[1351] "Means for displaying suggested products to users in-store" refers to a system that uses in-store displays and users' devices to visually provide product suggestions based on analysis.

[1352] In order to implement this invention, it is necessary to build a system that collects user input data and emotion data, analyzes the data, and generates customized habits and action plans. Specific embodiments will be described below.

[1353] System configuration

[1354] The system of the present invention comprises the following main components:

[1355] 1. User device: Smartphone or tablet, etc. Collects user input data and emotion data and sends them to the server.

[1356] 2. Server: Analyzes the received data, generates customized habits and action plans, and provides feedback.

[1357] 3. Display device: This can be a display in a store or a user's device. It displays the generated plans and product suggestions to the user.

[1358] Data entry and analysis

[1359] The device provides users with an input form about their daily life, work situation, and personal goals. This allows users to enter data such as "I like the outdoors," "My style is casual," and "My budget is between ¥5,000 and ¥15,000." It can also collect real-time emotional data (e.g., "Stress level 4," "I'm happy")

[1360] Generate customized proposals

[1361] The server uses an artificial intelligence algorithm to analyze the user's profile data and emotional data received from the device. Based on the results, it generates optimal product suggestions for the user, such as "relaxing outdoor wear" or "casual jeans." The suggestions are then displayed on displays in the store and on the user's device.

[1362] Progress data monitoring and feedback

[1363] When a user enters the results of purchasing or trying on a suggested product into the terminal, the server receives and monitors this as progress data. For example, data such as "Purchased items: T-shirt, jeans" and "Feedback: Satisfied" can be used. Based on this, the server generates additional feedback and provides advice to the user, such as "Try this color T-shirt next time."

[1364] Hardware and software used

[1365] EmotionAnalyzer: Software for analyzing emotional data.

[1366] Recommender: Software that makes product suggestions based on user profiles.

[1367] RESTful API: A protocol for data communication between a terminal and a server.

[1368] Specific examples

[1369] If User A likes "outdoor wear" and "casual style" and enters "stress level 4" as emotion data, the following process will occur:

[1370] The server analyzes the data received and suggests "casual, outdoor wear" that will help users relax.

[1371] Displays within the store include "casual outdoor jackets" and "relaxing items."

[1372] The user purchases or tries on the suggested product and then enters and submits their feedback on the device.

[1373] The server uses this progress data to improve its next suggestion and provides feedback such as, "Next time, try matching pants with this jacket."

[1374] Prompt Sentence Examples

[1375] Here are some example prompts for a generative AI model:

[1376] Suggest the best products to the user based on their profile (style: "casual", size: "M", budget: "¥5000 - ¥15000", lifestyle: "outdoor") and emotional data (emotion: "happy", intensity: 7).

[1377] In this way, the present invention can provide a specific action plan tailored to the user's individual needs and emotional state, enhancing the in-store shopping experience.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] The user terminal collects user input data. It provides a form for the user to enter information about their daily life, preferences, budget, and lifestyle, and the user manually enters this data. The user terminal receives this data and sends it to the server. The input at this time is, for example, "Style: Casual," "Size: M," "Budget: ¥5,000 - ¥15,000," and "Lifestyle: Outdoor." The output is that this user data is sent to the server.

[1381] Step 2:

[1382] The user device also collects emotional data. It provides a means for users to input their emotional state in real time while shopping, collecting emotional data such as "Stress level: 4" or "Mood: happy." The user device receives this emotional data and sends it to the server. The input is data about the user's emotional state, and the output is the transmission of this data to the server.

[1383] Step 3:

[1384] The server analyzes the received user profile data and emotional data. The server uses EmotionAnalyzer software to analyze the emotional state and Recommender software to generate product suggestions based on the user profile. The input data is the user profile and emotional data, and specific product suggestions such as "relaxed outdoor jacket" and "casual jeans" are generated as a result of data analysis. The output is the analysis results and the suggested products.

[1385] Step 4:

[1386] The server sends the generated product suggestions to the user's terminal or a display device in the store. The user's terminal or display visually presents the suggested products to the user. For example, the display may show "Recommended product: casual outdoor jacket." The input is the suggested product data from the server, and the output is the display of this product information to the user.

[1387] Step 5:

[1388] The user tries on the suggested products and enters the results of their purchase into the terminal. For example, progress data such as "Purchased item: T-shirt" and "Feedback: Satisfied" is entered. The user terminal receives this data and sends it to the server. The input data is the user's progress information, and the output is that this is sent to the server.

[1389] Step 6:

[1390] The server receives the progress data and monitors the user's behavior. Based on the progress data, the server generates the next feedback and provides it to the user. For example, personalized advice such as "Try this color T-shirt next time" is generated. The input is the progress data, and the feedback is generated as a result of data analysis. The output is the feedback provided to the user.

[1391] Step 7:

[1392] The server tracks the feedback provided to the user and continuously evaluates the user's satisfaction and behavioral improvements, allowing it to continue optimizing the user's shopping experience. The input is the user's response to the feedback and additional progress data, and the output is the next suggestion or improvement of the feedback.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] [Fourth embodiment]

[1397] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1398] 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.

[1399] 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).

[1400] 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.

[1401] 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.

[1402] 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).

[1403] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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."

[1410] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates a customized habit and action plan for the user, displays it to the user, and monitors progress and provides feedback.

[1411] Explaining program processing in natural language

[1412] 1. Enter user data

[1413] The device presents the user with a form prompting them to enter data about their daily life, work situation, and personal goals.

[1414] The user enters this data and sends it to the terminal.

[1415] The terminal transmits the input data to the server.

[1416] 2. Data Receipt and Analysis

[1417] The server receives the user data from the terminal.

[1418] The server analyzes the received data using artificial intelligence algorithms.

[1419] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[1420] 3. Generate customized habits and action plans

[1421] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle and goals.

[1422] For example, the server might suggest things like "walking for 30 minutes every morning at 7:30" or "training at the gym three times a week."

[1423] The server sends the generated plan to the terminal, which displays it to the user.

[1424] 4. Entering and monitoring progress data

[1425] The user inputs progress data into the device regarding the activities performed and the degree of achievement.

[1426] The device sends progress data to the server.

[1427] The server receives and monitors the progress data.

[1428] 5. Generating and Providing Feedback

[1429] The server generates corrections and additional advice for the user based on their progress.

[1430] For example, the server provides feedback such as "keep walking" or "record your meals for the next week."

[1431] The server sends the feedback to the device, which displays it to the user.

[1432] Specific examples

[1433] For example, if a user sets a goal to lose 5 kg in 3 months:

[1434] 1. Enter user data

[1435] When a user inputs "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the device sends this to the server.

[1436] 2. Data Receipt and Analysis

[1437] The server receives this data and analyzes it using AI algorithms, which may determine, for example, that morning exercise is more effective based on a user's activity patterns and work hours.

[1438] 3. Generate customized habits and action plans

[1439] The server generates a specific plan such as "walk for 30 minutes every morning at 7:30," "train at the gym three times a week," and "record your food intake after each meal," and the device displays this to the user.

[1440] 4. Entering and monitoring progress data

[1441] When the user enters progress data such as "I have completed today's walk" or "I have recorded today's meals," the device sends this to the server.

[1442] The server monitors the progress data and evaluates the percentage of completion and any necessary corrections.

[1443] 5. Generating and Providing Feedback

[1444] Based on the progress data, the server generates feedback such as "continue walking for the next week" or "improve the quality of your diet," and the device displays this to the user.

[1445] In this way, the present invention provides a specific action plan tailored to the user's individual needs and supports them in achieving their goals.

[1446] The processing flow will be explained below.

[1447] Step 1:

[1448] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[1449] Step 2:

[1450] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[1451] Step 3:

[1452] The terminal transmits the entered user data to the server.

[1453] Step 4:

[1454] The server receives the user data from the device for analysis.

[1455] Step 5:

[1456] The server uses artificial intelligence algorithms to analyze the received data, including the user's daily life patterns, work schedule, and actions required to achieve goals.

[1457] Step 6:

[1458] Based on the analysis results, the server generates optimal habits and action plans for the user, such as suggesting a 30-minute walk every morning at 7:30 and going to the gym three times a week.

[1459] Step 7:

[1460] The server transmits the generated customized habits and action plan to the terminal.

[1461] Step 8:

[1462] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[1463] Step 9:

[1464] The user inputs daily progress data into the device, such as "I completed today's walk" or "I recorded today's meals."

[1465] Step 10:

[1466] The device sends progress data to the server.

[1467] Step 11:

[1468] The server receives the progress data and monitors the user's progress. Progress evaluation includes the degree of goal achievement and any necessary corrections.

[1469] Step 12:

[1470] The server uses the progress data to generate feedback and additional advice for the user, such as suggestions like "continue walking for the next week" or "improve the quality of your diet."

[1471] Step 13:

[1472] The server generates feedback and sends it to the terminal, which displays it to the user.

[1473] Example 1

[1474] 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."

[1475] Conventional methods have the problem of making customized action plans based on a user's lifestyle and personal goals, monitoring progress, and providing appropriate feedback. Because users have diverse lifestyles and general advice cannot create effective action plans, an individually adapted support system is needed.

[1476] 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.

[1477] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring the user's progress data, means for generating and providing feedback to the user based on the progress data, means for inputting the user's input to the terminal through an interface, means for the terminal to transmit the input data to the server via the Internet, means for the artificial intelligence algorithm to identify a customized action plan based on the user's lifestyle and goals, means for the terminal to display the specific action plan to the user, means for inputting the user's activity progress to the terminal and transmitting it to the server, and means for the server to analyze the progress data and generate necessary improvements and additional advice, thereby providing a specific action plan tailored to the user's individual needs and supporting them in achieving their goals.

[1478] "User" refers to the individual person or entity who uses the System.

[1479] "Input data" is information users provide to the system, including information about their daily lives, work situations, personal goals, etc.

[1480] "Means for receiving" refers to the mechanism by which the system obtains input data from the user.

[1481] "Artificial intelligence algorithm" refers to an intelligent processing method that uses a computer program to analyze data and automatically perform a specific task.

[1482] "Customized Habits and Action Plans" refers to individually adapted instructions for actions and habits generated based on user input data.

[1483] "Means for displaying" refers to a mechanism for visually conveying the generated habits and action plans to the user.

[1484] "Progress Data" means information about the activities you have undertaken and your progress towards achieving them.

[1485] "Means for monitoring" refers to the mechanism by which the system tracks and evaluates user progress data.

[1486] "Means for generating feedback" refers to a mechanism for providing corrective or additional advice to the user based on progress data.

[1487] "Interface" refers to the means or devices by which a user interacts with a system.

[1488] "Terminal" refers to a device such as a computer or smartphone used by a user.

[1489] "Means for transmitting to a server via the Internet" refers to the communication function for transmitting data from a terminal to a server.

[1490] "Lifestyle" refers to a user's daily life patterns and habits.

[1491] A "goal" is a specific outcome or objective that a user wants to achieve.

[1492] "Activity progress" refers to the degree to which a user has performed planned actions or habits.

[1493] "Means for generating necessary improvements or additional advice" refers to a mechanism for generating improvement suggestions or supplemental advice to the user based on progress data.

[1494] The present invention is a system that uses artificial intelligence algorithms to receive and analyze user input data, and based on the analysis generates and displays customized habits and action plans to the user, monitoring their progress and providing feedback.

[1495] System configuration

[1496] 1. Hardware Configuration

[1497] Device: A device for user input and display, such as a smartphone, computer, or tablet.

[1498] Server: A high-performance computer system for receiving, analyzing, and managing data.

[1499] 2. Software Configuration

[1500] Generative AI models: Algorithms that analyze data and make predictions using Python and TensorFlow.

[1501] Interface: Acts as a web browser or mobile application and interacts with the user.

[1502] Processing flow and specific examples

[1503] Entering User Data

[1504] Users access a form on their smartphone or computer to enter data about their daily life, work situation, and personal goals, such as "I wake up at 7am," "I go to bed at 11pm," "I work five days a week," and "I want to lose 5kg in three months."

[1505] Receiving and analyzing data

[1506] The device sends the input data over the internet to a server, which then analyzes it using artificial intelligence algorithms to identify the optimal plan of action based on the user's lifestyle and goals.

[1507] Generate customized habits and action plans

[1508] Based on the analysis results, the server generates a specific action plan tailored to the user's lifestyle and goals. For example, it might suggest "walking for 30 minutes every day at 7:30 a.m." or "training at the gym three times a week." This plan is then sent back to the device and displayed to the user.

[1509] Entering and monitoring progress data

[1510] As the user performs the planned activity, they enter their progress into the device, which then sends it to the server, which monitors the progress data. For example, if the user enters "I completed today's walk," the server records the data and evaluates the activity's achievement.

[1511] Generating and Providing Feedback

[1512] Based on the progress data, the server generates necessary improvements and additional advice. For example, it generates feedback such as "continue walking for the next week" or "improve the quality of your diet." The generated feedback is sent to the device and displayed to the user.

[1513] Examples of prompt statements

[1514] The following is an example of a prompt sentence to be input to the generative AI model used in this invention:

[1515] "Generate an action plan to lose 5 kg in 3 months. The user's lifestyle is 'Wake up at 7 am, go to bed at 11 pm, work 5 days a week.'"

[1516] "The user's goal is to live a healthy life. Please suggest a customized activity plan based on their daily schedule."

[1517] In this way, the present invention can provide a specific action plan tailored to the individual needs of the user and support them in achieving their goals.

[1518] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1519] Step 1: Entering User Data

[1520] 1. The device presents the user with a form to fill out, displaying questions about the user's daily life, work situation, and personal goals.

[1521] Input: The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work 5 days a week," and "lose 5kg in 3 months."

[1522] Specific operation: The user enters each item on the device screen and presses the send button.

[1523] Output: Correctly entered data is saved on the device and ready to be sent to the server.

[1524] Step 2: Receiving and analyzing data

[1525] 1. The device sends user data to a server via the Internet.

[1526] Input: Data entered by the terminal and sent to the server.

[1527] Specific operation: The device transmits user data over a secure channel via an Internet connection.

[1528] Output: The server receives the user data.

[1529] 2. The server analyzes the data using artificial intelligence algorithms.

[1530] Input: Received user data.

[1531] How it works: The server analyzes the data using a generative AI model built in Python and generates analytical results based on the user's lifestyle and goals.

[1532] Output: Information about the appropriate habits and action plans as a result of the analysis.

[1533] Step 3: Generate a customized habit and action plan

[1534] 1. Based on the analysis results, the server generates habits and action plans tailored to the user's lifestyle and goals.

[1535] Input: Analysis results.

[1536] Specific Action: The generated action plan is specified through a generative AI model.

[1537] Output: A customized action plan.

[1538] 2. The server sends the generated plan to the terminal.

[1539] Input: The generated action plan.

[1540] Specific operation: The server sends the action plan to the terminal via the Internet.

[1541] Output: The action plan arrives on the terminal.

[1542] 3. The device displays the action plan to the user.

[1543] Input: Action plan sent by the server.

[1544] Specific action: The device visually displays the action plan on the screen.

[1545] Output: User can see the action plan.

[1546] Step 4: Enter and monitor progress data

[1547] 1. The user enters the progress of the activity into the terminal.

[1548] Input: User activity progress data (e.g., "Completed today's walk").

[1549] What it does: Users report their progress within the app and save their data.

[1550] Output: Progress data saved on the device.

[1551] 2. The device sends the progress data to the server.

[1552] Input: Progress data stored on the device.

[1553] Specific operation: The device sends progress data to the server via the Internet.

[1554] Output: The server receives the progress data.

[1555] 3. The server monitors the progress data and evaluates the progress.

[1556] Input: Received progress data.

[1557] Specific operation: Analyzes progress data on the server and evaluates achievement status.

[1558] Output: Assessment data on progress.

[1559] Step 5: Generate and provide feedback

[1560] 1. The server generates advice on necessary improvements and additions based on progress data.

[1561] Input: Assessment data on progress.

[1562] Specific behavior: A generative AI model analyzes progress data and generates appropriate feedback.

[1563] Output: The generated feedback.

[1564] 2. The server sends the generated feedback to the device.

[1565] Input: Generated feedback.

[1566] Specific operation: The server sends feedback to the device via the Internet.

[1567] Output: Feedback data received on the device.

[1568] 3. The device displays feedback to the user.

[1569] Input: Feedback sent by the server.

[1570] Specific behavior: The device will visually display feedback on the screen.

[1571] Output: The user checks the feedback and uses it to guide their next action.

[1572] In this way, the system provides specific action plans and feedback adapted to the user's individual needs, supporting them in achieving their goals.

[1573] (Application example 1)

[1574] 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."

[1575] Conventional shopping systems struggle to effectively reflect users' individual preferences and purchasing patterns, making it difficult to provide personalized product recommendations and promotions. Furthermore, users have to spend time searching for products in physical stores, which makes it difficult to have an efficient shopping experience. Furthermore, efforts to improve user satisfaction are lacking because user progress data and feedback are not utilized.

[1576] 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.

[1577] In this invention, the server includes means for receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating a habit and action plan customized for the user based on the analysis results, means for displaying the generated habit and action plan to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's purchasing history, preferences, and purchasing patterns and suggesting customized products and promotions, and means for providing navigation within a physical store using the user's location information. This enables a personalized shopping experience for each user, improves product search efficiency, and increases user satisfaction.

[1578] "User input data" refers to information about lifestyle habits, goals, preferences, purchasing history, etc. that users provide to the system.

[1579] An "artificial intelligence algorithm" is a computational method or model that analyzes input data and creates optimal action plans and product recommendations for users.

[1580] "Customized habits and action plans" are suggestions for specific actions and habits that are individually tailored based on the user's input data.

[1581] "User Progress Data" means information about actions and achievements performed by a User.

[1582] "Monitoring" means continuously tracking a user's progress data and evaluating their achievements.

[1583] "Feedback" refers to providing users with behavioral or habit modifications or additional advice based on their progress data.

[1584] "Purchase history" is information about products purchased by a user in the past.

[1585] "Preferences" refers to information that indicates a user's tendency to like certain brands or products.

[1586] "Purchase patterns" are data that indicate the tendency of users to purchase what products and when.

[1587] "Customized products and promotions" refers to individually optimized product recommendations and special offers provided based on an analysis of a user's purchasing history, preferences, and purchasing patterns.

[1588] "Location information" is data that indicates a user's current physical location.

[1589] "In-store navigation" refers to using a user's location to provide directions to specific products or sections within a store.

[1590] The invention is embodied in the form of a smartphone application for brick-and-mortar stores that analyzes user input data and provides customized product recommendations and promotions.

[1591] First, the server receives user input data: the user uses a smartphone to input data about their shopping history, preferences, and purchasing patterns, and the device that receives this data then sends it to the server.

[1592] The server then uses an artificial intelligence algorithm to analyze the received data. This algorithm is built using machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, product recommendations and promotional information tailored to the user are generated. For example, based on data such as "You recently purchased running shoes" or "Your favorite brand is Nike," related products and sale information will be recommended.

[1593] The generated product recommendations and promotion information are displayed to the user via the device, using a smartphone app, allowing the user to view the recommended products and promotions.

[1594] Furthermore, to help users efficiently find products in physical stores, the server provides navigation using the user's location information. Location information is acquired using the smartphone's GPS sensor and beacons installed in the store. This allows users to be guided to the desired shelf or section in the store.

[1595] The user's progress data (such as product purchase information and movement history within the store) is received and monitored by the server. Based on this, the server generates feedback and provides it to the user via the device. For example, "Next time you visit, there will be a sale on new Nike apparel."

[1596] As an example of implementation, if a user enters "My favorite brand is Nike," "I recently bought running shoes," and "I want to know about sales," the system will act as follows:

[1597] 1. Receiving user input data:

[1598] The user enters information into the app, such as "My favorite brand is Nike," and the device sends this to the server.

[1599] 2. Data Receipt and Analysis:

[1600] The server then analyzes this data using AI algorithms to identify the best product recommendations and promotions for the user.

[1601] 3. Generate customized product recommendations:

[1602] Based on the analysis results, related products such as Nike sportswear are listed.

[1603] 4. Product List Display:

[1604] Display a list of recommended products to the user.

[1605] 5. Obtaining user location information:

[1606] The user's location within the store is obtained using GPS sensors and beacons.

[1607] 6. Route guidance:

[1608] Navigate users to Nike's sportswear section in a physical store.

[1609] Example prompt sentence:

[1610] "Users input their preferred brands and product purchase history. We then analyze the user data to provide relevant product recommendations."

[1611] Generative AI models can be used to create a personalized shopping experience for each user, improving product discovery efficiency while also increasing user satisfaction.

[1612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1613] Step 1:

[1614] The device receives user input data. The user uses a smartphone app to input information such as lifestyle habits, shopping history, preferences, and purchasing patterns. Examples of input data include "My favorite brand is Nike" and "I recently purchased a pair of running shoes." The device receives this data and sends it to the server.

[1615] Step 2:

[1616] The server receives user data sent from the device. The input data includes the user's personal preferences and purchasing history. The received data is temporarily stored to be fed directly into the AI ​​algorithm.

[1617] Step 3:

[1618] The server analyzes the received data using artificial intelligence algorithms. Here, models built using machine learning libraries such as TensorFlow and PyTorch are used. Specifically, user data is input into the AI ​​model, which then makes predictions. For example, if a user inputs "Nike" and "running shoes," related product recommendations are made. The output generated by the AI ​​model is a customized product list.

[1619] Step 4:

[1620] Based on the analysis results of the AI ​​algorithm, the server generates a product list and promotion information customized for the user. The generated information includes optimal products and sale information based on the user's preferences and purchasing patterns. For example, recommendations such as "Nike sportswear" and "sale on running shoes" are made. This information is sent to the device.

[1621] Step 5:

[1622] The device receives customized product lists and promotional information from the server and displays them to the user. Based on the information displayed, the user can access and check specific product and sale information. For example, the app screen might display "New Nike sportswear" or "Special sale on running shoes."

[1623] Step 6:

[1624] The server uses the user's location information to provide navigation within the physical store. Location information is obtained from the smartphone's GPS sensor and beacons installed in the store. The server identifies the user's current location and calculates the optimal route to the desired shelf or section. Navigation information is sent to the device.

[1625] Step 7:

[1626] The device provides the user with navigation information sent from the server. Users can check route guidance on the app screen and navigate efficiently within the physical store. For example, a detailed route to reach the "Nike sportswear section" is displayed.

[1627] Step 8:

[1628] The device records the user's actions (product purchase information, store movement history, etc.) and sends the progress data to the server. The progress data includes information on whether the user purchased the suggested products and how they moved around the store. The server receives this data and monitors the progress.

[1629] Step 9:

[1630] The server analyzes the progress data and generates feedback, such as "There will be a sale on new Nike apparel on your next visit." The generated feedback is sent to the device.

[1631] Step 10:

[1632] The device provides the user with the feedback it receives from the server. The user can check the feedback through the app and use it to make their next purchase. For example, a notification will appear on the app screen saying, "Get a great deal on new Nike apparel on your next visit."

[1633] 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.

[1634] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input data and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[1635] Explaining program processing in natural language

[1636] 1. Enter user data

[1637] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[1638] The user enters this data and sends it to the terminal.

[1639] The terminal transmits the input data to the server.

[1640] 2. Entering Emotion Data

[1641] The terminal or compatible device collects the user's emotional data in real time and transmits it to the server.

[1642] 3. Data Receipt and Analysis

[1643] The server receives the user data and emotion data from the terminal.

[1644] The server analyzes the received data using artificial intelligence algorithms and emotion engines.

[1645] For example, if a user enters data such as "wake up at 7am, go to bed at 11pm," "work five days a week," and "lose 5kg in three months," the server will use this information to identify an appropriate activity schedule.

[1646] The emotion engine also analyzes the user's emotional data and makes adjustments based on the user's emotional state.

[1647] 4. Generate customized habits and action plans

[1648] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[1649] For example, the server might suggest things like "walking 30 minutes at 7:30 every morning," "going to the gym three times a week," or "adding a short meditation session on days when stress levels are high."

[1650] The server sends the generated plan to the terminal, which displays it to the user.

[1651] 5. Entering and monitoring progress data

[1652] Progress data regarding the activities performed by the user and changes in emotions are entered into the device.

[1653] The terminal transmits the progress data and emotion data to the server.

[1654] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[1655] 6. Generating and Providing Feedback

[1656] The server generates corrective or additional advice for the user based on the progress and emotional data.

[1657] For example, the server provides feedback such as "keep walking," "record your food intake for the next week," or "do a short meditation when you feel stressed."

[1658] The server sends the feedback to the device, which displays it to the user.

[1659] Specific examples

[1660] Example of a user setting a goal to lose 5kg in 3 months and also prioritizing stress management:

[1661] 1. Enter user data

[1662] The user enters "Wake up at 7am every day, go to bed at 11pm," "Work five days a week," and "Lose 5kg in three months," and the device sends this to the server.

[1663] 2. Entering Emotion Data

[1664] The user inputs their daily emotional state (such as stress level and mood), and the device sends this to the server.

[1665] 3. Data Receipt and Analysis

[1666] The server receives this data and analyzes it using AI algorithms and an emotion engine. For example, based on a user's activity patterns, work hours, and emotional state, it may determine that morning exercise is beneficial, or that relaxation activities are needed on days when stress levels are high.

[1667] 4. Generate customized habits and action plans

[1668] The server generates a specific plan such as "30 minutes of walking every morning at 7:30," "go to the gym three times a week," or "10 minutes of meditation on stressful days," and the device displays this to the user.

[1669] 5. Entering and monitoring progress data

[1670] The user enters progress data such as "I have completed today's walk" or "Today's emotional state is stress level 3," and the device sends this to the server.

[1671] The server monitors the progress and emotional data to assess achievement and emotional state.

[1672] 6. Generating and Providing Feedback

[1673] The server generates feedback such as "continue walking for the next week," "improve the quality of your diet," or "engage in a short meditation session the next time you feel stressed," and the device displays this to the user.

[1674] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[1675] The processing flow will be explained below.

[1676] Step 1:

[1677] The device provides the user with a form to enter data about their daily life, work situation, and personal goals.

[1678] Step 2:

[1679] The user enters data into the form, such as "wake up at 7am," "go to bed at 11pm," "work five days a week," and "lose 5kg in three months."

[1680] Step 3:

[1681] The terminal transmits the entered user data to the server.

[1682] Step 4:

[1683] The terminal or a compatible device collects the user's emotional data in real time, for example, by using the user's voice tone, facial expression recognition, self-evaluation, etc.

[1684] Step 5:

[1685] The device transmits the collected emotion data to a server.

[1686] Step 6:

[1687] A server receives the user data and the emotion data.

[1688] Step 7:

[1689] The server uses artificial intelligence algorithms and emotion engines to analyze the received data, such as analyzing the user's daily life patterns, working hours, and emotional state.

[1690] Step 8:

[1691] Based on the analysis results, the server generates habits and specific action plans tailored to the user's lifestyle, goals, and emotional state.

[1692] Step 9:

[1693] The server transmits the generated habits and action plans to the terminal.

[1694] Step 10:

[1695] The device displays the received plan to the user, allowing the user to confirm the specific action plan.

[1696] Step 11:

[1697] Users input data about their daily progress and emotional state into the device, such as "Completed today's walk" or "Today's stress level is 3."

[1698] Step 12:

[1699] The terminal transmits the progress data and emotion data to the server.

[1700] Step 13:

[1701] A server receives the progress data and emotion data and monitors the progress and emotional state of the user.

[1702] Step 14:

[1703] Based on the progress and emotional data, the server generates corrective or additional advice for the user, such as "keep walking," "improve the quality of your diet," or "take a short meditation session when you feel stressed."

[1704] Step 15:

[1705] The server generates feedback and sends it to the device.

[1706] Step 16:

[1707] The device displays feedback to the user, allowing them to see their next steps.

[1708] Example 2

[1709] 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."

[1710] Conventional systems have difficulty generating specific habits and action plans tailored to a user's individual needs and emotional state, and providing appropriate feedback based on the user's progress and emotional changes. This leads to a lack of comprehensive support for users to achieve their goals and manage their emotional state.

[1711] 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.

[1712] In this invention, the server includes means for using an artificial intelligence algorithm and an emotion engine to receive and analyze user input data and emotion data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring the user's progress data and emotion data, and means for generating and providing feedback to the user based on the progress data and emotion data, thereby providing a specific action plan tailored to the user's individual needs and emotional state and enabling comprehensive support for goal achievement and emotion management.

[1713] "User input data" is data that a user provides to the system about their lifestyle, work situation, personal goals, etc.

[1714] An "artificial intelligence algorithm" is a program that has computational methods for analyzing data, recognizing patterns, and making predictions.

[1715] An "emotion engine" is an algorithm or model that analyzes a user's emotional data and understands their emotional state.

[1716] "Customized habits and action plans" are specific action plans that are individually generated based on the user's input data and analysis results.

[1717] "Means for displaying the generated habits and behavioral plans to the user" refers to techniques or methods for visually presenting the generated behavioral plans to the user through a terminal or device.

[1718] "User progress data" refers to data that a user records and provides to the system regarding their daily activities and progress toward achieving their goals.

[1719] "Emotional data" refers to data that indicates the user's psychological or emotional state, and includes information such as heart rate, stress level, and mood.

[1720] "Means for monitoring" refers to the functions and methods by which the system continuously monitors and records the user's progress data and emotional data.

[1721] "Means for generating and providing feedback to the user" refers to the method by which the system makes useful advice or adjustments based on progress data and emotional data and communicates them to the user.

[1722] The present invention is a system that uses artificial intelligence algorithms and emotion engines to receive and analyze user input and emotion data, and based on the analysis generates customized habits and action plans for the user, displays them to the user, monitors their progress, and provides feedback.

[1723] The program of this system uses the following specific hardware and software to carry out a series of processes such as data entry, data analysis, generation of action plans, progress monitoring, and provision of feedback.

[1724] (Hardware and software used)

[1725] Hardware: Servers, devices (PCs, smartphones, tablets, etc.), biosensors (smartwatches, etc.)

[1726] Software: Artificial intelligence algorithms (TensorFlow, PyTorch, etc.), emotion engines, database management systems (MySQL, PostgreSQL, etc.)

[1727] As an example of specific behavior, the following prompt sentence is input into the generative AI model:

[1728] "I'm aiming to lose 5 kg in 3 months, but my work schedule is busy and I'm under a lot of stress. Could you please give me some advice on my daily activity schedule and stress management?"

[1729] (Example)

[1730] 1. Enter user data

[1731] The device presents the user with a form, which could be a web form or an app prompt, to enter data about their daily life, work situation, or personal goals.

[1732] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[1733] The security of the data is ensured by the device encrypting the data entered and sending it to the server.

[1734] 2. Entering Emotion Data

[1735] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time to understand the user's emotional state.

[1736] Users may also manually enter their mood or stress level, for example, entering "3 (high)" in response to the question, "What is your stress level today?"

[1737] The device transmits the collected and input emotion data to the server as needed.

[1738] 3. Data Receipt and Analysis

[1739] The server receives the user data and emotion data transmitted from the terminal.

[1740] The server first stores the received data in a database, then analyzes it using artificial intelligence algorithms and an emotion engine to generate a customized plan based on the user's activity patterns and goals.

[1741] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[1742] 4. Generate customized habits and action plans

[1743] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[1744] The server formats the generated action plan and sends it to the device in a format such as JSON.

[1745] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[1746] 5. Entering and monitoring progress data

[1747] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[1748] The device periodically sends this progress data to a server, and may also have an automatic synchronization function.

[1749] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[1750] 6. Generating and Providing Feedback

[1751] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[1752] The server generates feedback, formats it, and sends it to the device, where notifications can be used to instantly notify the user.

[1753] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[1754] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[1755] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1756] Step 1:

[1757] Entering User Data

[1758] The device will prompt the user to enter information about their daily life, work situation, and personal goals, either through a web form or an app input screen.

[1759] The user enters information such as "Wake up at 7am every day," "Work 5 days a week," and "Lose 5kg in 3 months" into the form and clicks the submit button.

[1760] The device sends the user's input data in JSON format to the server, which encrypts the data before sending it to ensure its security.

[1761] Input: Data about lifestyle habits and goals that users enter into forms

[1762] Output: User data sent to the server in JSON format.

[1763] Step 2:

[1764] Entering emotion data

[1765] The device or a compatible device such as a smartwatch collects the user's heart rate, sweat rate, and even smartphone sensor data in real time, which can then be used to understand the user's emotional state.

[1766] Users may also manually enter their mood or stress level within the app, for example by entering "3 (high)" in response to the question "What is your stress level today?"

[1767] The device periodically sends emotion data in JSON format to the server.

[1768] Input: User's heart rate, sweat rate, self-input stress level, and other emotional data

[1769] Output: Emotion data in JSON format sent to the server

[1770] Step 3:

[1771] Receiving and analyzing data

[1772] The server receives the user data and emotion data sent from the device, and the received data is first stored in a database.

[1773] The server analyzes the data using artificial intelligence algorithms and emotion engines (using TensorFlow and PyTorch), and generates a customized plan based on the user's activity patterns and goals.

[1774] For example, if a user enters data such as "wake up at 7am," "work five days a week," and "lose 5kg in three months," the server will use this information to generate suggestions such as "walking for 30 minutes at 7:30 every morning is effective" and "go to the gym three times a week."

[1775] Input: User data and emotion data in JSON format sent to the server

[1776] Output: Customized habits and action plans

[1777] Step 4:

[1778] Generate customized habits and action plans

[1779] The server uses the analysis results to generate an action plan that best suits the user's lifestyle, goals, and emotional state, including suggestions to add meditation sessions when the user is under stress.

[1780] The server sends the generated action plan in JSON format to the terminal.

[1781] The device receives the action plan and displays it to the user, possibly in the form of an in-app dashboard or calendar.

[1782] Input: Output data of the AI ​​algorithm based on the analysis results

[1783] Output: A customized action plan in JSON format

[1784] Step 5:

[1785] Entering and monitoring progress data

[1786] The user inputs progress data about the activities they have performed (e.g., "Completed a walk") and changes in their emotions (e.g., "Today's stress level is 5"). A UI for inputting progress data is provided.

[1787] The device periodically sends this progress data to the server in JSON format, and may also have an automatic synchronization function.

[1788] The server receives the progress data and emotional data, and uses that data to monitor the user's progress and emotional state in real time.

[1789] Input: Progress and emotion data entered by the user

[1790] Output: Progress and emotion data sent to the server in JSON format.

[1791] Step 6:

[1792] Generating and Providing Feedback

[1793] The server analyzes the user's progress and emotional data and generates feedback, such as "Since you've been walking, add jogging as your next step" or "Since your stress level is high, try meditation."

[1794] The server generates feedback and sends it to the device in JSON format, allowing the user to be notified immediately using notifications.

[1795] The device will provide feedback to the user in a format that is immediately visible to the user, such as a push notification or in-app message.

[1796] Input: User progress and emotion data

[1797] Output: Feedback in JSON format

[1798] In this way, the present invention provides a specific action plan tailored to the user's individual needs and emotional state, providing comprehensive support for goal achievement and emotional management.

[1799] (Application example 2)

[1800] 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 robot 414 will be referred to as a "terminal."

[1801] In modern brick-and-mortar stores, it is difficult to quickly and efficiently make customized product recommendations based on individual customer preferences and emotional states. In particular, there is a lack of technology that can analyze a customer's ongoing shopping experience in real time and make optimal product recommendations. This can result in lower customer satisfaction and a loss of purchasing motivation.

[1802] 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 receiving user input data, means for using an artificial intelligence algorithm to analyze the received data, means for generating habits and action plans customized for the user based on the analysis results, means for displaying the generated habits and action plans to the user, means for receiving and monitoring user progress data, means for generating and providing feedback to the user based on the progress data, means for analyzing the user's profile and emotional data in-store and generating product suggestions tailored to individual preferences, and means for displaying the suggested products to the user in-store. This makes it possible to customize the shopping experience in physical stores in real time and improve customer satisfaction and purchasing motivation.

[1803] "User Input Data" means information provided manually or automatically by a User regarding their profile, preferences, lifestyle, budget, etc.

[1804] "Artificial intelligence algorithms" refers to the machine learning models and other AI techniques used for analysis and interpretation of received data.

[1805] "Means for generating customized habits and action plans based on analysis results" refers to the process of setting and proposing optimal action plans and habits to users based on the analysis results.

[1806] "Means for displaying the generated habits and behavioral plan to the user" refers to a mechanism for visually presenting the proposed plan to the user using the user's device, an in-store display, etc.

[1807] "User progress data" refers to information about the actions taken by a user and the results achieved, and is data used to monitor progress.

[1808] "Means for generating and providing feedback to users based on progress data" refers to the process of generating improvements and additional advice based on the user's progress and notifying the user.

[1809] "Means for analyzing user profile and emotional data in-store to generate product suggestions tailored to individual preferences" refers to the process of analyzing data provided by users in-store in real time and suggesting products that suit their individual preferences.

[1810] "Means for displaying suggested products to users in-store" refers to a system that uses in-store displays and users' devices to visually provide product suggestions based on analysis.

[1811] In order to implement this invention, it is necessary to build a system that collects user input data and emotion data, analyzes the data, and generates customized habits and action plans. Specific embodiments will be described below.

[1812] System configuration

[1813] The system of the present invention comprises the following main components:

[1814] 1. User device: Smartphone or tablet, etc. Collects user input data and emotion data and sends them to the server.

[1815] 2. Server: Analyzes the received data, generates customized habits and action plans, and provides feedback.

[1816] 3. Display device: This can be a display in a store or a user's device. It displays the generated plans and product suggestions to the user.

[1817] Data entry and analysis

[1818] The device provides users with an input form about their daily life, work situation, and personal goals. This allows users to enter data such as "I like the outdoors," "My style is casual," and "My budget is between ¥5,000 and ¥15,000." It can also collect real-time emotional data (e.g., "Stress level 4," "I'm happy")

[1819] Generate customized proposals

[1820] The server uses an artificial intelligence algorithm to analyze the user's profile data and emotional data received from the device. Based on the results, it generates optimal product suggestions for the user, such as "relaxing outdoor wear" or "casual jeans." The suggestions are then displayed on displays in the store and on the user's device.

[1821] Progress data monitoring and feedback

[1822] When a user enters the results of purchasing or trying on a suggested product into the terminal, the server receives and monitors this as progress data. For example, data such as "Purchased items: T-shirt, jeans" and "Feedback: Satisfied" can be used. Based on this, the server generates additional feedback and provides advice to the user, such as "Try this color T-shirt next time."

[1823] Hardware and software used

[1824] EmotionAnalyzer: Software for analyzing emotional data.

[1825] Recommender: Software that makes product suggestions based on user profiles.

[1826] RESTful API: A protocol for data communication between a terminal and a server.

[1827] Specific examples

[1828] If User A likes "outdoor wear" and "casual style" and enters "stress level 4" as emotion data, the following process will occur:

[1829] The server analyzes the data received and suggests "casual, outdoor wear" that will help users relax.

[1830] Displays within the store include "casual outdoor jackets" and "relaxing items."

[1831] The user purchases or tries on the suggested product and then enters and submits their feedback on the device.

[1832] The server uses this progress data to improve its next suggestion and provides feedback such as, "Next time, try matching pants with this jacket."

[1833] Prompt Sentence Examples

[1834] Here are some example prompts for a generative AI model:

[1835] Suggest the best products to the user based on their profile (style: "casual", size: "M", budget: "¥5000 - ¥15000", lifestyle: "outdoor") and emotional data (emotion: "happy", intensity: 7).

[1836] In this way, the present invention can provide a specific action plan tailored to the user's individual needs and emotional state, enhancing the in-store shopping experience.

[1837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1838] Step 1:

[1839] The user terminal collects user input data. It provides a form for the user to enter information about their daily life, preferences, budget, and lifestyle, and the user manually enters this data. The user terminal receives this data and sends it to the server. The input at this time is, for example, "Style: Casual," "Size: M," "Budget: ¥5,000 - ¥15,000," and "Lifestyle: Outdoor." The output is that this user data is sent to the server.

[1840] Step 2:

[1841] The user device also collects emotional data. It provides a means for users to input their emotional state in real time while shopping, collecting emotional data such as "Stress level: 4" or "Mood: happy." The user device receives this emotional data and sends it to the server. The input is data about the user's emotional state, and the output is the transmission of this data to the server.

[1842] Step 3:

[1843] The server analyzes the received user profile data and emotional data. The server uses EmotionAnalyzer software to analyze the emotional state and Recommender software to generate product suggestions based on the user profile. The input data is the user profile and emotional data, and specific product suggestions such as "relaxed outdoor jacket" and "casual jeans" are generated as a result of data analysis. The output is the analysis results and the suggested products.

[1844] Step 4:

[1845] The server sends the generated product suggestions to the user's terminal or a display device in the store. The user's terminal or display visually presents the suggested products to the user. For example, the display may show "Recommended product: casual outdoor jacket." The input is the suggested product data from the server, and the output is the display of this product information to the user.

[1846] Step 5:

[1847] The user tries on the suggested products and enters the results of their purchase into the terminal. For example, progress data such as "Purchased item: T-shirt" and "Feedback: Satisfied" is entered. The user terminal receives this data and sends it to the server. The input data is the user's progress information, and the output is that this is sent to the server.

[1848] Step 6:

[1849] The server receives the progress data and monitors the user's behavior. Based on the progress data, the server generates the next feedback and provides it to the user. For example, personalized advice such as "Try this color T-shirt next time" is generated. The input is the progress data, and the feedback is generated as a result of data analysis. The output is the feedback provided to the user.

[1850] Step 7:

[1851] The server tracks the feedback provided to the user and continuously evaluates the user's satisfaction and behavioral improvements, allowing it to continue optimizing the user's shopping experience. The input is the user's response to the feedback and additional progress data, and the output is the next suggestion or improvement of the feedback.

[1852] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1853] 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.

[1854] 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 robot 414.

[1855] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1856] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1857] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1858] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1859] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1860] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1861] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1862] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1863] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1864] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1865] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1866] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1867] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1868] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1869] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1870] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1871] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1872] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1873] The following is further disclosed regarding the above embodiment.

[1874] (Claim 1)

[1875] means for receiving user input data;

[1876] means for using an artificial intelligence algorithm to analyze the received data;

[1877] means for generating a customized habit and action plan for the user based on the analysis results;

[1878] a means for displaying the generated habits and action plans to the user;

[1879] means for receiving and monitoring user progress data;

[1880] a means for generating and providing feedback to the user based on the progress data;

[1881] A system including:

[1882] (Claim 2)

[1883] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's daily life, work situation, and personal goals.

[1884] (Claim 3)

[1885] 10. The system of claim 1, wherein the generating means includes means for generating feedback to suggest specific actions and habit modifications to the user.

[1886] "Example 1"

[1887] (Claim 1)

[1888] means for receiving user input data;

[1889] means for using an artificial intelligence algorithm to analyze the received data;

[1890] means for generating a customized habit and action plan for the user based on the analysis results;

[1891] a means for displaying the generated habits and action plans to the user;

[1892] means for receiving and monitoring user progress data;

[1893] a means for generating and providing feedback to the user based on the progress data;

[1894] means for inputting user input into the terminal through an interface;

[1895] A means for the terminal to transmit the input data to a server via the Internet;

[1896] A means by which artificial intelligence algorithms identify a customized plan of action based on a user's lifestyle and goals;

[1897] a means by which the device displays a specific action plan to the user;

[1898] A means for inputting the user's activity progress into the terminal and transmitting it to the server;

[1899] A means for the server to analyze your progress data and generate necessary improvements and additional advice;

[1900] A system including:

[1901] (Claim 2)

[1902] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's daily life, work situation, and personal goals.

[1903] (Claim 3)

[1904] 10. The system of claim 1, wherein the generating means includes means for generating feedback to suggest specific actions and habit modifications to the user.

[1905] "Application Example 1"

[1906] (Claim 1)

[1907] means for receiving user input data;

[1908] means for using an artificial intelligence algorithm to analyze the received data;

[1909] means for generating a customized habit and action plan for the user based on the analysis results;

[1910] a means for displaying the generated habits and action plans to the user;

[1911] means for receiving and monitoring user progress data;

[1912] a means for generating and providing feedback to the user based on the progress data;

[1913] A means of analyzing users' purchasing history, preferences, and purchasing patterns to suggest customized products and promotions;

[1914] A means of providing navigation within a physical store using the user's location information;

[1915] A system including:

[1916] (Claim 2)

[1917] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's daily life, work situation, and personal goals.

[1918] (Claim 3)

[1919] 10. The system of claim 1, wherein the generating means includes means for generating feedback to suggest specific actions and habit modifications to the user.

[1920] "Example 2: Combining Emotion Engines"

[1921] (Claim 1)

[1922] means for receiving user input data;

[1923] means for using artificial intelligence algorithms and emotion engines to analyze the received data and emotion data;

[1924] means for generating a customized habit and action plan for the user based on the analysis results;

[1925] a means for displaying the generated habits and action plans to the user;

[1926] means for receiving and monitoring user progress and emotion data;

[1927] a means for generating and providing feedback to the user based on the progress data and the emotion data;

[1928] A system including:

[1929] (Claim 2)

[1930] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's daily life, work situation, and personal goals.

[1931] (Claim 3)

[1932] 10. The system of claim 1, wherein the generating means includes means for generating feedback to suggest specific actions and habit modifications to the user.

[1933] "Application example 2 when combining emotion engines"

[1934] (Claim 1)

[1935] means for receiving user input data;

[1936] means for using an artificial intelligence algorithm to analyze the received data;

[1937] means for generating a customized habit and action plan for the user based on the analysis results;

[1938] a means for displaying the generated habits and action plans to the user;

[1939] means for receiving and monitoring user progress data;

[1940] a means for generating and providing feedback to the user based on the progress data;

[1941] A means of analyzing user profile and sentiment data in-store to generate personalized product recommendations;

[1942] A means of displaying suggested products to users in-store;

[1943] A system including:

[1944] (Claim 2)

[1945] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the user's daily life, work situation, and personal goals.

[1946] (Claim 3)

[1947] 10. The system of claim 1, wherein the generating means includes means for generating feedback to suggest specific actions and habit modifications to the user. [Explanation of symbols]

[1948] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user input data; means for using an artificial intelligence algorithm to analyze the received data; means for generating a customized habit and action plan for the user based on the analysis results; a means for displaying the generated habits and action plans to the user; means for receiving and monitoring user progress data; a means for generating and providing feedback to the user based on the progress data; A system including:

2. 2. The system of claim 1, wherein the analysis means includes means for analyzing the user's daily life, work situation, and personal goals.

3. 2. The system of claim 1, wherein the generating means includes means for generating feedback to suggest specific actions and habit modifications to the user.

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