system

Abstract

We provide the system. [Solution] A means for collecting user behavior data, analyzing behavioral history, and generating user profiles, A means for automatically setting tasks and rewards suitable for the user using AI based on the aforementioned profile, A means for monitoring the progress of the aforementioned task and awarding reward points when it is achieved, In order to maintain users' continued motivation, we offer methods such as login bonuses and retention rewards, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a point activity platform, in order for an investor to effectively promote the behavior of users at a low cost, it is necessary to set optimal tasks and rewards for individual users. However, in conventional systems, this process is often performed manually, requiring high costs and labor for effective incentive design. There is also a problem that it is difficult to maintain continuous motivation and the participation willingness of users tends to decline.

Means for Solving the Problems

[0005] This invention first collects user behavior data and generates user profiles by analyzing it using AI. Based on these profiles, it enables the automatic setting of optimal tasks and rewards for each user. Furthermore, this system monitors the user's task completion status and provides a mechanism to improve user satisfaction by immediately awarding reward points upon completion. In addition, the introduction of login bonuses and continuous user rewards enables long-term user engagement. As a result, investors can efficiently promote desirable user behavior at low cost.

[0006] "User behavior data" refers to information collected in relation to the activities that users perform on a daily basis, and includes app usage history and fitness data.

[0007] A "user profile" is personalized information that shows a user's behavioral patterns and preferences, generated by analyzing collected behavioral data.

[0008] "AI" refers to artificial intelligence technology, particularly in data analysis, which learns user behavior patterns and supports the setting of optimal tasks and rewards.

[0009] A "challenge" is a specific action goal presented to the user, and a requirement for fulfilling the set conditions.

[0010] "Rewards" refer to incentives such as points that users earn by completing tasks, and are used as a means to improve user motivation.

[0011] A "login bonus" is a reward that users can receive periodically by logging into the system, and it serves to encourage continued use.

[0012] "Continued participation rewards" are special rewards that users can receive when they consistently take action over a certain period of time, and are used to encourage long-term use. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system of the present invention provides an effective means for promoting user behavior. The system mainly consists of three elements: a server, a terminal, and a user.

[0035] The server plays a central role, responsible for generating user profiles. The server first collects user behavior data and analyzes it using AI. Based on the analysis, it generates a user profile and automatically sets optimal tasks and rewards accordingly. The server then transmits this information to the terminal and notifies the user.

[0036] The terminal functions as an interface with the user. The terminal presents the user with tasks received from the server and records the user's actions. When the user completes a task, the terminal sends progress data to the server and, in cooperation with the server, immediately processes the awarding of reward points.

[0037] Users interact with the system through their devices. Users plan and execute actions to achieve the tasks presented to them. For example, if the goal is to improve health, users will work on a daily step count challenge. The system rewards users with points each time they complete a task, giving them a sense of accomplishment.

[0038] Furthermore, the server designs login bonuses and retention rewards to support continued user engagement. The device prompts users for these rewards and provides elements that encourage continued use.

[0039] As a concrete example, a fitness app might be designed to reward users with special points if they achieve their step goal for a week straight. This can motivate users and encourage long-term use. This system allows investors to manage user behavior efficiently and at low cost, and provide appropriate incentives.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server periodically receives user behavior data from the device. This data includes, for example, the user's daily step count and app usage history. The server inputs this data into an AI system to analyze the user's behavior patterns.

[0043] Step 2:

[0044] The server generates user profiles based on the analyzed data. These profiles include information about the user's preferences and past behavioral characteristics. Based on these profiles, the server uses AI to automatically set the optimal tasks and rewards for each user.

[0045] Step 3:

[0046] The server sends information about the set task and reward to the device. The device then notifies the user of this information and presents specific behavioral goals. For example, a task such as walking 10,000 steps a day might be displayed.

[0047] Step 4:

[0048] Users work on tasks presented by the server through their devices. Through their daily activities, users take actions to complete these tasks. The device records the user's activities and continuously transmits progress data to the server.

[0049] Step 5:

[0050] The server analyzes the received progress data to confirm whether the user has completed the assigned task. Once completion is confirmed, the server immediately calculates the reward points and sends that information to the terminal.

[0051] Step 6:

[0052] The terminal notifies the user of the reward points received from the server. This allows the user to gain a sense of accomplishment and increase their motivation for the next challenge.

[0053] Step 7:

[0054] The server plans login bonuses and retention rewards to maintain user motivation and sends this information to the device. The device then presents this reward information to the user to encourage further use.

[0055] (Example 1)

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

[0057] Conventional systems often lack sufficient individualization in promoting user behavior, making it difficult to maintain user motivation over the long term. Furthermore, rewards and tasks are frequently set manually, limiting their effectiveness in facilitating behavioral change.

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

[0059] In this invention, the server includes means for a data processing device to acquire user activity information and analyze activity history to form a user profile; means for automatically setting tasks and rewards suitable for the user using artificial intelligence based on the profile; and means for monitoring the achievement level of the tasks and assigning rewards upon completion. This enables the automatic setting of individualized tasks and rewards, and efficiently supports the sustained motivation of the user.

[0060] A "data processing device" is a device that can acquire and analyze information about users' behavior and actions.

[0061] "Action information" refers to data about the user's behavior and activities, and is fundamental information used to analyze behavioral patterns.

[0062] A "user profile" is personalized information obtained by analyzing a user's behavioral history, and is used for setting tasks and providing rewards.

[0063] "Artificial intelligence" is a technology that analyzes large amounts of data and automatically sets tasks and rewards tailored to the user, and it includes a variety of algorithms.

[0064] A "challenge" is a goal or challenge that users should aim to achieve, and it is set to encourage action.

[0065] "Rewards" are incentives given to users upon completing tasks, and they play a role in improving motivation.

[0066] "Achievement level" is an indicator that shows the extent to which a user has achieved the assigned tasks, and it serves as a criterion for determining whether to award rewards.

[0067] "Connection benefits" are bonuses offered to users who regularly access the system, encouraging continued use.

[0068] A "generative AI model" is an artificial intelligence model used to set tasks and rewards that are adapted to the user, enabling efficient data analysis.

[0069] This invention is a system that facilitates user behavior and supports sustained engagement. It mainly consists of three elements: a server, a terminal, and the user.

[0070] The server plays a central role in the system, acquiring user activity information. This information is collected from hardware such as smartphones and wearable devices. The server analyzes the collected information using AI analysis tools such as TENSORFLOW® and PyTorch. Based on the analysis results, it forms a user profile and has the function to automatically set tasks and rewards appropriate to that profile using a generative AI model.

[0071] The terminal functions as an interface device with the user. It notifies the user of tasks received from the server and records their progress. When the user completes a task, the terminal sends progress data to the server, supporting the process of immediately awarding reward points.

[0072] Users tackle challenges through their devices. For example, if the goal is health, users aim to achieve their daily step count target. The system rewards users with points or badges each time they achieve their goal. This helps users stay motivated and manage their own progress.

[0073] For example, fitness apps are designed to reward users with special points for achieving their step goal for a week straight. Such incentives effectively encourage user behavior.

[0074] An example of a prompt might be the text, "Suggest an appropriate reward to give when the user achieves their exercise goal." This prompt is input into the generative AI model and serves as the basis for guiding it to set the optimal reward.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server acquires user activity information from smartphones and wearable devices. It receives raw data transmitted from each device as input (e.g., steps, heart rate, location information, etc.). The server stores the received data in a database and organizes it for the next analysis step. The output is a set of organized behavioral data.

[0078] Step 2:

[0079] The server analyzes the acquired behavioral information using AI analysis tools such as TensorFlow and PyTorch. This analysis identifies behavioral patterns and extracts user trends. The data organized in step 1 is used as input, and this results in the output of a user profile. The profile includes estimates of the current health status and activity level.

[0080] Step 3:

[0081] The server uses a generative AI model to set optimal tasks and rewards based on the user profile. The input is the user profile created in step 2, and based on this, the AI ​​determines what kind of tasks will motivate the user. The output is a customized task and reward plan for each user.

[0082] Step 4:

[0083] The server sends the generated task and reward information to the device. This input is the customized plan output in step 3. The output to the device includes task notification data and reward information. This allows the user to check the new task on their device.

[0084] Step 5:

[0085] The device notifies the user of tasks received from the server. Specifically, it executes push notifications and in-app messages to allow the user to check the tasks immediately. The device also prepares to record the user's actions. The output is the task information displayed to the user.

[0086] Step 6:

[0087] Users work on tasks using the device. Specifically, they perform tasks related to exercise goals and health management as presented, and the device automatically records this information. The input consists of the task content provided via the device, and daily activities are carried out based on this information.

[0088] Step 7:

[0089] The terminal records the user's task completion status and sends progress data to the server at regular intervals. The input is a log of the user's activities, and the output received by the server is the latest progress information. Based on this information, preparations are made for the next step to be executed.

[0090] Step 8:

[0091] The server analyzes the received progress data and immediately calculates and awards reward points upon completion of the task. The input is the progress data obtained in step 7, and the output is the reward information reflected in the user's account. This process helps maintain user motivation.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] The present invention aims to provide a method for motivating consumer behavior in order to enhance the in-store experience for consumers and encourage store visits and purchases. Conventional systems have limited means of increasing consumer motivation, making it difficult to maintain long-term engagement.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate a user profile; means for automatically setting appropriate tasks and rewards for the user using AI based on the profile; and means for generating specific behavioral tasks to motivate the user's real-world actions and notifying the user via a smart device. This allows consumers to receive personalized challenges and immediate rewards through real-world actions, thereby enhancing the in-store experience and promoting continuous engagement.

[0097] "User behavior data" refers to information about various behavioral histories generated by users, including, for example, the number of store visits and product scanning history.

[0098] A "user profile" is a collection of information that indicates the preferences and behavioral patterns of individual users, generated from the analysis of behavioral data.

[0099] "AI" refers to artificial intelligence technology that analyzes user data to determine the optimal tasks and rewards.

[0100] A "challenge" is a specific action or challenge presented to the user, designed to encourage actions to be taken within the physical store.

[0101] "Reward points" are incentives that users earn by completing tasks, and are provided in the form of coupons, points, etc., that can be used on their next visit or purchase.

[0102] A "login bonus" is a reward that users receive for regularly using the system, and it is a factor that supports continued user engagement.

[0103] "Continued use benefits" are additional incentives that users receive for continuing to use the system, and are designed to encourage continued use.

[0104] "Real-world actions" refer to activities that users perform in actual physical environments, including trying on or testing products in stores.

[0105] A "smart device" is a terminal device used by a user, such as a smartphone or smart glasses, that has the function of notifying or recording information.

[0106] This system is designed to encourage user behavior and enrich the in-store experience. The server acts as the system's core, generating profiles using behavioral data collected from users. This involves the server analyzing the collected data using AI models, such as TensorFlow or PyTorch. Based on these profiles, optimal behavioral tasks and rewards are automatically set.

[0107] The server also generates specific tasks to motivate users to act in the real world and sends them to their devices. These devices are smart devices such as smartphones and smart glasses, and task notifications are sent to the user through these devices. The tasks are tailored to the user and may include tasks such as trying on new products or purchasing specific promotional items.

[0108] When users complete these tasks, action data is sent from their device to the server. The server processes the data in real time and verifies task completion. After verification, the server immediately calculates reward points and reflects them in the user's account. This allows users to receive immediate feedback through their in-store experience, providing them with a greater sense of incentive.

[0109] To give a concrete example, suppose a user visits a fashion brand's store using a smartphone app and takes on a task instructed by the app to "try on three new items." Once the user completes this task, the system immediately issues a special coupon and notifies them that it can be used for purchases in the store.

[0110] An example of a prompt for a generative AI model would be, "Design an AI model that sets optimal challenges and rewards to encourage user behavior in-store." In this way, the system provides a mechanism to maximize the individual consumer experience.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server collects data on the user's past behavior. The input is the behavioral history obtained via the terminal, and the output is an aggregate of this behavioral data. The server stores the data in a database and uses it for analysis.

[0114] Step 2:

[0115] The server analyzes the collected behavioral data using a generating AI model. The input is the behavioral data obtained in step 1, and the output is a profile for each user. This profile includes analysis results that reflect behavioral tendencies and preferences. The AI ​​model applies algorithms based on the data to identify trends.

[0116] Step 3:

[0117] The server sets tasks appropriate for the user based on the generated user profile. The input is profile data, and the output is appropriate task content. Here, products and experiences highly relevant to the user are selected.

[0118] Step 4:

[0119] The device notifies the user of tasks sent from the server. The input is the task content, and the output is the notification the user receives. The smart device presents the user with a specific task and prompts them to take action.

[0120] Step 5:

[0121] The user completes tasks notified via a terminal. The input is the presented task, and the output is the result of its execution. The user completes tasks such as trying on products or performing specific actions and reports them to the system.

[0122] Step 6:

[0123] The device sends the user's actions to the server. The input is the result of the user's task completion, and the output is the data sent to the server. The smart device sends the action data back to the server in real time.

[0124] Step 7:

[0125] The server reviews the submitted execution data and checks if the task has been completed. The input is the execution data, and the output is the completion status. Once completion is confirmed, the reward is prepared immediately.

[0126] Step 8:

[0127] The server awards reward points to users based on their completion status. The input is the completion status, and the output is the reflection of the reward in the user's account. The system adds points or coupons to the user's account.

[0128] Through this entire process, the system can motivate user behavior, enrich the in-store experience, and foster long-term engagement.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is a system that analyzes user behavior data and combines it with an emotion engine to set tasks and rewards that are more suitable for individual users. The system mainly consists of a server, a terminal, an emotion engine, and a user.

[0131] The server handles central processing, receiving user behavior data transmitted from terminals. The server analyzes this data using AI to generate user profiles. Based on these profiles, the server sets optimal tasks and rewards. In this process, the emotion engine plays a role in analyzing data to estimate the user's emotional state.

[0132] The emotion engine is designed to estimate changes in emotional state from user behavior data. For example, if it is estimated that a user experienced stress in their daily activities, the emotion engine provides the server with information to appropriately adjust the reward content and task difficulty.

[0133] The device is responsible for presenting the user with tasks and reward information sent from the server. It also sends the user's emotional state and behavioral data back to the server in real time to receive feedback. When the user completes a task, the device notifies them of an evaluation result tailored to their emotional state. Furthermore, the device provides motivational feedback adjusted based on the user's emotional state.

[0134] Users take on challenges presented through their devices and aim to achieve them. For example, when a user works towards a goal of walking 10,000 steps daily using a health app, the emotion engine evaluates the stress and satisfaction experienced during the process and adjusts rewards to help them achieve their goal. In this way, the server, device, and emotion engine work together to provide users with a personalized experience and promote long-term engagement.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] Users use their devices to perform everyday activities and record their activity data (e.g., steps taken, heart rate, app usage). The devices then send this data to a server.

[0138] Step 2:

[0139] The server analyzes the received behavioral data and uses AI to generate a user profile. This profile reflects the user's behavioral patterns and preferences.

[0140] Step 3:

[0141] The server sets the most suitable tasks and rewards for the user based on the generated profile. This setting also takes into account the results of the emotion engine's analysis. The emotion engine provides emotional states estimated from the user's behavioral data.

[0142] Step 4:

[0143] The emotion engine analyzes the user's emotional state (e.g., stress, satisfaction) from behavioral data and provides it to the server. The server uses this information to adjust the difficulty of the tasks and the content of the rewards.

[0144] Step 5:

[0145] The server sends information about the adjusted tasks and rewards to the device. The device notifies the user of this information and presents specific tasks. For example, it might display something like, "Earn a special bonus if you walk 10,000 steps today."

[0146] Step 6:

[0147] Users work on tasks and monitor their progress through their devices. These devices continuously send this progress data to the server.

[0148] Step 7:

[0149] The server analyzes the submitted progress data to determine whether the user has completed the task. If completion is confirmed, the server calculates reward points and sends them to the device.

[0150] Step 8:

[0151] The device notifies the user of reward information received from the server. The user feels a sense of accomplishment and gains motivation for the next task. The device also sends follow-up notifications to the user based on feedback from the emotion engine (for example, a message such as "Your recent efforts have been recognized").

[0152] Step 9:

[0153] The device displays information about regular login bonuses and continuous user rewards to the user, maintaining long-term motivation. The server updates this reward information as needed and delivers it to the device.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] Traditionally, providing individually optimized experiences based on user behavior and emotional states has been challenging. In particular, there is a need for effective methods to operate dynamically adjustable task settings and reward systems to sustain user motivation and maintain long-term engagement.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes means for acquiring user behavior data and analyzing the behavior history to create a user profile; means for automatically setting optimal goals and rewards for the user using generative AI technology; and means for estimating the user's emotional state using an emotion analysis engine and dynamically adjusting the goals and rewards. This makes it possible to provide individually optimized tasks and rewards to users, thereby maintaining motivation and improving engagement.

[0159] "Action data" refers to data collected from information related to the user's activities and behaviors.

[0160] "Activity history" refers to a record of a user's activities over time and is used to analyze the user's behavioral patterns.

[0161] A "user profile" is a dataset that represents the individual characteristics and behavioral tendencies of a user, generated based on their behavioral data and history.

[0162] "Generative AI technology" is a technology that utilizes artificial intelligence models to generate optimal results based on user profiles and other input data.

[0163] "Goals" refer to specific challenges or tasks that users should achieve.

[0164] "Reward" refers to the benefits or incentives that users receive when they achieve their goals.

[0165] An "emotion analysis engine" is software or a system that estimates a user's emotional state from their behavioral data and adjusts the information and results accordingly.

[0166] "Motivation" refers to providing users with stimuli or reasons to continue a particular behavior.

[0167] The embodiments for carrying out this invention will be described mainly by dividing them into three roles: server, terminal, and user.

[0168] The server is responsible for central data processing. First, it receives user behavior data transmitted from the terminal. This behavior data includes information such as the user's activities and operation history. Next, the server uses generative AI technology to analyze this behavior data and generate a user profile. This profile facilitates personalized goal setting and reward design based on behavioral patterns extracted from the behavior history. At this time, an emotion analysis engine is used to estimate the user's emotional state, enabling dynamic and flexible adjustment of tasks and rewards.

[0169] The device presents the user with tasks and reward information sent from the server. Specifically, the device displays appropriate feedback and motivational messages to the user. It also plays a role in acquiring the user's latest behavioral data and sending it back to the server in real time. This allows the server to continuously update the user profile based on the latest data.

[0170] Users take on challenges presented via their devices and aim to achieve their goals. For example, in a health app, if a user aims to walk 10,000 steps every day, the emotion analysis engine evaluates the user's stress and satisfaction during the process and adjusts rewards to support goal achievement.

[0171] An example of a prompt message is: "Based on the user's daily step count data in the health app, and considering the stress level analyzed by the emotion engine, design the optimal reward scenario."

[0172] This system aims to provide users with personalized and optimal challenges and rewards by having servers, terminals, and an emotion engine work together, thereby maintaining user motivation and promoting long-term engagement.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The device collects user activity data through sensors and input interfaces. Inputs include step count and app usage history. The raw data is sent to a server as output. Specifically, the device periodically collects data and transfers it to the server via the network.

[0176] Step 2:

[0177] The server receives behavioral data from the terminal and analyzes it using a generating AI model. The input is behavioral data sent from the terminal. Data processing involves analyzing behavioral patterns and generating a user profile. The output is the generated user profile. Specifically, the server uses algorithms to perform data transformation and pattern identification.

[0178] Step 3:

[0179] The server uses an emotion analysis engine to estimate the user's emotional state. Inputs include the user profile and recent behavioral data. Data calculations involve determining emotional indices to estimate the user's stress level and satisfaction level. The output is the user's emotional state. Specifically, multiple emotional indices are aggregated, and the evaluation results are analyzed.

[0180] Step 4:

[0181] The server uses generative AI technology to automatically set the optimal tasks and rewards for each user. Inputs include user profiles and emotional states. Data calculations are performed to design tasks and rewards based on this information. The output is customized tasks and rewards for each user. Specifically, a predictive model is used to simulate multiple scenarios and derive the optimal solution.

[0182] Step 5:

[0183] The device presents the user with task and reward information sent from the server. Inputs include task data and reward data from the server. Output is presented to the user as visual or audible feedback. Specifically, the device uses a notification function to inform the user of new tasks and rewards.

[0184] Step 6:

[0185] Users work on the assigned tasks and input the results into a terminal. Input includes the user's activity results and feedback. Output is that data sent back to the server via the terminal. Specifically, users operate the application screen to perform self-reporting and automated measurements.

[0186] Step 7:

[0187] The server continuously optimizes the entire system based on user feedback. Inputs include the user's latest behavioral data and feedback. Data processing involves continuous analysis and learning to update user profiles. Outputs are used to inform future task settings and reward designs. Specifically, the server forms a feedback loop to improve the accuracy of the AI ​​model.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0190] In today's consumer society, providing services that meet user needs and emotions is a crucial challenge. In particular, improving the user experience in physical spaces requires understanding users' real-time emotional states and providing personalized benefits and services immediately. However, conventional systems have struggled to effectively analyze users' emotional states and appropriately propose personalized benefits based on that analysis.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate user characteristic data; means for automatically setting tasks and rewards suitable for the user using machine learning technology; and means for estimating the user's emotional state in real space via a virtual vision device. This enables immediate reward suggestions based on the user's emotional state.

[0193] "User characteristic data" refers to data that shows attribute information unique to individual users, obtained by analyzing the user's behavioral history.

[0194] "Machine learning technology" refers to algorithms and methods for automatically discovering patterns and rules by analyzing large amounts of data, and for predicting or classifying new information.

[0195] A "virtual vision device" is a device that can overlay digital information onto a real-world environment and is used to assist in the presentation and manipulation of information.

[0196] "Emotional state" refers to the user's psychological and emotional state at any given time, and is primarily estimated by analyzing data obtained from external sources.

[0197] "Proposing special benefits" is the process of proposing special services or rewards to users based on their behavior and emotional state.

[0198] This invention utilizes a server, a virtual vision device, and an emotion analysis engine to implement a system that appropriately provides benefits based on the emotional state of the user. The server is responsible for collecting user behavior data, analyzing its history using behavior analysis algorithms, and generating user characteristic data. Database software and machine learning frameworks (e.g., TensorFlow) are used in this process.

[0199] The virtual vision device, acting as the terminal, acquires the user's facial expressions and movements in real time and processes the data with an emotion analysis engine to estimate their emotional state. Image processing libraries (e.g., OpenCV) can be used for this process. Based on this emotional state, the server then sets user-specific tasks and proposes corresponding rewards.

[0200] Users receive the suggested benefits via their device. For example, if a user uses this system in a real-world setting and it is determined that they are in a stressed emotional state, they may be immediately offered relaxation-related benefits through a virtual vision device.

[0201] As a concrete example, imagine a scenario where a user who appears tired in a shopping mall has their emotional state analyzed in real time through a virtual vision device, and is offered a discount for using a special rest area.

[0202] The generation AI model can be improved in terms of sentiment analysis and reward suggestions by inputting prompts such as the following:

[0203] "We are seeking support in designing a system that analyzes user facial expression data acquired from smart devices to identify their current emotional state, and then provides appropriate services and benefits in real time."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server receives user behavior data and stores it in a database. This input data includes the user's past purchase history and behavioral patterns. Based on this, a behavioral analysis algorithm is used to process the data and output it as user characteristic data.

[0207] Step 2:

[0208] The virtual vision device, which acts as the terminal, detects the user's facial expressions and movements in real time. It acquires image and video data as input and processes them using an emotion analysis engine. To obtain an output representing the emotional state, it uses an image processing library (e.g., OpenCV) to estimate the user's current emotional state.

[0209] Step 3:

[0210] The server determines individually appropriate tasks and rewards by referencing user characteristic data based on the emotional state transmitted from the terminal. It uses machine learning techniques to perform data calculations and output the content of the reward suggestions. This process utilizes algorithms for personalized suggestions.

[0211] Step 4:

[0212] The user receives reward suggestions sent from the server via their device. Input includes details such as the reward's content and expiration date, and based on this, the user initiates actions to utilize the reward in real time. The reward is applied based on the user's selection, and the feedback is sent back to the server as data for the next analysis cycle.

[0213] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0220] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0224] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] The system of the present invention provides an effective means for promoting user behavior. The system mainly consists of three elements: a server, a terminal, and a user.

[0230] The server plays a central role, responsible for generating user profiles. The server first collects user behavior data and analyzes it using AI. Based on the analysis, it generates a user profile and automatically sets optimal tasks and rewards accordingly. The server then transmits this information to the terminal and notifies the user.

[0231] The terminal functions as an interface with the user. The terminal presents the user with tasks received from the server and records the user's actions. When the user completes a task, the terminal sends progress data to the server and, in cooperation with the server, immediately processes the awarding of reward points.

[0232] Users interact with the system through their devices. Users plan and execute actions to achieve the tasks presented to them. For example, if the goal is to improve health, users will work on a daily step count challenge. The system rewards users with points each time they complete a task, giving them a sense of accomplishment.

[0233] Furthermore, the server designs login bonuses and retention rewards to support continued user engagement. The device prompts users for these rewards and provides elements that encourage continued use.

[0234] As a concrete example, a fitness app might be designed to reward users with special points if they achieve their step goal for a week straight. This can motivate users and encourage long-term use. This system allows investors to manage user behavior efficiently and at low cost, and provide appropriate incentives.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server periodically receives user behavior data from the device. This data includes, for example, the user's daily step count and app usage history. The server inputs this data into an AI system to analyze the user's behavior patterns.

[0238] Step 2:

[0239] The server generates user profiles based on the analyzed data. These profiles include information about the user's preferences and past behavioral characteristics. Based on these profiles, the server uses AI to automatically set the optimal tasks and rewards for each user.

[0240] Step 3:

[0241] The server sends information about the set task and reward to the device. The device then notifies the user of this information and presents specific behavioral goals. For example, a task such as walking 10,000 steps a day might be displayed.

[0242] Step 4:

[0243] Users work on tasks presented by the server through their devices. Through their daily activities, users take actions to complete these tasks. The device records the user's activities and continuously transmits progress data to the server.

[0244] Step 5:

[0245] The server analyzes the received progress data to confirm whether the user has completed the assigned task. Once completion is confirmed, the server immediately calculates the reward points and sends that information to the terminal.

[0246] Step 6:

[0247] The terminal notifies the user of the reward points received from the server. This allows the user to gain a sense of accomplishment and increase their motivation for the next challenge.

[0248] Step 7:

[0249] The server plans login bonuses and retention rewards to maintain user motivation and sends this information to the device. The device then presents this reward information to the user to encourage further use.

[0250] (Example 1)

[0251] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] Conventional systems often lack sufficient individualization in promoting user behavior, making it difficult to maintain user motivation over the long term. Furthermore, rewards and tasks are frequently set manually, limiting their effectiveness in facilitating behavioral change.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for a data processing device to acquire user activity information and analyze activity history to form a user profile; means for automatically setting tasks and rewards suitable for the user using artificial intelligence based on the profile; and means for monitoring the achievement level of the tasks and assigning rewards upon completion. This enables the automatic setting of individualized tasks and rewards, and efficiently supports the sustained motivation of the user.

[0255] A "data processing device" is a device that can acquire and analyze information about users' behavior and actions.

[0256] "Action information" refers to data about the user's behavior and activities, and is fundamental information used to analyze behavioral patterns.

[0257] A "user profile" is personalized information obtained by analyzing a user's behavioral history, and is used for setting tasks and providing rewards.

[0258] "Artificial intelligence" is a technology that analyzes large amounts of data and automatically sets tasks and rewards tailored to the user, and it includes a variety of algorithms.

[0259] A "challenge" is a goal or challenge that users should aim to achieve, and it is set to encourage action.

[0260] "Rewards" are incentives given to users upon completing tasks, and they play a role in improving motivation.

[0261] "Achievement level" is an indicator that shows the extent to which a user has achieved the assigned tasks, and it serves as a criterion for determining whether to award rewards.

[0262] "Connection benefits" are bonuses offered to users who regularly access the system, encouraging continued use.

[0263] A "generative AI model" is an artificial intelligence model used to set tasks and rewards that are adapted to the user, enabling efficient data analysis.

[0264] This invention is a system that facilitates user behavior and supports sustained engagement. It mainly consists of three elements: a server, a terminal, and the user.

[0265] The server plays a central role in the system, acquiring user activity information. This information is collected from hardware such as smartphones and wearable devices. The server analyzes the collected information using AI analysis tools such as TensorFlow and PyTorch. Based on the analysis results, it forms a user profile and has the function to automatically set tasks and rewards appropriate to that profile using a generative AI model.

[0266] The terminal functions as an interface device with the user. It notifies the user of tasks received from the server and records their progress. When the user completes a task, the terminal sends progress data to the server, supporting the process of immediately awarding reward points.

[0267] Users tackle challenges through their devices. For example, if the goal is health, users aim to achieve their daily step count target. The system rewards users with points or badges each time they achieve their goal. This helps users stay motivated and manage their own progress.

[0268] For example, fitness apps are designed to reward users with special points for achieving their step goal for a week straight. Such incentives effectively encourage user behavior.

[0269] An example of a prompt might be the text, "Suggest an appropriate reward to give when the user achieves their exercise goal." This prompt is input into the generative AI model and serves as the basis for guiding it to set the optimal reward.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The server acquires user activity information from smartphones and wearable devices. It receives raw data transmitted from each device as input (e.g., steps, heart rate, location information, etc.). The server stores the received data in a database and organizes it for the next analysis step. The output is a set of organized behavioral data.

[0273] Step 2:

[0274] The server analyzes the acquired behavioral information using AI analysis tools such as TensorFlow and PyTorch. This analysis identifies behavioral patterns and extracts user trends. The data organized in step 1 is used as input, and this results in the output of a user profile. The profile includes estimates of the current health status and activity level.

[0275] Step 3:

[0276] The server uses a generative AI model to set optimal tasks and rewards based on the user profile. The input is the user profile created in step 2, and based on this, the AI ​​determines what kind of tasks will motivate the user. The output is a customized task and reward plan for each user.

[0277] Step 4:

[0278] The server sends the generated task and reward information to the device. This input is the customized plan output in step 3. The output to the device includes task notification data and reward information. This allows the user to check the new task on their device.

[0279] Step 5:

[0280] The device notifies the user of tasks received from the server. Specifically, it executes push notifications and in-app messages to allow the user to check the tasks immediately. The device also prepares to record the user's actions. The output is the task information displayed to the user.

[0281] Step 6:

[0282] Users work on tasks using the device. Specifically, they perform tasks related to exercise goals and health management as presented, and the device automatically records this information. The input consists of the task content provided via the device, and daily activities are carried out based on this information.

[0283] Step 7:

[0284] The terminal records the user's task achievement status and transmits progress data to the server at regular time intervals. The input is the log of the user's activities, and based on this, the output obtained by the server is the latest progress information. Based on this information, preparations are made for the next step to be executed.

[0285] Step 8:

[0286] The server analyzes the transmitted progress data and immediately calculates and awards reward points if the task is completed. The input is the progress data obtained in Step 7, and the output is the reward information reflected in the user account. Through this process, the user's motivation is maintained.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] The purpose of the present invention is to provide a method of motivating consumers' behavior in order to make consumers enjoy the experience in physical stores even more and promote store visits and purchases. In conventional systems, there was a problem that the means of enhancing consumers' motivation was limited and it was difficult to maintain long-term engagement.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate a user profile; means for automatically setting appropriate tasks and rewards for the user using AI based on the profile; and means for generating specific behavioral tasks to motivate the user's real-world actions and notifying the user via a smart device. This allows consumers to receive personalized challenges and immediate rewards through real-world actions, thereby enhancing the in-store experience and promoting continuous engagement.

[0292] "User behavior data" refers to information about various behavioral histories generated by users, including, for example, the number of store visits and product scanning history.

[0293] A "user profile" is a collection of information that indicates the preferences and behavioral patterns of individual users, generated from the analysis of behavioral data.

[0294] "AI" refers to artificial intelligence technology that analyzes user data to determine the optimal tasks and rewards.

[0295] A "challenge" is a specific action or challenge presented to the user, designed to encourage actions to be taken within the physical store.

[0296] "Reward points" are incentives that users earn by completing tasks, and are provided in the form of coupons, points, etc., that can be used on their next visit or purchase.

[0297] A "login bonus" is a reward that users receive for regularly using the system, and it is a factor that supports continued user engagement.

[0298] "Continued use benefits" are additional incentives that users receive for continuing to use the system, and are designed to encourage continued use.

[0299] "Real-world actions" refer to activities that users perform in actual physical environments, including trying on or testing products in stores.

[0300] A "smart device" is a terminal device used by a user, such as a smartphone or smart glasses, that has the function of notifying or recording information.

[0301] This system is designed to encourage user behavior and enrich the in-store experience. The server acts as the system's core, generating profiles using behavioral data collected from users. This involves the server analyzing the collected data using AI models, such as TensorFlow or PyTorch. Based on these profiles, optimal behavioral tasks and rewards are automatically set.

[0302] The server also generates specific tasks to motivate users to act in the real world and sends them to their devices. These devices are smart devices such as smartphones and smart glasses, and task notifications are sent to the user through these devices. The tasks are tailored to the user and may include tasks such as trying on new products or purchasing specific promotional items.

[0303] When users complete these tasks, action data is sent from their device to the server. The server processes the data in real time and verifies task completion. After verification, the server immediately calculates reward points and reflects them in the user's account. This allows users to receive immediate feedback through their in-store experience, providing them with a greater sense of incentive.

[0304] To give a concrete example, suppose a user visits a fashion brand's store using a smartphone app and takes on a task instructed by the app to "try on three new items." Once the user completes this task, the system immediately issues a special coupon and notifies them that it can be used for purchases in the store.

[0305] As an example of a prompt sentence for the generation AI model, a sentence such as "Please design an AI model that sets optimal tasks and rewards for promoting users' in-store behavior." is used. In this way, the system provides a mechanism for maximizing individual consumer experiences.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The server collects the past behavior data of the user. The input is the behavior history obtained via the terminal, and the output is the accumulation of this behavior data. The server stores the data in a database and uses it for analysis.

[0309] Step 2:

[0310] The server analyzes the collected behavior data using the generation AI model. The input is the behavior data obtained in Step 1, and the output is a profile for each user. This profile includes analysis results reflecting behavior tendencies and preferences. The AI model applies an algorithm based on the data to find trends.

[0311] Step 3:

[0312] The server sets tasks suitable for the user based on the generated user profile. The input is the profile data, and the output is the appropriate task content. Here, products and experiences highly relevant to the user are selected.

[0313] Step 4:

[0314] The terminal notifies the user of the task sent from the server. The input is the task content, and the output is the notification received by the user. The smart device presents a specific task to the user and prompts action.

[0315] Step 5:

[0316] The user completes tasks notified via a terminal. The input is the presented task, and the output is the result of its execution. The user completes tasks such as trying on products or performing specific actions and reports them to the system.

[0317] Step 6:

[0318] The device sends the user's actions to the server. The input is the result of the user's task completion, and the output is the data sent to the server. The smart device sends the action data back to the server in real time.

[0319] Step 7:

[0320] The server reviews the submitted execution data and checks if the task has been completed. The input is the execution data, and the output is the completion status. Once completion is confirmed, the reward is prepared immediately.

[0321] Step 8:

[0322] The server awards reward points to users based on their completion status. The input is the completion status, and the output is the reflection of the reward in the user's account. The system adds points or coupons to the user's account.

[0323] Through this entire process, the system can motivate user behavior, enrich the in-store experience, and foster long-term engagement.

[0324] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0325] This invention is a system that analyzes user behavior data and combines it with an emotion engine to set tasks and rewards that are more suitable for individual users. The system mainly consists of a server, a terminal, an emotion engine, and a user.

[0326] The server handles central processing, receiving user behavior data transmitted from terminals. The server analyzes this data using AI to generate user profiles. Based on these profiles, the server sets optimal tasks and rewards. In this process, the emotion engine plays a role in analyzing data to estimate the user's emotional state.

[0327] The emotion engine is designed to estimate changes in emotional state from user behavior data. For example, if it is estimated that a user experienced stress in their daily activities, the emotion engine provides the server with information to appropriately adjust the reward content and task difficulty.

[0328] The device is responsible for presenting the user with tasks and reward information sent from the server. It also sends the user's emotional state and behavioral data back to the server in real time to receive feedback. When the user completes a task, the device notifies them of an evaluation result tailored to their emotional state. Furthermore, the device provides motivational feedback adjusted based on the user's emotional state.

[0329] Users take on challenges presented through their devices and aim to achieve them. For example, when a user works towards a goal of walking 10,000 steps daily using a health app, the emotion engine evaluates the stress and satisfaction experienced during the process and adjusts rewards to help them achieve their goal. In this way, the server, device, and emotion engine work together to provide users with a personalized experience and promote long-term engagement.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] Users use their devices to perform everyday activities and record their activity data (e.g., steps taken, heart rate, app usage). The devices then send this data to a server.

[0333] Step 2:

[0334] The server analyzes the received behavioral data and uses AI to generate a user profile. This profile reflects the user's behavioral patterns and preferences.

[0335] Step 3:

[0336] The server sets the most suitable tasks and rewards for the user based on the generated profile. This setting also takes into account the results of the emotion engine's analysis. The emotion engine provides emotional states estimated from the user's behavioral data.

[0337] Step 4:

[0338] The emotion engine analyzes the user's emotional state (e.g., stress, satisfaction) from behavioral data and provides it to the server. The server uses this information to adjust the difficulty of the tasks and the content of the rewards.

[0339] Step 5:

[0340] The server sends information about the adjusted tasks and rewards to the device. The device notifies the user of this information and presents specific tasks. For example, it might display something like, "Earn a special bonus if you walk 10,000 steps today."

[0341] Step 6:

[0342] Users work on tasks and monitor their progress through their devices. These devices continuously send this progress data to the server.

[0343] Step 7:

[0344] The server analyzes the submitted progress data to determine whether the user has completed the task. If completion is confirmed, the server calculates reward points and sends them to the device.

[0345] Step 8:

[0346] The device notifies the user of reward information received from the server. The user feels a sense of accomplishment and gains motivation for the next task. The device also sends follow-up notifications to the user based on feedback from the emotion engine (for example, a message such as "Your recent efforts have been recognized").

[0347] Step 9:

[0348] The device displays information about regular login bonuses and continuous user rewards to the user, maintaining long-term motivation. The server updates this reward information as needed and delivers it to the device.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] Traditionally, providing individually optimized experiences based on user behavior and emotional states has been challenging. In particular, there is a need for effective methods to operate dynamically adjustable task settings and reward systems to sustain user motivation and maintain long-term engagement.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for acquiring user behavior data and analyzing the behavior history to create a user profile; means for automatically setting optimal goals and rewards for the user using generative AI technology; and means for estimating the user's emotional state using an emotion analysis engine and dynamically adjusting the goals and rewards. This makes it possible to provide individually optimized tasks and rewards to users, thereby maintaining motivation and improving engagement.

[0354] "Action data" refers to data collected from information related to the user's activities and behaviors.

[0355] "Activity history" refers to a record of a user's activities over time and is used to analyze the user's behavioral patterns.

[0356] A "user profile" is a dataset that represents the individual characteristics and behavioral tendencies of a user, generated based on their behavioral data and history.

[0357] "Generative AI technology" is a technology that utilizes artificial intelligence models to generate optimal results based on user profiles and other input data.

[0358] "Goals" refer to specific challenges or tasks that users should achieve.

[0359] "Reward" refers to the benefits or incentives that users receive when they achieve their goals.

[0360] An "emotion analysis engine" is software or a system that estimates a user's emotional state from their behavioral data and adjusts the information and results accordingly.

[0361] "Motivation" refers to providing users with stimuli or reasons to continue a particular behavior.

[0362] The embodiments for carrying out this invention will be described mainly by dividing them into three roles: server, terminal, and user.

[0363] The server is responsible for central data processing. First, it receives user behavior data transmitted from the terminal. This behavior data includes information such as the user's activities and operation history. Next, the server uses generative AI technology to analyze this behavior data and generate a user profile. This profile facilitates personalized goal setting and reward design based on behavioral patterns extracted from the behavior history. At this time, an emotion analysis engine is used to estimate the user's emotional state, enabling dynamic and flexible adjustment of tasks and rewards.

[0364] The device presents the user with tasks and reward information sent from the server. Specifically, the device displays appropriate feedback and motivational messages to the user. It also plays a role in acquiring the user's latest behavioral data and sending it back to the server in real time. This allows the server to continuously update the user profile based on the latest data.

[0365] Users take on challenges presented via their devices and aim to achieve their goals. For example, in a health app, if a user aims to walk 10,000 steps every day, the emotion analysis engine evaluates the user's stress and satisfaction during the process and adjusts rewards to support goal achievement.

[0366] An example of a prompt message is: "Based on the user's daily step count data in the health app, and considering the stress level analyzed by the emotion engine, design the optimal reward scenario."

[0367] This system aims to provide users with personalized and optimal challenges and rewards by having servers, terminals, and an emotion engine work together, thereby maintaining user motivation and promoting long-term engagement.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The device collects user activity data through sensors and input interfaces. Inputs include step count and app usage history. The raw data is sent to a server as output. Specifically, the device periodically collects data and transfers it to the server via the network.

[0371] Step 2:

[0372] The server receives behavioral data from the terminal and analyzes it using a generating AI model. The input is behavioral data sent from the terminal. Data processing involves analyzing behavioral patterns and generating a user profile. The output is the generated user profile. Specifically, the server uses algorithms to perform data transformation and pattern identification.

[0373] Step 3:

[0374] The server uses an emotion analysis engine to estimate the user's emotional state. Inputs include the user profile and recent behavioral data. Data calculations involve determining emotional indices to estimate the user's stress level and satisfaction level. The output is the user's emotional state. Specifically, multiple emotional indices are aggregated, and the evaluation results are analyzed.

[0375] Step 4:

[0376] The server uses generative AI technology to automatically set the optimal tasks and rewards for each user. Inputs include user profiles and emotional states. Data calculations are performed to design tasks and rewards based on this information. The output is customized tasks and rewards for each user. Specifically, a predictive model is used to simulate multiple scenarios and derive the optimal solution.

[0377] Step 5:

[0378] The device presents the user with task and reward information sent from the server. Inputs include task data and reward data from the server. Output is presented to the user as visual or audible feedback. Specifically, the device uses a notification function to inform the user of new tasks and rewards.

[0379] Step 6:

[0380] Users work on the assigned tasks and input the results into a terminal. Input includes the user's activity results and feedback. Output is that data sent back to the server via the terminal. Specifically, users operate the application screen to perform self-reporting and automated measurements.

[0381] Step 7:

[0382] The server continuously optimizes the entire system based on user feedback. Inputs include the user's latest behavioral data and feedback. Data processing involves continuous analysis and learning to update user profiles. Outputs are used to inform future task settings and reward designs. Specifically, the server forms a feedback loop to improve the accuracy of the AI ​​model.

[0383] (Application Example 2)

[0384] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0385] In today's consumer society, providing services that meet user needs and emotions is a crucial challenge. In particular, improving the user experience in physical spaces requires understanding users' real-time emotional states and providing personalized benefits and services immediately. However, conventional systems have struggled to effectively analyze users' emotional states and appropriately propose personalized benefits based on that analysis.

[0386] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0387] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate user characteristic data; means for automatically setting tasks and rewards suitable for the user using machine learning technology; and means for estimating the user's emotional state in real space via a virtual vision device. This enables immediate reward suggestions based on the user's emotional state.

[0388] "User characteristic data" refers to data that shows attribute information unique to individual users, obtained by analyzing the user's behavioral history.

[0389] "Machine learning technology" refers to algorithms and methods for automatically discovering patterns and rules by analyzing large amounts of data, and for predicting or classifying new information.

[0390] A "virtual vision device" is a device that can overlay digital information onto a real-world environment and is used to assist in the presentation and manipulation of information.

[0391] "Emotional state" refers to the user's psychological and emotional state at any given time, and is primarily estimated by analyzing data obtained from external sources.

[0392] "Proposing special benefits" is the process of proposing special services or rewards to users based on their behavior and emotional state.

[0393] This invention utilizes a server, a virtual vision device, and an emotion analysis engine to implement a system that appropriately provides benefits based on the emotional state of the user. The server is responsible for collecting user behavior data, analyzing its history using behavior analysis algorithms, and generating user characteristic data. Database software and machine learning frameworks (e.g., TensorFlow) are used in this process.

[0394] The virtual vision device, acting as the terminal, acquires the user's facial expressions and movements in real time and processes the data with an emotion analysis engine to estimate their emotional state. Image processing libraries (e.g., OpenCV) can be used for this process. Based on this emotional state, the server then sets user-specific tasks and proposes corresponding rewards.

[0395] Users receive the suggested benefits via their device. For example, if a user uses this system in a real-world setting and it is determined that they are in a stressed emotional state, they may be immediately offered relaxation-related benefits through a virtual vision device.

[0396] As a concrete example, imagine a scenario where a user who appears tired in a shopping mall has their emotional state analyzed in real time through a virtual vision device, and is offered a discount for using a special rest area.

[0397] The generation AI model can be improved in terms of sentiment analysis and reward suggestions by inputting prompts such as the following:

[0398] "We are seeking support in designing a system that analyzes user facial expression data acquired from smart devices to identify their current emotional state, and then provides appropriate services and benefits in real time."

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The server receives user behavior data and stores it in a database. This input data includes the user's past purchase history and behavioral patterns. Based on this, a behavioral analysis algorithm is used to process the data and output it as user characteristic data.

[0402] Step 2:

[0403] The virtual vision device, which acts as the terminal, detects the user's facial expressions and movements in real time. It acquires image and video data as input and processes them using an emotion analysis engine. To obtain an output representing the emotional state, it uses an image processing library (e.g., OpenCV) to estimate the user's current emotional state.

[0404] Step 3:

[0405] The server determines individually appropriate tasks and rewards by referencing user characteristic data based on the emotional state transmitted from the terminal. It uses machine learning techniques to perform data calculations and output the content of the reward suggestions. This process utilizes algorithms for personalized suggestions.

[0406] Step 4:

[0407] The user receives reward suggestions sent from the server via their device. Input includes details such as the reward's content and expiration date, and based on this, the user initiates actions to utilize the reward in real time. The reward is applied based on the user's selection, and the feedback is sent back to the server as data for the next analysis cycle.

[0408] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0409] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0414] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0416] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0418] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0419] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0420] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0421] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0423] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0424] The system of the present invention provides an effective means for promoting user behavior. The system mainly consists of three elements: a server, a terminal, and a user.

[0425] The server plays a central role, responsible for generating user profiles. The server first collects user behavior data and analyzes it using AI. Based on the analysis, it generates a user profile and automatically sets optimal tasks and rewards accordingly. The server then transmits this information to the terminal and notifies the user.

[0426] The terminal functions as an interface with the user. The terminal presents the user with tasks received from the server and records the user's actions. When the user completes a task, the terminal sends progress data to the server and, in cooperation with the server, immediately processes the awarding of reward points.

[0427] Users interact with the system through their devices. Users plan and execute actions to achieve the tasks presented to them. For example, if the goal is to improve health, users will work on a daily step count challenge. The system rewards users with points each time they complete a task, giving them a sense of accomplishment.

[0428] Furthermore, the server designs login bonuses and retention rewards to support continued user engagement. The device prompts users for these rewards and provides elements that encourage continued use.

[0429] As a concrete example, a fitness app might be designed to reward users with special points if they achieve their step goal for a week straight. This can motivate users and encourage long-term use. This system allows investors to manage user behavior efficiently and at low cost, and provide appropriate incentives.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The server periodically receives user behavior data from the device. This data includes, for example, the user's daily step count and app usage history. The server inputs this data into an AI system to analyze the user's behavior patterns.

[0433] Step 2:

[0434] The server generates user profiles based on the analyzed data. These profiles include information about the user's preferences and past behavioral characteristics. Based on these profiles, the server uses AI to automatically set the optimal tasks and rewards for each user.

[0435] Step 3:

[0436] The server sends information about the set task and reward to the device. The device then notifies the user of this information and presents specific behavioral goals. For example, a task such as walking 10,000 steps a day might be displayed.

[0437] Step 4:

[0438] Users work on tasks presented by the server through their devices. Through their daily activities, users take actions to complete these tasks. The device records the user's activities and continuously transmits progress data to the server.

[0439] Step 5:

[0440] The server analyzes the received progress data to confirm whether the user has completed the assigned task. Once completion is confirmed, the server immediately calculates the reward points and sends that information to the terminal.

[0441] Step 6:

[0442] The terminal notifies the user of the reward points received from the server. This allows the user to gain a sense of accomplishment and increase their motivation for the next challenge.

[0443] Step 7:

[0444] The server plans login bonuses and retention rewards to maintain user motivation and sends this information to the device. The device then presents this reward information to the user to encourage further use.

[0445] (Example 1)

[0446] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] Conventional systems often lack sufficient individualization in promoting user behavior, making it difficult to maintain user motivation over the long term. Furthermore, rewards and tasks are frequently set manually, limiting their effectiveness in facilitating behavioral change.

[0448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0449] In this invention, the server includes means for a data processing device to acquire user activity information and analyze activity history to form a user profile; means for automatically setting tasks and rewards suitable for the user using artificial intelligence based on the profile; and means for monitoring the achievement level of the tasks and assigning rewards upon completion. This enables the automatic setting of individualized tasks and rewards, and efficiently supports the sustained motivation of the user.

[0450] A "data processing device" is a device that can acquire and analyze information about users' behavior and actions.

[0451] "Action information" refers to data about the user's behavior and activities, and is fundamental information used to analyze behavioral patterns.

[0452] A "user profile" is personalized information obtained by analyzing a user's behavioral history, and is used for setting tasks and providing rewards.

[0453] "Artificial intelligence" is a technology that analyzes large amounts of data and automatically sets tasks and rewards tailored to the user, and it includes a variety of algorithms.

[0454] A "challenge" is a goal or challenge that users should aim to achieve, and it is set to encourage action.

[0455] "Rewards" are incentives given to users upon completing tasks, and they play a role in improving motivation.

[0456] "Achievement level" is an indicator that shows the extent to which a user has achieved the assigned tasks, and it serves as a criterion for determining whether to award rewards.

[0457] "Connection benefits" are bonuses offered to users who regularly access the system, encouraging continued use.

[0458] A "generative AI model" is an artificial intelligence model used to set tasks and rewards that are adapted to the user, enabling efficient data analysis.

[0459] This invention is a system that facilitates user behavior and supports sustained engagement. It mainly consists of three elements: a server, a terminal, and the user.

[0460] The server plays a central role in the system, acquiring user activity information. This information is collected from hardware such as smartphones and wearable devices. The server analyzes the collected information using AI analysis tools such as TensorFlow and PyTorch. Based on the analysis results, it forms a user profile and has the function to automatically set tasks and rewards appropriate to that profile using a generative AI model.

[0461] The terminal functions as an interface device with the user. It notifies the user of tasks received from the server and records their progress. When the user completes a task, the terminal sends progress data to the server, supporting the process of immediately awarding reward points.

[0462] Users tackle challenges through their devices. For example, if the goal is health, users aim to achieve their daily step count target. The system rewards users with points or badges each time they achieve their goal. This helps users stay motivated and manage their own progress.

[0463] For example, fitness apps are designed to reward users with special points for achieving their step goal for a week straight. Such incentives effectively encourage user behavior.

[0464] An example of a prompt might be the text, "Suggest an appropriate reward to give when the user achieves their exercise goal." This prompt is input into the generative AI model and serves as the basis for guiding it to set the optimal reward.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] The server acquires user activity information from smartphones and wearable devices. It receives raw data transmitted from each device as input (e.g., steps, heart rate, location information, etc.). The server stores the received data in a database and organizes it for the next analysis step. The output is a set of organized behavioral data.

[0468] Step 2:

[0469] The server analyzes the acquired behavioral information using AI analysis tools such as TensorFlow and PyTorch. This analysis identifies behavioral patterns and extracts user trends. The data organized in step 1 is used as input, and this results in the output of a user profile. The profile includes estimates of the current health status and activity level.

[0470] Step 3:

[0471] The server uses a generative AI model to set optimal tasks and rewards based on the user profile. The input is the user profile created in step 2, and based on this, the AI ​​determines what kind of tasks will motivate the user. The output is a customized task and reward plan for each user.

[0472] Step 4:

[0473] The server sends the generated task and reward information to the device. This input is the customized plan output in step 3. The output to the device includes task notification data and reward information. This allows the user to check the new task on their device.

[0474] Step 5:

[0475] The device notifies the user of tasks received from the server. Specifically, it executes push notifications and in-app messages to allow the user to check the tasks immediately. The device also prepares to record the user's actions. The output is the task information displayed to the user.

[0476] Step 6:

[0477] Users work on tasks using the device. Specifically, they perform tasks related to exercise goals and health management as presented, and the device automatically records this information. The input consists of the task content provided via the device, and daily activities are carried out based on this information.

[0478] Step 7:

[0479] The terminal records the user's task completion status and sends progress data to the server at regular intervals. The input is a log of the user's activities, and the output received by the server is the latest progress information. Based on this information, preparations are made for the next step to be executed.

[0480] Step 8:

[0481] The server analyzes the received progress data and immediately calculates and awards reward points upon completion of the task. The input is the progress data obtained in step 7, and the output is the reward information reflected in the user's account. This process helps maintain user motivation.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] The present invention aims to provide a method for motivating consumer behavior in order to enhance the in-store experience for consumers and encourage store visits and purchases. Conventional systems have limited means of increasing consumer motivation, making it difficult to maintain long-term engagement.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate a user profile; means for automatically setting appropriate tasks and rewards for the user using AI based on the profile; and means for generating specific behavioral tasks to motivate the user's real-world actions and notifying the user via a smart device. This allows consumers to receive personalized challenges and immediate rewards through real-world actions, thereby enhancing the in-store experience and promoting continuous engagement.

[0487] "User behavior data" refers to information about various behavioral histories generated by users, including, for example, the number of store visits and product scanning history.

[0488] A "user profile" is a collection of information that indicates the preferences and behavioral patterns of individual users, generated from the analysis of behavioral data.

[0489] "AI" refers to artificial intelligence technology that analyzes user data to determine the optimal tasks and rewards.

[0490] A "challenge" is a specific action or challenge presented to the user, designed to encourage actions to be taken within the physical store.

[0491] "Reward points" are incentives that users earn by completing tasks, and are provided in the form of coupons, points, etc., that can be used on their next visit or purchase.

[0492] A "login bonus" is a reward that users receive for regularly using the system, and it is a factor that supports continued user engagement.

[0493] "Continued use benefits" are additional incentives that users receive for continuing to use the system, and are designed to encourage continued use.

[0494] "Real-world actions" refer to activities that users perform in actual physical environments, including trying on or testing products in stores.

[0495] A "smart device" is a terminal device used by a user, such as a smartphone or smart glasses, that has the function of notifying or recording information.

[0496] This system is designed to encourage user behavior and enrich the in-store experience. The server acts as the system's core, generating profiles using behavioral data collected from users. This involves the server analyzing the collected data using AI models, such as TensorFlow or PyTorch. Based on these profiles, optimal behavioral tasks and rewards are automatically set.

[0497] The server also generates specific tasks to motivate users to act in the real world and sends them to their devices. These devices are smart devices such as smartphones and smart glasses, and task notifications are sent to the user through these devices. The tasks are tailored to the user and may include tasks such as trying on new products or purchasing specific promotional items.

[0498] When users complete these tasks, action data is sent from their device to the server. The server processes the data in real time and verifies task completion. After verification, the server immediately calculates reward points and reflects them in the user's account. This allows users to receive immediate feedback through their in-store experience, providing them with a greater sense of incentive.

[0499] To give a concrete example, suppose a user visits a fashion brand's store using a smartphone app and takes on a task instructed by the app to "try on three new items." Once the user completes this task, the system immediately issues a special coupon and notifies them that it can be used for purchases in the store.

[0500] An example of a prompt for a generative AI model would be, "Design an AI model that sets optimal challenges and rewards to encourage user behavior in-store." In this way, the system provides a mechanism to maximize the individual consumer experience.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The server collects data on the user's past behavior. The input is the behavioral history obtained via the terminal, and the output is an aggregate of this behavioral data. The server stores the data in a database and uses it for analysis.

[0504] Step 2:

[0505] The server analyzes the collected behavioral data using a generating AI model. The input is the behavioral data obtained in step 1, and the output is a profile for each user. This profile includes analysis results that reflect behavioral tendencies and preferences. The AI ​​model applies algorithms based on the data to identify trends.

[0506] Step 3:

[0507] The server sets tasks appropriate for the user based on the generated user profile. The input is profile data, and the output is appropriate task content. Here, products and experiences highly relevant to the user are selected.

[0508] Step 4:

[0509] The device notifies the user of tasks sent from the server. The input is the task content, and the output is the notification the user receives. The smart device presents the user with a specific task and prompts them to take action.

[0510] Step 5:

[0511] The user completes tasks notified via a terminal. The input is the presented task, and the output is the result of its execution. The user completes tasks such as trying on products or performing specific actions and reports them to the system.

[0512] Step 6:

[0513] The device sends the user's actions to the server. The input is the result of the user's task completion, and the output is the data sent to the server. The smart device sends the action data back to the server in real time.

[0514] Step 7:

[0515] The server reviews the submitted execution data and checks if the task has been completed. The input is the execution data, and the output is the completion status. Once completion is confirmed, the reward is prepared immediately.

[0516] Step 8:

[0517] The server awards reward points to users based on their completion status. The input is the completion status, and the output is the reflection of the reward in the user's account. The system adds points or coupons to the user's account.

[0518] Through this entire process, the system can motivate user behavior, enrich the in-store experience, and foster long-term engagement.

[0519] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0520] This invention is a system that analyzes user behavior data and combines it with an emotion engine to set tasks and rewards that are more suitable for individual users. The system mainly consists of a server, a terminal, an emotion engine, and a user.

[0521] The server handles central processing, receiving user behavior data transmitted from terminals. The server analyzes this data using AI to generate user profiles. Based on these profiles, the server sets optimal tasks and rewards. In this process, the emotion engine plays a role in analyzing data to estimate the user's emotional state.

[0522] The emotion engine is designed to estimate changes in emotional state from user behavior data. For example, if it is estimated that a user experienced stress in their daily activities, the emotion engine provides the server with information to appropriately adjust the reward content and task difficulty.

[0523] The device is responsible for presenting the user with tasks and reward information sent from the server. It also sends the user's emotional state and behavioral data back to the server in real time to receive feedback. When the user completes a task, the device notifies them of an evaluation result tailored to their emotional state. Furthermore, the device provides motivational feedback adjusted based on the user's emotional state.

[0524] Users take on challenges presented through their devices and aim to achieve them. For example, when a user works towards a goal of walking 10,000 steps daily using a health app, the emotion engine evaluates the stress and satisfaction experienced during the process and adjusts rewards to help them achieve their goal. In this way, the server, device, and emotion engine work together to provide users with a personalized experience and promote long-term engagement.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] Users use their devices to perform everyday activities and record their activity data (e.g., steps taken, heart rate, app usage). The devices then send this data to a server.

[0528] Step 2:

[0529] The server analyzes the received behavioral data and uses AI to generate a user profile. This profile reflects the user's behavioral patterns and preferences.

[0530] Step 3:

[0531] The server sets the most suitable tasks and rewards for the user based on the generated profile. This setting also takes into account the results of the emotion engine's analysis. The emotion engine provides emotional states estimated from the user's behavioral data.

[0532] Step 4:

[0533] The emotion engine analyzes the user's emotional state (e.g., stress, satisfaction) from behavioral data and provides it to the server. The server uses this information to adjust the difficulty of the tasks and the content of the rewards.

[0534] Step 5:

[0535] The server sends information about the adjusted tasks and rewards to the device. The device notifies the user of this information and presents specific tasks. For example, it might display something like, "Earn a special bonus if you walk 10,000 steps today."

[0536] Step 6:

[0537] Users work on tasks and monitor their progress through their devices. These devices continuously send this progress data to the server.

[0538] Step 7:

[0539] The server analyzes the submitted progress data to determine whether the user has completed the task. If completion is confirmed, the server calculates reward points and sends them to the device.

[0540] Step 8:

[0541] The device notifies the user of reward information received from the server. The user feels a sense of accomplishment and gains motivation for the next task. The device also sends follow-up notifications to the user based on feedback from the emotion engine (for example, a message such as "Your recent efforts have been recognized").

[0542] Step 9:

[0543] The device displays information about regular login bonuses and continuous user rewards to the user, maintaining long-term motivation. The server updates this reward information as needed and delivers it to the device.

[0544] (Example 2)

[0545] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] Traditionally, providing individually optimized experiences based on user behavior and emotional states has been challenging. In particular, there is a need for effective methods to operate dynamically adjustable task settings and reward systems to sustain user motivation and maintain long-term engagement.

[0547] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0548] In this invention, the server includes means for acquiring user behavior data and analyzing the behavior history to create a user profile; means for automatically setting optimal goals and rewards for the user using generative AI technology; and means for estimating the user's emotional state using an emotion analysis engine and dynamically adjusting the goals and rewards. This makes it possible to provide individually optimized tasks and rewards to users, thereby maintaining motivation and improving engagement.

[0549] "Action data" refers to data collected from information related to the user's activities and behaviors.

[0550] "Activity history" refers to a record of a user's activities over time and is used to analyze the user's behavioral patterns.

[0551] A "user profile" is a dataset that represents the individual characteristics and behavioral tendencies of a user, generated based on their behavioral data and history.

[0552] "Generative AI technology" is a technology that utilizes artificial intelligence models to generate optimal results based on user profiles and other input data.

[0553] "Goals" refer to specific challenges or tasks that users should achieve.

[0554] "Reward" refers to the benefits or incentives that users receive when they achieve their goals.

[0555] An "emotion analysis engine" is software or a system that estimates a user's emotional state from their behavioral data and adjusts the information and results accordingly.

[0556] "Motivation" refers to providing users with stimuli or reasons to continue a particular behavior.

[0557] The embodiments for carrying out this invention will be described mainly by dividing them into three roles: server, terminal, and user.

[0558] The server is responsible for central data processing. First, it receives user behavior data transmitted from the terminal. This behavior data includes information such as the user's activities and operation history. Next, the server uses generative AI technology to analyze this behavior data and generate a user profile. This profile facilitates personalized goal setting and reward design based on behavioral patterns extracted from the behavior history. At this time, an emotion analysis engine is used to estimate the user's emotional state, enabling dynamic and flexible adjustment of tasks and rewards.

[0559] The device presents the user with tasks and reward information sent from the server. Specifically, the device displays appropriate feedback and motivational messages to the user. It also plays a role in acquiring the user's latest behavioral data and sending it back to the server in real time. This allows the server to continuously update the user profile based on the latest data.

[0560] Users take on challenges presented via their devices and aim to achieve their goals. For example, in a health app, if a user aims to walk 10,000 steps every day, the emotion analysis engine evaluates the user's stress and satisfaction during the process and adjusts rewards to support goal achievement.

[0561] An example of a prompt message is: "Based on the user's daily step count data in the health app, and considering the stress level analyzed by the emotion engine, design the optimal reward scenario."

[0562] This system aims to provide users with personalized and optimal challenges and rewards by having servers, terminals, and an emotion engine work together, thereby maintaining user motivation and promoting long-term engagement.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] The device collects user activity data through sensors and input interfaces. Inputs include step count and app usage history. The raw data is sent to a server as output. Specifically, the device periodically collects data and transfers it to the server via the network.

[0566] Step 2:

[0567] The server receives behavioral data from the terminal and analyzes it using a generating AI model. The input is behavioral data sent from the terminal. Data processing involves analyzing behavioral patterns and generating a user profile. The output is the generated user profile. Specifically, the server uses algorithms to perform data transformation and pattern identification.

[0568] Step 3:

[0569] The server uses an emotion analysis engine to estimate the user's emotional state. Inputs include the user profile and recent behavioral data. Data calculations involve determining emotional indices to estimate the user's stress level and satisfaction level. The output is the user's emotional state. Specifically, multiple emotional indices are aggregated, and the evaluation results are analyzed.

[0570] Step 4:

[0571] The server uses generative AI technology to automatically set the optimal tasks and rewards for each user. Inputs include user profiles and emotional states. Data calculations are performed to design tasks and rewards based on this information. The output is customized tasks and rewards for each user. Specifically, a predictive model is used to simulate multiple scenarios and derive the optimal solution.

[0572] Step 5:

[0573] The device presents the user with task and reward information sent from the server. Inputs include task data and reward data from the server. Output is presented to the user as visual or audible feedback. Specifically, the device uses a notification function to inform the user of new tasks and rewards.

[0574] Step 6:

[0575] Users work on the assigned tasks and input the results into a terminal. Input includes the user's activity results and feedback. Output is that data sent back to the server via the terminal. Specifically, users operate the application screen to perform self-reporting and automated measurements.

[0576] Step 7:

[0577] The server continuously optimizes the entire system based on user feedback. Inputs include the user's latest behavioral data and feedback. Data processing involves continuous analysis and learning to update user profiles. Outputs are used to inform future task settings and reward designs. Specifically, the server forms a feedback loop to improve the accuracy of the AI ​​model.

[0578] (Application Example 2)

[0579] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0580] In today's consumer society, providing services that meet user needs and emotions is a crucial challenge. In particular, improving the user experience in physical spaces requires understanding users' real-time emotional states and providing personalized benefits and services immediately. However, conventional systems have struggled to effectively analyze users' emotional states and appropriately propose personalized benefits based on that analysis.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0582] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate user characteristic data; means for automatically setting tasks and rewards suitable for the user using machine learning technology; and means for estimating the user's emotional state in real space via a virtual vision device. This enables immediate reward suggestions based on the user's emotional state.

[0583] "User characteristic data" refers to data that shows attribute information unique to individual users, obtained by analyzing the user's behavioral history.

[0584] "Machine learning technology" refers to algorithms and methods for automatically discovering patterns and rules by analyzing large amounts of data, and for predicting or classifying new information.

[0585] A "virtual vision device" is a device that can overlay digital information onto a real-world environment and is used to assist in the presentation and manipulation of information.

[0586] "Emotional state" refers to the user's psychological and emotional state at any given time, and is primarily estimated by analyzing data obtained from external sources.

[0587] "Proposing special benefits" is the process of proposing special services or rewards to users based on their behavior and emotional state.

[0588] This invention utilizes a server, a virtual vision device, and an emotion analysis engine to implement a system that appropriately provides benefits based on the emotional state of the user. The server is responsible for collecting user behavior data, analyzing its history using behavior analysis algorithms, and generating user characteristic data. Database software and machine learning frameworks (e.g., TensorFlow) are used in this process.

[0589] The virtual vision device, acting as the terminal, acquires the user's facial expressions and movements in real time and processes the data with an emotion analysis engine to estimate their emotional state. Image processing libraries (e.g., OpenCV) can be used for this process. Based on this emotional state, the server then sets user-specific tasks and proposes corresponding rewards.

[0590] Users receive the suggested benefits via their device. For example, if a user uses this system in a real-world setting and it is determined that they are in a stressed emotional state, they may be immediately offered relaxation-related benefits through a virtual vision device.

[0591] As a concrete example, imagine a scenario where a user who appears tired in a shopping mall has their emotional state analyzed in real time through a virtual vision device, and is offered a discount for using a special rest area.

[0592] The generation AI model can be improved in terms of sentiment analysis and reward suggestions by inputting prompts such as the following:

[0593] "We are seeking support in designing a system that analyzes user facial expression data acquired from smart devices to identify their current emotional state, and then provides appropriate services and benefits in real time."

[0594] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0595] Step 1:

[0596] The server receives user behavior data and stores it in a database. This input data includes the user's past purchase history and behavioral patterns. Based on this, a behavioral analysis algorithm is used to process the data and output it as user characteristic data.

[0597] Step 2:

[0598] The virtual vision device, which acts as the terminal, detects the user's facial expressions and movements in real time. It acquires image and video data as input and processes them using an emotion analysis engine. To obtain an output representing the emotional state, it uses an image processing library (e.g., OpenCV) to estimate the user's current emotional state.

[0599] Step 3:

[0600] The server determines individually appropriate tasks and rewards by referencing user characteristic data based on the emotional state transmitted from the terminal. It uses machine learning techniques to perform data calculations and output the content of the reward suggestions. This process utilizes algorithms for personalized suggestions.

[0601] Step 4:

[0602] The user receives reward suggestions sent from the server via their device. Input includes details such as the reward's content and expiration date, and based on this, the user initiates actions to utilize the reward in real time. The reward is applied based on the user's selection, and the feedback is sent back to the server as data for the next analysis cycle.

[0603] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0604] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0605] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0609] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0610] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0611] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0612] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0613] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0614] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0615] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0616] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0617] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0618] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0619] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0620] The system of the present invention provides an effective means for promoting user behavior. The system mainly consists of three elements: a server, a terminal, and a user.

[0621] The server plays a central role, responsible for generating user profiles. The server first collects user behavior data and analyzes it using AI. Based on the analysis, it generates a user profile and automatically sets optimal tasks and rewards accordingly. The server then transmits this information to the terminal and notifies the user.

[0622] The terminal functions as an interface with the user. The terminal presents the user with tasks received from the server and records the user's actions. When the user completes a task, the terminal sends progress data to the server and, in cooperation with the server, immediately processes the awarding of reward points.

[0623] Users interact with the system through their devices. Users plan and execute actions to achieve the tasks presented to them. For example, if the goal is to improve health, users will work on a daily step count challenge. The system rewards users with points each time they complete a task, giving them a sense of accomplishment.

[0624] Furthermore, the server designs login bonuses and retention rewards to support continued user engagement. The device prompts users for these rewards and provides elements that encourage continued use.

[0625] As a concrete example, a fitness app might be designed to reward users with special points if they achieve their step goal for a week straight. This can motivate users and encourage long-term use. This system allows investors to manage user behavior efficiently and at low cost, and provide appropriate incentives.

[0626] The following describes the processing flow.

[0627] Step 1:

[0628] The server periodically receives user behavior data from the device. This data includes, for example, the user's daily step count and app usage history. The server inputs this data into an AI system to analyze the user's behavior patterns.

[0629] Step 2:

[0630] The server generates user profiles based on the analyzed data. These profiles include information about the user's preferences and past behavioral characteristics. Based on these profiles, the server uses AI to automatically set the optimal tasks and rewards for each user.

[0631] Step 3:

[0632] The server sends information about the set task and reward to the device. The device then notifies the user of this information and presents specific behavioral goals. For example, a task such as walking 10,000 steps a day might be displayed.

[0633] Step 4:

[0634] Users work on tasks presented by the server through their devices. Through their daily activities, users take actions to complete these tasks. The device records the user's activities and continuously transmits progress data to the server.

[0635] Step 5:

[0636] The server analyzes the received progress data to confirm whether the user has completed the assigned task. Once completion is confirmed, the server immediately calculates the reward points and sends that information to the terminal.

[0637] Step 6:

[0638] The terminal notifies the user of the reward points received from the server. This allows the user to gain a sense of accomplishment and increase their motivation for the next challenge.

[0639] Step 7:

[0640] The server plans login bonuses and retention rewards to maintain user motivation and sends this information to the device. The device then presents this reward information to the user to encourage further use.

[0641] (Example 1)

[0642] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] Conventional systems often lack sufficient individualization in promoting user behavior, making it difficult to maintain user motivation over the long term. Furthermore, rewards and tasks are frequently set manually, limiting their effectiveness in facilitating behavioral change.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0645] In this invention, the server includes means for a data processing device to acquire user activity information and analyze activity history to form a user profile; means for automatically setting tasks and rewards suitable for the user using artificial intelligence based on the profile; and means for monitoring the achievement level of the tasks and assigning rewards upon completion. This enables the automatic setting of individualized tasks and rewards, and efficiently supports the sustained motivation of the user.

[0646] A "data processing device" is a device that can acquire and analyze information about users' behavior and actions.

[0647] "Action information" refers to data about the user's behavior and activities, and is fundamental information used to analyze behavioral patterns.

[0648] A "user profile" is personalized information obtained by analyzing a user's behavioral history, and is used for setting tasks and providing rewards.

[0649] "Artificial intelligence" is a technology that analyzes large amounts of data and automatically sets tasks and rewards tailored to the user, and it includes a variety of algorithms.

[0650] A "challenge" is a goal or challenge that users should aim to achieve, and it is set to encourage action.

[0651] "Rewards" are incentives given to users upon completing tasks, and they play a role in improving motivation.

[0652] "Achievement level" is an indicator that shows the extent to which a user has achieved the assigned tasks, and it serves as a criterion for determining whether to award rewards.

[0653] "Connection benefits" are bonuses offered to users who regularly access the system, encouraging continued use.

[0654] A "generative AI model" is an artificial intelligence model used to set tasks and rewards that are adapted to the user, enabling efficient data analysis.

[0655] This invention is a system that facilitates user behavior and supports sustained engagement. It mainly consists of three elements: a server, a terminal, and the user.

[0656] The server plays a central role in the system, acquiring user activity information. This information is collected from hardware such as smartphones and wearable devices. The server analyzes the collected information using AI analysis tools such as TensorFlow and PyTorch. Based on the analysis results, it forms a user profile and has the function to automatically set tasks and rewards appropriate to that profile using a generative AI model.

[0657] The terminal functions as an interface device with the user. It notifies the user of tasks received from the server and records their progress. When the user completes a task, the terminal sends progress data to the server, supporting the process of immediately awarding reward points.

[0658] Users tackle challenges through their devices. For example, if the goal is health, users aim to achieve their daily step count target. The system rewards users with points or badges each time they achieve their goal. This helps users stay motivated and manage their own progress.

[0659] For example, fitness apps are designed to reward users with special points for achieving their step goal for a week straight. Such incentives effectively encourage user behavior.

[0660] An example of a prompt might be the text, "Suggest an appropriate reward to give when the user achieves their exercise goal." This prompt is input into the generative AI model and serves as the basis for guiding it to set the optimal reward.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The server acquires user activity information from smartphones and wearable devices. It receives raw data transmitted from each device as input (e.g., steps, heart rate, location information, etc.). The server stores the received data in a database and organizes it for the next analysis step. The output is a set of organized behavioral data.

[0664] Step 2:

[0665] The server analyzes the acquired behavioral information using AI analysis tools such as TensorFlow and PyTorch. This analysis identifies behavioral patterns and extracts user trends. The data organized in step 1 is used as input, and this results in the output of a user profile. The profile includes estimates of the current health status and activity level.

[0666] Step 3:

[0667] The server uses a generative AI model to set optimal tasks and rewards based on the user profile. The input is the user profile created in step 2, and based on this, the AI ​​determines what kind of tasks will motivate the user. The output is a customized task and reward plan for each user.

[0668] Step 4:

[0669] The server sends the generated task and reward information to the device. This input is the customized plan output in step 3. The output to the device includes task notification data and reward information. This allows the user to check the new task on their device.

[0670] Step 5:

[0671] The device notifies the user of tasks received from the server. Specifically, it executes push notifications and in-app messages to allow the user to check the tasks immediately. The device also prepares to record the user's actions. The output is the task information displayed to the user.

[0672] Step 6:

[0673] Users work on tasks using the device. Specifically, they perform tasks related to exercise goals and health management as presented, and the device automatically records this information. The input consists of the task content provided via the device, and daily activities are carried out based on this information.

[0674] Step 7:

[0675] The terminal records the user's task completion status and sends progress data to the server at regular intervals. The input is a log of the user's activities, and the output received by the server is the latest progress information. Based on this information, preparations are made for the next step to be executed.

[0676] Step 8:

[0677] The server analyzes the received progress data and immediately calculates and awards reward points upon completion of the task. The input is the progress data obtained in step 7, and the output is the reward information reflected in the user's account. This process helps maintain user motivation.

[0678] (Application Example 1)

[0679] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] The present invention aims to provide a method for motivating consumer behavior in order to enhance the in-store experience for consumers and encourage store visits and purchases. Conventional systems have limited means of increasing consumer motivation, making it difficult to maintain long-term engagement.

[0681] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0682] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate a user profile; means for automatically setting appropriate tasks and rewards for the user using AI based on the profile; and means for generating specific behavioral tasks to motivate the user's real-world actions and notifying the user via a smart device. This allows consumers to receive personalized challenges and immediate rewards through real-world actions, thereby enhancing the in-store experience and promoting continuous engagement.

[0683] "User behavior data" refers to information about various behavioral histories generated by users, including, for example, the number of store visits and product scanning history.

[0684] A "user profile" is a collection of information that indicates the preferences and behavioral patterns of individual users, generated from the analysis of behavioral data.

[0685] "AI" refers to artificial intelligence technology that analyzes user data to determine the optimal tasks and rewards.

[0686] A "challenge" is a specific action or challenge presented to the user, designed to encourage actions to be taken within the physical store.

[0687] "Reward points" are incentives that users earn by completing tasks, and are provided in the form of coupons, points, etc., that can be used on their next visit or purchase.

[0688] A "login bonus" is a reward that users receive for regularly using the system, and it is a factor that supports continued user engagement.

[0689] "Continued use benefits" are additional incentives that users receive for continuing to use the system, and are designed to encourage continued use.

[0690] "Real-world actions" refer to activities that users perform in actual physical environments, including trying on or testing products in stores.

[0691] A "smart device" is a terminal device used by a user, such as a smartphone or smart glasses, that has the function of notifying or recording information.

[0692] This system is designed to encourage user behavior and enrich the in-store experience. The server acts as the system's core, generating profiles using behavioral data collected from users. This involves the server analyzing the collected data using AI models, such as TensorFlow or PyTorch. Based on these profiles, optimal behavioral tasks and rewards are automatically set.

[0693] The server also generates specific tasks to motivate users to act in the real world and sends them to their devices. These devices are smart devices such as smartphones and smart glasses, and task notifications are sent to the user through these devices. The tasks are tailored to the user and may include tasks such as trying on new products or purchasing specific promotional items.

[0694] When users complete these tasks, action data is sent from their device to the server. The server processes the data in real time and verifies task completion. After verification, the server immediately calculates reward points and reflects them in the user's account. This allows users to receive immediate feedback through their in-store experience, providing them with a greater sense of incentive.

[0695] To give a concrete example, suppose a user visits a fashion brand's store using a smartphone app and takes on a task instructed by the app to "try on three new items." Once the user completes this task, the system immediately issues a special coupon and notifies them that it can be used for purchases in the store.

[0696] An example of a prompt for a generative AI model would be, "Design an AI model that sets optimal challenges and rewards to encourage user behavior in-store." In this way, the system provides a mechanism to maximize the individual consumer experience.

[0697] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0698] Step 1:

[0699] The server collects data on the user's past behavior. The input is the behavioral history obtained via the terminal, and the output is an aggregate of this behavioral data. The server stores the data in a database and uses it for analysis.

[0700] Step 2:

[0701] The server analyzes the collected behavioral data using a generating AI model. The input is the behavioral data obtained in step 1, and the output is a profile for each user. This profile includes analysis results that reflect behavioral tendencies and preferences. The AI ​​model applies algorithms based on the data to identify trends.

[0702] Step 3:

[0703] The server sets tasks appropriate for the user based on the generated user profile. The input is profile data, and the output is appropriate task content. Here, products and experiences highly relevant to the user are selected.

[0704] Step 4:

[0705] The device notifies the user of tasks sent from the server. The input is the task content, and the output is the notification the user receives. The smart device presents the user with a specific task and prompts them to take action.

[0706] Step 5:

[0707] The user completes tasks notified via a terminal. The input is the presented task, and the output is the result of its execution. The user completes tasks such as trying on products or performing specific actions and reports them to the system.

[0708] Step 6:

[0709] The device sends the user's actions to the server. The input is the result of the user's task completion, and the output is the data sent to the server. The smart device sends the action data back to the server in real time.

[0710] Step 7:

[0711] The server reviews the submitted execution data and checks if the task has been completed. The input is the execution data, and the output is the completion status. Once completion is confirmed, the reward is prepared immediately.

[0712] Step 8:

[0713] The server awards reward points to users based on their completion status. The input is the completion status, and the output is the reflection of the reward in the user's account. The system adds points or coupons to the user's account.

[0714] Through this entire process, the system can motivate user behavior, enrich the in-store experience, and foster long-term engagement.

[0715] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0716] This invention is a system that analyzes user behavior data and combines it with an emotion engine to set tasks and rewards that are more suitable for individual users. The system mainly consists of a server, a terminal, an emotion engine, and a user.

[0717] The server handles central processing, receiving user behavior data transmitted from terminals. The server analyzes this data using AI to generate user profiles. Based on these profiles, the server sets optimal tasks and rewards. In this process, the emotion engine plays a role in analyzing data to estimate the user's emotional state.

[0718] The emotion engine is designed to estimate changes in emotional state from user behavior data. For example, if it is estimated that a user experienced stress in their daily activities, the emotion engine provides the server with information to appropriately adjust the reward content and task difficulty.

[0719] The device is responsible for presenting the user with tasks and reward information sent from the server. It also sends the user's emotional state and behavioral data back to the server in real time to receive feedback. When the user completes a task, the device notifies them of an evaluation result tailored to their emotional state. Furthermore, the device provides motivational feedback adjusted based on the user's emotional state.

[0720] Users take on challenges presented through their devices and aim to achieve them. For example, when a user works towards a goal of walking 10,000 steps daily using a health app, the emotion engine evaluates the stress and satisfaction experienced during the process and adjusts rewards to help them achieve their goal. In this way, the server, device, and emotion engine work together to provide users with a personalized experience and promote long-term engagement.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] Users use their devices to perform everyday activities and record their activity data (e.g., steps taken, heart rate, app usage). The devices then send this data to a server.

[0724] Step 2:

[0725] The server analyzes the received behavioral data and uses AI to generate a user profile. This profile reflects the user's behavioral patterns and preferences.

[0726] Step 3:

[0727] The server sets the most suitable tasks and rewards for the user based on the generated profile. This setting also takes into account the results of the emotion engine's analysis. The emotion engine provides emotional states estimated from the user's behavioral data.

[0728] Step 4:

[0729] The emotion engine analyzes the user's emotional state (e.g., stress, satisfaction) from behavioral data and provides it to the server. The server uses this information to adjust the difficulty of the tasks and the content of the rewards.

[0730] Step 5:

[0731] The server sends information about the adjusted tasks and rewards to the device. The device notifies the user of this information and presents specific tasks. For example, it might display something like, "Earn a special bonus if you walk 10,000 steps today."

[0732] Step 6:

[0733] Users work on tasks and monitor their progress through their devices. These devices continuously send this progress data to the server.

[0734] Step 7:

[0735] The server analyzes the submitted progress data to determine whether the user has completed the task. If completion is confirmed, the server calculates reward points and sends them to the device.

[0736] Step 8:

[0737] The device notifies the user of reward information received from the server. The user feels a sense of accomplishment and gains motivation for the next task. The device also sends follow-up notifications to the user based on feedback from the emotion engine (for example, a message such as "Your recent efforts have been recognized").

[0738] Step 9:

[0739] The device displays information about regular login bonuses and continuous user rewards to the user, maintaining long-term motivation. The server updates this reward information as needed and delivers it to the device.

[0740] (Example 2)

[0741] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0742] Traditionally, providing individually optimized experiences based on user behavior and emotional states has been challenging. In particular, there is a need for effective methods to operate dynamically adjustable task settings and reward systems to sustain user motivation and maintain long-term engagement.

[0743] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0744] In this invention, the server includes means for acquiring user behavior data and analyzing the behavior history to create a user profile; means for automatically setting optimal goals and rewards for the user using generative AI technology; and means for estimating the user's emotional state using an emotion analysis engine and dynamically adjusting the goals and rewards. This makes it possible to provide individually optimized tasks and rewards to users, thereby maintaining motivation and improving engagement.

[0745] "Action data" refers to data collected from information related to the user's activities and behaviors.

[0746] "Activity history" refers to a record of a user's activities over time and is used to analyze the user's behavioral patterns.

[0747] A "user profile" is a dataset that represents the individual characteristics and behavioral tendencies of a user, generated based on their behavioral data and history.

[0748] "Generative AI technology" is a technology that utilizes artificial intelligence models to generate optimal results based on user profiles and other input data.

[0749] "Goals" refer to specific challenges or tasks that users should achieve.

[0750] "Reward" refers to the benefits or incentives that users receive when they achieve their goals.

[0751] An "emotion analysis engine" is software or a system that estimates a user's emotional state from their behavioral data and adjusts the information and results accordingly.

[0752] "Motivation" refers to providing users with stimuli or reasons to continue a particular behavior.

[0753] The embodiments for carrying out this invention will be described mainly by dividing them into three roles: server, terminal, and user.

[0754] The server is responsible for central data processing. First, it receives user behavior data transmitted from the terminal. This behavior data includes information such as the user's activities and operation history. Next, the server uses generative AI technology to analyze this behavior data and generate a user profile. This profile facilitates personalized goal setting and reward design based on behavioral patterns extracted from the behavior history. At this time, an emotion analysis engine is used to estimate the user's emotional state, enabling dynamic and flexible adjustment of tasks and rewards.

[0755] The device presents the user with tasks and reward information sent from the server. Specifically, the device displays appropriate feedback and motivational messages to the user. It also plays a role in acquiring the user's latest behavioral data and sending it back to the server in real time. This allows the server to continuously update the user profile based on the latest data.

[0756] Users take on challenges presented via their devices and aim to achieve their goals. For example, in a health app, if a user aims to walk 10,000 steps every day, the emotion analysis engine evaluates the user's stress and satisfaction during the process and adjusts rewards to support goal achievement.

[0757] An example of a prompt message is: "Based on the user's daily step count data in the health app, and considering the stress level analyzed by the emotion engine, design the optimal reward scenario."

[0758] This system aims to provide users with personalized and optimal challenges and rewards by having servers, terminals, and an emotion engine work together, thereby maintaining user motivation and promoting long-term engagement.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The device collects user activity data through sensors and input interfaces. Inputs include step count and app usage history. The raw data is sent to a server as output. Specifically, the device periodically collects data and transfers it to the server via the network.

[0762] Step 2:

[0763] The server receives behavioral data from the terminal and analyzes it using a generating AI model. The input is behavioral data sent from the terminal. Data processing involves analyzing behavioral patterns and generating a user profile. The output is the generated user profile. Specifically, the server uses algorithms to perform data transformation and pattern identification.

[0764] Step 3:

[0765] The server uses an emotion analysis engine to estimate the user's emotional state. Inputs include the user profile and recent behavioral data. Data calculations involve determining emotional indices to estimate the user's stress level and satisfaction level. The output is the user's emotional state. Specifically, multiple emotional indices are aggregated, and the evaluation results are analyzed.

[0766] Step 4:

[0767] The server uses generative AI technology to automatically set the optimal tasks and rewards for each user. Inputs include user profiles and emotional states. Data calculations are performed to design tasks and rewards based on this information. The output is customized tasks and rewards for each user. Specifically, a predictive model is used to simulate multiple scenarios and derive the optimal solution.

[0768] Step 5:

[0769] The device presents the user with task and reward information sent from the server. Inputs include task data and reward data from the server. Output is presented to the user as visual or audible feedback. Specifically, the device uses a notification function to inform the user of new tasks and rewards.

[0770] Step 6:

[0771] Users work on the assigned tasks and input the results into a terminal. Input includes the user's activity results and feedback. Output is that data sent back to the server via the terminal. Specifically, users operate the application screen to perform self-reporting and automated measurements.

[0772] Step 7:

[0773] The server continuously optimizes the entire system based on user feedback. Inputs include the user's latest behavioral data and feedback. Data processing involves continuous analysis and learning to update user profiles. Outputs are used to inform future task settings and reward designs. Specifically, the server forms a feedback loop to improve the accuracy of the AI ​​model.

[0774] (Application Example 2)

[0775] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] In today's consumer society, providing services that meet user needs and emotions is a crucial challenge. In particular, improving the user experience in physical spaces requires understanding users' real-time emotional states and providing personalized benefits and services immediately. However, conventional systems have struggled to effectively analyze users' emotional states and appropriately propose personalized benefits based on that analysis.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0778] In this invention, the server includes means for collecting user behavior data and analyzing behavioral history to generate user characteristic data; means for automatically setting tasks and rewards suitable for the user using machine learning technology; and means for estimating the user's emotional state in real space via a virtual vision device. This enables immediate reward suggestions based on the user's emotional state.

[0779] "User characteristic data" refers to data that shows attribute information unique to individual users, obtained by analyzing the user's behavioral history.

[0780] "Machine learning technology" refers to algorithms and methods for automatically discovering patterns and rules by analyzing large amounts of data, and for predicting or classifying new information.

[0781] A "virtual vision device" is a device that can overlay digital information onto a real-world environment and is used to assist in the presentation and manipulation of information.

[0782] "Emotional state" refers to the user's psychological and emotional state at any given time, and is primarily estimated by analyzing data obtained from external sources.

[0783] "Proposing special benefits" is the process of proposing special services or rewards to users based on their behavior and emotional state.

[0784] This invention utilizes a server, a virtual vision device, and an emotion analysis engine to implement a system that appropriately provides benefits based on the emotional state of the user. The server is responsible for collecting user behavior data, analyzing its history using behavior analysis algorithms, and generating user characteristic data. Database software and machine learning frameworks (e.g., TensorFlow) are used in this process.

[0785] The virtual vision device, acting as the terminal, acquires the user's facial expressions and movements in real time and processes the data with an emotion analysis engine to estimate their emotional state. Image processing libraries (e.g., OpenCV) can be used for this process. Based on this emotional state, the server then sets user-specific tasks and proposes corresponding rewards.

[0786] Users receive the suggested benefits via their device. For example, if a user uses this system in a real-world setting and it is determined that they are in a stressed emotional state, they may be immediately offered relaxation-related benefits through a virtual vision device.

[0787] As a concrete example, imagine a scenario where a user who appears tired in a shopping mall has their emotional state analyzed in real time through a virtual vision device, and is offered a discount for using a special rest area.

[0788] The generation AI model can be improved in terms of sentiment analysis and reward suggestions by inputting prompts such as the following:

[0789] "We are seeking support in designing a system that analyzes user facial expression data acquired from smart devices to identify their current emotional state, and then provides appropriate services and benefits in real time."

[0790] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0791] Step 1:

[0792] The server receives user behavior data and stores it in a database. This input data includes the user's past purchase history and behavioral patterns. Based on this, a behavioral analysis algorithm is used to process the data and output it as user characteristic data.

[0793] Step 2:

[0794] The virtual vision device, which acts as the terminal, detects the user's facial expressions and movements in real time. It acquires image and video data as input and processes them using an emotion analysis engine. To obtain an output representing the emotional state, it uses an image processing library (e.g., OpenCV) to estimate the user's current emotional state.

[0795] Step 3:

[0796] The server determines individually appropriate tasks and rewards by referencing user characteristic data based on the emotional state transmitted from the terminal. It uses machine learning techniques to perform data calculations and output the content of the reward suggestions. This process utilizes algorithms for personalized suggestions.

[0797] Step 4:

[0798] The user receives reward suggestions sent from the server via their device. Input includes details such as the reward's content and expiration date, and based on this, the user initiates actions to utilize the reward in real time. The reward is applied based on the user's selection, and the feedback is sent back to the server as data for the next analysis cycle.

[0799] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0800] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0801] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0802] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0803] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0804] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0805] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0806] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0807] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0808] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0809] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0810] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0811] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0812] 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.

[0813] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0814] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0815] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0816] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0817] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0818] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0819] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] A means for collecting user behavior data, analyzing behavioral history, and generating user profiles,

[0823] A means for automatically setting tasks and rewards suitable for the user using AI based on the aforementioned profile,

[0824] A means for monitoring the progress of the aforementioned task and awarding reward points when it is achieved,

[0825] In order to maintain users' continued motivation, we offer methods such as login bonuses and retention rewards,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the generation of the user profile involves analyzing individualized behavioral patterns using an algorithm.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for notifying the user in real time of the evaluation results when the user has achieved the aforementioned task.

[0831] "Example 1"

[0832] (Claim 1)

[0833] A data processing device acquires user activity information, analyzes the activity history, and forms a user profile.

[0834] A means for automatically setting tasks and rewards suitable for the user using artificial intelligence based on the aforementioned profile,

[0835] A means for monitoring the degree of achievement of the aforementioned task and assigning rewards upon completion,

[0836] In order to maintain users' sustained motivation, means of providing connection benefits and continuous benefit,

[0837] A means of creating appropriate input sentences for a generative AI model,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the formation of the user profile involves analyzing individualized behavioral patterns using a computational method.

[0841] (Claim 3)

[0842] The system according to claim 1, further comprising means for immediately notifying the user of the evaluation results when the user has achieved the aforementioned task.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A means for collecting user behavior data, analyzing behavioral history, and generating user profiles,

[0846] A means for automatically setting tasks and rewards suitable for the user using AI based on the aforementioned profile,

[0847] A means for monitoring the progress of the aforementioned task and awarding reward points when it is achieved,

[0848] In order to maintain users' continued motivation, we offer methods such as login bonuses and retention rewards,

[0849] A means of generating specific behavioral tasks to motivate users to take real-world actions (e.g., visiting stores or testing products) and informing users of these tasks via smart devices,

[0850] A means of calculating and immediately providing rewards based on achieved real-world actions, and using them to improve future activities,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the generation of the user profile involves analyzing individualized behavioral patterns using an algorithm, as well as analyzing patterns that take into account the user's activity history at the store.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for notifying the user in real time of the evaluation results when the user has achieved the task, and means for immediately presenting the user with details of the reward when the user has achieved a real-world action.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] A means of acquiring user behavior data, analyzing the behavior history, and creating a user profile,

[0859] Based on the aforementioned profile, a means for automatically setting optimal goals and rewards for the user using generative AI technology,

[0860] A means for estimating the user's emotional state using an emotion analysis engine and dynamically adjusting the aforementioned goal and reward,

[0861] A means for monitoring the achievement status of the aforementioned objective and issuing a reward when it is achieved,

[0862] In order to maintain user motivation, we offer methods such as login rewards and recurring rewards,

[0863] ...

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which uses an algorithm to analyze individually optimized behavioral patterns based on the aforementioned operational data.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising means for immediately notifying the user of the evaluation results when the aforementioned goal is achieved.

[0869] "Application example 2 of combining emotional engines"

[0870] (Claim 1)

[0871] A means for collecting user behavior data, analyzing behavioral history, and generating user characteristic data,

[0872] A means for automatically setting tasks and rewards suitable for the user using machine learning technology based on the aforementioned feature data,

[0873] A means for monitoring the progress of the aforementioned task and providing reward resources when it is achieved,

[0874] In order to maintain user motivation, we offer means to provide connection bonuses and retention benefits,

[0875] A means for estimating a user's emotional state in real space via a virtual visual device,

[0876] A means of proposing the most suitable benefits to the user based on the estimated emotional state,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the generation of user characteristic data involves analyzing individualized behavioral patterns using an algorithm.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for notifying the user in real time of the evaluation results when the user has achieved the aforementioned task. [Explanation of Symbols]

[0882] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting user behavior data, analyzing behavioral history, and generating user profiles, A means for automatically setting tasks and rewards suitable for the user using AI based on the aforementioned profile, A means for monitoring the progress of the aforementioned task and awarding reward points when it is achieved, In order to maintain users' continued motivation, we offer methods such as login bonuses and retention rewards, A system that includes this.

2. The system according to claim 1, wherein the generation of the user profile involves analyzing individualized behavioral patterns using an algorithm.

3. The system according to claim 1, further comprising means for notifying the user in real time of the evaluation results when the user has achieved the aforementioned task.

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