A method, system and computer readable storage medium for generating premeditated interactive content

Abstract

With the widespread application of intelligent systems in industrial equipment, service robots, in-vehicle systems, smart homes, and other fields, users have placed higher demands on the naturalness, emotional warmth, and responsiveness of interactions. Existing technologies typically suffer from drawbacks such as immediacy, lack of pre-planning, resource contention leading to response delays, weak emotional connection, and disconnect from future events. Therefore, there is a need for a system and method that can plan ahead based on future events or opportunities, pre-generate content during system idle periods, and proactively present it at appropriate times to improve the anthropomorphism, resource utilization efficiency, and emotional connection of interactions. This invention provides a pre-planned interactive content generation method, system, and computer-readable storage medium, aiming to enable the system to plan ahead based on future events or opportunities, pre-generate content during idle periods, and proactively present it at appropriate times, achieving anthropomorphic and warm interactions while optimizing resource utilization.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence, human-computer interaction, resource scheduling and affective computing, and more specifically, to a method, system and computer-readable storage medium for premeditated interactive content generation. Background Technology

[0002] With the widespread application of intelligent systems in industrial equipment, service robots, in-vehicle systems, and smart homes, users are demanding higher levels of naturalness, emotional warmth, and responsiveness in interactions. Current interactive systems generally suffer from the flaw of instant response, generating content only when a user initiates an interaction, resulting in a passively triggered system behavior. Such systems cannot simulate the human ability to plan behavior in advance, and the interaction process lacks emotional depth and elements of surprise, making the interaction feel mechanical and stiff to users. In terms of resource utilization, the instant generation of complex content upon user request easily leads to competition for computing resources, causing response delays and stuttering, significantly impacting the smoothness of the user experience. Furthermore, while existing technologies can identify calendar events or user behavior patterns, this is limited to simple reminder functions and fails to combine event prediction with proactive behavior planning, preventing the system from establishing deep emotional connections. In addition, the interaction content generation process is disconnected from the system resource status, failing to effectively utilize idle time for preprocessing, resulting in uneven resource allocation. These shortcomings collectively limit the anthropomorphism and emotional warmth of human-computer interaction, making it difficult for the system to proactively create surprising experiences.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this application is to provide a premeditated interactive content generation method, system, and computer-readable storage medium, which has the advantages of improving the naturalness and response speed of interaction, reducing latency, enhancing the smoothness of user experience, and increasing emotional depth and surprise elements.

[0005] This application provides a premeditated interactive content generation method, the technical solution of which is as follows: Predict future interaction opportunities; Determine the interactive content to be generated before the timing of future interactions; Pre-generate and store interactive content; When the time for future interaction arrives, the pre-generated interactive content will be invoked for interaction.

[0006] Furthermore, this application also proposes to predict future interaction opportunities, including but not limited to predictions based on calendar events, user behavior patterns, or periodic needs.

[0007] Furthermore, this application also proposes that the interactive content to be generated be determined autonomously, including but not limited to user historical preferences, current context, or relationship parameters.

[0008] Furthermore, this application also proposes that pre-generating interactive content includes monitoring the system resource usage status, identifying system idle periods, and calling a generation model during the idle periods. The generation model is used to generate any form of content required for the interaction.

[0009] Furthermore, this application also proposes that the generative model includes, but is not limited to, an image generation model, a text generation model, an action generation model, or an audio generation model.

[0010] Furthermore, this application also proposes that invoking pre-generated interactive content for interaction includes reading the pre-generated content from storage and presenting it to the user when the predicted future interaction time arrives.

[0011] Furthermore, this application also proposes to include recording user feedback on interactive content and optimizing future autonomous decision-making steps based on the feedback.

[0012] Furthermore, this application also proposes that the interactive content includes, but is not limited to, images, text, audio, or action sequences.

[0013] Furthermore, this application also proposes a premeditated interactive content generation system, comprising: an event prediction module for predicting future interaction opportunities; a premeditated decision-making module for autonomously determining the interactive content to be generated before the future interaction opportunity; a pre-generation module for pre-generating and storing the interactive content; and a triggering module for calling the pre-generated interactive content to perform the interaction when the future interaction opportunity arrives.

[0014] Furthermore, this application also proposes to include a feedback learning module for recording user feedback on interactive content and optimizing the decision-making strategy of the pre-planning decision-making module.

[0015] Furthermore, this application also proposes that the pre-generation module includes, but is not limited to, an image generation unit, a text generation unit, an action generation unit, or an audio generation unit.

[0016] Furthermore, this application also proposes to include a resource monitoring module for monitoring system idle periods; Furthermore, this application also proposes that the resource monitoring module be configured to identify idle periods based on the system's CPU, memory, or GPU usage, and the pre-generation module perform pre-generation during the idle periods.

[0017] Furthermore, this application also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0018] As can be seen from the above, the pre-planned interactive content generation method, system, and computer-readable storage medium provided in this application effectively avoid resource contention during real-time responses, reduce latency, and improve interactive fluency by predicting future interaction opportunities and determining and pre-generating interactive content in advance. At the same time, it enhances emotional depth and surprise elements, thus possessing the aforementioned advantages.

[0019] Comparative analysis with existing technologies: To more clearly illustrate the innovations of this application, a comparative analysis of the technical solution of this application and existing technologies is presented. It should be noted that the following comparison is only for the purpose of helping to understand the innovative essence of this invention and does not constitute a limitation on the scope of protection of this invention.

[0020] Regarding the response mode: existing technologies generally adopt an immediate response, which is only processed after the user triggers it; while the technical solution of this application can achieve a pre-planned response, which can be planned in advance and presented in a timely manner.

[0021] Regarding resource scheduling: existing technologies generate data in real time during interaction, which may cause delays; while the technical solution of this application can pre-generate data during idle periods, making use of idle system resources in advance.

[0022] Regarding the timing of content generation: Existing technologies generate content when a user requests it; while the technical solution of this application can achieve future timing prediction + generation during idle periods.

[0023] Regarding emotional connection: existing technologies respond mechanically and lack emotional depth; while the technical solution of this application can create surprise and enhance emotional connection through premeditated behavior.

[0024] Regarding event association: in the prior art, events are only used for simple reminders; while the technical solution of this application can realize event-driven planning, and autonomously decide the content of the planning based on the predicted future timing.

[0025] In terms of technical effectiveness: existing technologies suffer from passive interaction, resource constraints, and limited user experience; while the technical solution of this application can achieve proactive interaction, smooth resource utilization, and a rich user experience.

[0026] The above comparison shows that this application is not a simple "scheduled task" or "resource scheduling", but rather a completely closed loop of "future opportunity prediction + autonomous decision-making + idle pre-generation + delayed triggering", which realizes a brand-new "premeditated" interaction paradigm and has significant creativity. Attached Figure Description

[0027] Several embodiments of this application are described below with reference to the accompanying drawings. It should be noted that the specific structures, modules, steps, parameters, and connections shown in the drawings are preferred embodiments of this application and not limitations on the scope of protection of this application. Those skilled in the art can make various modifications, substitutions, or combinations to the specific details shown in the drawings based on the teachings of this application, and these modified embodiments should still be considered to fall within the scope of protection of this application.

[0028] Figure 1 This application provides an overall architecture diagram of a premeditated interactive content generation system. The diagram illustrates an exemplary architecture of the premeditated interactive content generation system. Each module can be adjusted according to actual applications and does not limit the scope of protection.

[0029] Figure 2 This application provides a flowchart of a premeditated interactive content generation process. The flowchart illustrates the complete process of premeditated content generation, reflecting a closed-loop mechanism of "prediction-decision-pregeneration-triggering-learning". The flowchart is only an example and does not constitute a limitation on the claims.

[0030] Figure 3 This diagram illustrates a cross-domain application example of this application. It shows application scenarios of this application in fields such as industrial equipment, service robots, vehicle systems, and smart homes. The diagram is only an example, and the specific implementation can be adjusted according to actual needs. It does not constitute a limitation on the claims. Detailed Implementation

[0031] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. Other technologies that may be mentioned in the embodiments can be implemented using existing technology or other patent applications filed by the applicant on the same day, and will not be repeated here. It should be particularly noted that the specific module divisions, process steps, data flow directions, status names, time values, etc., shown in the accompanying drawings are merely illustrative examples and should not constitute a limitation on the scope of protection of the claims of this application. The scope of protection of the claims is determined solely by their wording and should be interpreted in accordance with the overall content of the specification.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] In traditional interactive systems, content generation only responds to immediate user input, lacking a mechanism to predict future interaction opportunities. System resource scheduling is not configured to identify idle periods, leading to contention for computing resources among multiple generation tasks when user interaction requests occur in concentrated bursts. Response latency is introduced into critical interaction paths, thus limiting real-time system performance. Simultaneously, interactive behavior is constrained by the current input context, unable to proactively plan based on predicted future events, resulting in weak emotional connections and a user experience limited to transaction processing.

[0034] For example, in a home service robot application scenario, the system monitors behavior patterns and detects that users typically return home around 6:30 PM. When a user actually opens the door, the robot is required to instantly generate a personalized welcome animation and a summary of the day's information. At this time, multiple smart home devices simultaneously activate service requests, putting the system's central processing unit (CPU) under high load. After the image generation model is invoked, due to limited graphics processing unit (GPU) resources, the content generation process experiences delays exceeding the system design specifications. Users experience lag, the expected interactive experience is not achieved, and the system is unable to establish a deep emotional connection.

[0035] If the above problems are not resolved, the interactive system will continue to rely on passive triggering mechanisms. Resource contention will worsen during peak system load periods, disrupting the stability of response times for critical interaction paths. The lack of emotional connection will solidify system interaction behavior at the functional response level, failing to meet users' evolving needs for more human-like interaction. In the long run, the value of the technology will be limited by mechanical response patterns, hindering the deepening of user relationships and significantly reducing the system's adaptability in complex interaction scenarios.

[0036] In this regard, this application raises... A method for generating premeditated interactive content includes the following steps: Predict future interaction opportunities; Determine the interactive content to be generated before the timing of future interactions; Pre-generate and store interactive content; When the time for future interaction arrives, the pre-generated interactive content will be invoked for interaction.

[0037] For ease of understanding, the following explains some key terms in this embodiment: Premeditated interactive content generation is a systematic process that predicts potential future interaction opportunities and plans, generates, and stores interactive content in advance for presentation at the appropriate time. This method aims to improve the human-likeness, responsiveness, and user experience of interactions.

[0038] Future interaction opportunities refer to the points in time that the system anticipates or identifies through some means, indicating a potential need for interaction with the user in the future. These opportunities can be specific dates or time periods, or potential interaction points triggered by certain events.

[0039] Interactive content refers to any form of information or media presented to the user by the system during human-computer interaction. This can include, but is not limited to, text information, images, audio, video clips, or sequences of actions.

[0040] Autonomously determining the interactive content to be generated refers to the process by which the system, without immediate user instructions, decides what specific interactive content to generate based on its internal logic or preset rules. This process reflects the system's proactivity and foresight.

[0041] Pre-generating and storing interactive content refers to the system using its computing resources to generate the required interactive content before the future interaction opportunity arrives, and saving the generated results to an accessible storage medium for quick retrieval and use later.

[0042] Calling pre-generated interactive content for interaction means that when the predicted future interaction opportunity actually arrives, the system reads and uses the previously generated interactive content from the preset storage location and presents it to the user, thereby completing an interaction process.

[0043] This embodiment provides a pre-planned interactive content generation method, which optimizes the interaction through a series of steps.

[0044] First, the method includes predicting future interaction opportunities. This step aims to enable the system to anticipate potential interaction needs, thus laying the foundation for subsequent planned actions. For example, the system can be configured to predict interaction opportunities based on a pre-set fixed schedule, such as setting 8:00 AM daily as a fixed "good morning" interaction time. Alternatively, the system can predict interaction opportunities based on reminders manually entered by the user, such as a user manually entering an event like "Remind me to send birthday wishes to a friend in three days." In these ways, the system can proactively identify moments when future interaction with the user may be necessary, rather than simply passively waiting for the user to initiate interaction.

[0045] Secondly, before the predicted future interaction opportunity, the system autonomously determines the interactive content to be generated. This step aims to ensure that the system can plan and prepare relevant interactive content in advance. Specifically, the system can randomly select content from a pre-set general content library as the interactive content to be generated. For example, for the predicted "good morning greeting" opportunity mentioned above, the system can randomly select a greeting from a text library containing general greetings such as "Good morning" and "A new day begins." Alternatively, the system can select an image from a seasonal image library based on the current date as the interactive content. In this way, the system can initially determine a direction for the content to be used for interaction before the interaction opportunity arrives.

[0046] Furthermore, the method includes pre-generating and storing the interactive content. The core of this step is to complete the content generation task in advance to avoid potential resource contention and response delays during actual interaction. For example, after determining the interactive content to be generated, the system can immediately call a simple text generator to generate the corresponding text string and save it to a local cache file. Alternatively, if the content to be generated is an image, the system can call a basic image rendering program to generate an image based on a preset template and store it in the device's storage space. Through this mechanism of pre-generating and storing, the system ensures that the content is ready at the time of interaction.

[0047] Finally, when the predicted interaction time arrives, the system invokes pre-generated interactive content to perform the interaction. This step ensures that content is presented to the user quickly and seamlessly at the predicted time. For example, when the predicted "good morning greeting" time (such as 8:00 AM) arrives, the system reads the pre-generated greeting text from a previously stored local cache file and displays it on the user's smart device screen. Alternatively, if an image is pre-generated, the system will directly load and display the image when the time arrives. Thus, the user can receive proactive interaction from the system in a timely manner without waiting for content to be generated instantly.

[0048] The following example will provide a more detailed explanation of the above technical solution: Suppose in a smart assistant system, user A manually adds an event to their calendar, marked "Next Wednesday, remind me to send a project progress report to colleague B." The smart assistant system, by reading this calendar event, predicts that "Next Wednesday" is a future interaction opportunity that requires reminding user A. After predicting this opportunity, the smart assistant system autonomously determines the interaction content to be generated on Monday of the following year (i.e., before the future interaction opportunity). Specifically, the system can generate a text message based on a preset general reminder template, stating, "Please note that a project progress report needs to be sent to colleague B next Wednesday." The system then pre-generates this text message and stores it in a local reminder message cache. When the future interaction opportunity of "Next Wednesday" actually arrives, the smart assistant system retrieves the pre-generated text message from the cache and pushes it to user A's smart device as a notification, thus completing a pre-planned interaction.

[0049] The aforementioned pre-planned interactive content generation method, by predicting future interaction opportunities, enables the system to proactively identify potential interaction needs, rather than passively waiting for user triggers. This contrasts with traditional real-time response systems that only begin processing when a user initiates an interaction, effectively solving the problem of unplanned interaction. Furthermore, the system autonomously determines the interactive content to be generated before the future interaction opportunity, pre-generates and stores the interactive content, allowing the content generation task to be completed in advance. This avoids resource contention and response delays that may occur when generating complex content in real-time during peak interaction periods, thereby improving the system's response speed and resource utilization efficiency. When the future interaction opportunity arrives, the pre-generated interactive content is directly invoked for interaction, ensuring the timeliness and smoothness of the interaction. Compared to the mechanical and passive interaction modes in existing technologies, this embodiment achieves a more proactive and smooth interactive experience by constructing a complete closed loop of "prediction-decision-pre-generation-triggering," providing users with more forward-looking and efficient interactive services, and effectively solving the technical problems of pre-planning, resource efficiency, and response speed in existing interactive systems.

[0050] In some of the solutions mentioned above in this application, the prediction of future interaction opportunities is proposed to prepare interaction content in advance. However, in this process, the prediction method may not be comprehensive or specific enough to cover multiple possible sources of interaction opportunities, such as calendar events, user behavior patterns or periodic needs, which leads to inaccurate prediction and affects the overall planning effect.

[0051] In this regard, this application further proposes to predict future interaction opportunities, including but not limited to predictions based on calendar events, user behavior patterns, or periodic needs.

[0052] Predicting future interaction opportunities is the first step in premeditated interactive content generation methods. Its role is to identify and determine future points in time when the system needs to interact with the user. This step is fundamental to "premeditation," enabling the system to prepare content before the actual interaction occurs. Possible implementation methods include, but are not limited to: inferring potential interaction opportunities by analyzing user schedules, pre-set event triggers within the system, or combining external environmental information (such as weather forecasts and news headlines). Calendar event-based prediction refers to the system using known event information with specific time points to determine future interaction opportunities. These events are usually pre-set, fixed, or explicitly marked by the user. Possible implementation methods include, but are not limited to: the system integrating the user's digital calendar (such as Google Calendar or Outlook Calendar) to extract information such as birthdays, anniversaries, meetings, and holidays; or the system maintaining an internal event database containing public holidays, important event dates, etc. User behavior pattern-based prediction refers to the system learning and analyzing historical user behavior data to identify habitual patterns exhibited by users at specific times or in specific contexts, and inferring future interaction opportunities based on these patterns. Possible implementation methods include, but are not limited to: the system can analyze user activity levels for specific applications or functions at different times (such as morning, lunch break, and evening) to identify users' typical rest, work, or leisure times; or the system can track users' device usage habits in specific scenarios (such as returning home, leaving home, or driving) to predict when users may need to interact. Predicting based on periodic needs refers to the system identifying and utilizing needs or events that recur at fixed or predictable periods to determine future interaction opportunities. Possible implementation methods include, but are not limited to: the system can identify repetitive tasks such as monthly bill payments, weekly report checks, and daily morning news briefings; or the system can predict interaction opportunities based on system-level periodic events such as device maintenance cycles and software update cycles.

[0053] This application's solution refines the prediction of future interaction opportunities by basing it on calendar events, user behavior patterns, or periodic needs, enabling the system to identify potential interaction opportunities more comprehensively from multiple dimensions. Specifically, the system first analyzes the user's digital calendar or a pre-set holiday database to obtain calendar events with specific time points, thereby determining fixed interaction opportunities. Simultaneously, the system continuously monitors and learns from the user's historical behavioral data, such as application usage time, device activity periods, and specific operating habits, extracting user behavior patterns in different contexts to predict non-fixed but regular interaction opportunities. Furthermore, the system also identifies and records periodic tasks or needs of the user or the system itself, such as monthly bill reminders and weekly report generation, to capture recurring interaction opportunities. These prediction results are not isolated but can corroborate and complement each other. For example, the system can combine the user's birthday (calendar event) and the user's typical evening activity (user behavior pattern) to determine the optimal time for sending birthday greetings. This multi-dimensional and comprehensive prediction mechanism provides a more accurate and reliable time basis for subsequent steps such as autonomously determining the interaction content to be generated before the future interaction opportunity, pre-generating and storing the interaction content, and calling the pre-generated interaction content for interaction when the future interaction opportunity arrives, thereby significantly improving the accuracy and effectiveness of the entire pre-planned interaction process.

[0054] The following example illustrates this concept. In a smart home system, the system needs to provide users with personalized morning briefings. The system first accesses the user's digital calendar to identify a consistent commute schedule from Monday to Friday at 7:00 AM, providing a calendar-based prediction of when the user will receive a morning briefing. Simultaneously, the system analyzes the user's smart speaker usage history, discovering that the user typically plays news or weather forecasts between 7:15 and 7:45 AM on weekdays, revealing a user behavior pattern. Furthermore, the system identifies a weekly Monday morning newsletter subscription at 8:00 AM, indicating a recurring need. Combining this information, the system can accurately predict when the user will need a morning briefing around 7:15 AM on weekdays. Based on this prediction, the system can pre-generate and cache personalized news summaries and weather forecast audio during idle periods the previous night or early morning, playing them directly to the user at 7:15 AM the following morning.

[0055] Through the above technical solutions, the system can predict future interaction opportunities more comprehensively and accurately, avoiding the problems of missed or inaccurate interaction opportunities caused by a single or vague prediction method. This enables the system to prepare content more reliably before the interaction opportunity arrives, thereby improving the overall reliability of pre-planned interactions and enhancing the anthropomorphism of the interaction and the user experience.

[0056] In some of the solutions described above, the proposed method involves autonomously determining the interactive content to be generated in advance of predicted future interaction opportunities. However, without specific decision-making criteria, this process may result in content that is not personalized or relevant, affecting the interaction effect and user emotional connection. For example, decisions may rely solely on simple rules while ignoring user preferences or real-time environments, leading to mechanical and impersonal content. Therefore, this application further proposes autonomously determining the interactive content to be generated, including but not limited to those based on user historical preferences, current context, or relationship parameters.

[0057] Among these, "based on user historical preferences" refers to the system constructing a personalized user profile by analyzing past user interaction data, behavioral patterns, interest tags, purchase records, browsing history, likes, or dislikes. Specifically, machine learning algorithms, such as collaborative filtering or deep learning recommendation systems, can be used to model user historical data to identify long-term interests and short-term preferences. Additionally, user preferences can be obtained through explicitly set preferences, such as interest tags or subscribed content in the user's profile, or preferences explicitly expressed in interactions, such as "I like this style of image." "Current context" refers to the user's real-time environment, state, or contextual information. Specifically, this can include time information, such as date, day of the week, and time of day; geographical location information, such as the user's city, whether indoors or outdoors; weather information, such as sunny, rainy, or snowy; user device status, such as battery level and network connection; and the user's emotional state identified through voice tone or text sentiment analysis. Furthermore, it can also be real-time data acquired by the system through sensors, external APIs, or user input, such as the user's ongoing activities, such as driving, exercising, or working, and the noise level of the surrounding environment. Relationship parameters refer to the degree of connection, intimacy, or interaction history between the system and the user, or between the user and third-party entities such as relatives, friends, or colleagues. Specifically, this can be quantified by statistically analyzing the frequency and depth of interactions between the system and the user, the user's adoption rate of system suggestions, and the user's feedback on system actions, such as likes or comments. Furthermore, the strength of the relationship between the user and third-party entities can be inferred by analyzing user social network data, contact information, and jointly participated activities.

[0058] This application addresses the aforementioned problems by initiating an intelligent content decision-making process after predicting future interaction opportunities but before the actual interaction occurs. Specifically, when the system predicts a potential future interaction opportunity, the pre-planning decision-making module does not randomly or based on general rules to determine the content to be generated, but rather comprehensively considers multi-dimensional information. The system deeply analyzes the user's historical preferences, such as the types, styles, or topics the user has previously liked, to ensure that the generated content accurately matches the user's long-term interests and personalized needs. Simultaneously, the system also perceives and evaluates the current context in real time, including but not limited to time, location, weather, user device status, and even the user's possible emotional state, making the generated content more timely and relevant. Furthermore, the system adjusts the depth and emotional tone of the content based on relationship parameters with the user, such as the frequency or intimacy of interaction. Through comprehensive analysis and intelligent weighting of these parameters, the system can autonomously and intelligently determine the most suitable interactive content to present at the future interaction opportunity. This multi-dimensional and personalized decision-making mechanism ensures that the pre-generated content accurately matches the user's needs and context, thereby avoiding the problems of impersonal and irrelevant content and significantly improving the quality of interaction and user experience.

[0059] The following is a concrete example to illustrate this. As a specific implementation method, in an agent system, when the agent predicts that a user's wedding anniversary is in three days, it needs to autonomously determine a suitable gift. At this point, the agent first analyzes the user's historical preferences. For example, by retrieving the user's past conversations, browsing history, or social media likes, it discovers that the user has repeatedly mentioned liking roses or purchased rose-related products on e-commerce platforms. Simultaneously, the agent considers the current context; for example, it recognizes that the current season is spring, the peak season for roses, or it determines that the user is in a good mood and suitable to receive a surprise based on a recent sentiment analysis model. Furthermore, the agent evaluates the relationship parameters with the user. Since it's a wedding anniversary, the relationship between the system and the user is judged to be close, thus requiring a "gift" with emotional value rather than a simple reminder. Combining this information, the agent autonomously generates a high-resolution image of roses as the anniversary gift, along with a personalized blessing.

[0060] Through the aforementioned technical solutions, the system can make intelligent decisions based on users' historical preferences, current context, and relationship parameters, thereby autonomously determining highly personalized, context-relevant, and emotionally resonant interactive content. This transforms pre-planned interaction from a mechanical, passive functional response into a more human-like "pre-planned" behavior, significantly enhancing the naturalness, satisfaction, and emotional connection of the interaction. Users will feel the system's deep understanding and care for their needs, thus strengthening their trust and dependence on the system. Simultaneously, this intelligent decision-making avoids resource waste, ensuring that the pre-generated content is truly valuable and acceptable to users, thereby optimizing the overall interactive experience.

[0061] In some of the solutions mentioned above in this application, interactive content is generated in advance to prepare the content before the interaction. However, if the generation operation is carried out during the busy period of the system, it may aggravate the competition for resources, resulting in system delays or a decline in user experience.

[0062] In response, this application further proposes to pre-generate interactive content by monitoring the system resource usage status, identifying system idle periods, and calling a generation model during the idle periods. The generation model is used to generate any form of content required for the interaction.

[0063] Specifically, monitoring system resource usage status refers to the continuous monitoring and data collection of the real-time operational status of key hardware resources such as processors (CPU), memory, graphics processing units (GPUs), disk I / O, and network bandwidth, as well as software resources such as running processes and services in a computer system or device. This can be achieved through standard API interfaces provided by the operating system (e.g., reading information from the ` / proc` filesystem in Linux, or using Performance Counters in Windows), or by deploying a lightweight system monitoring agent. This agent periodically collects data on various resource utilization rates, idle rates, and other metrics, and summarizes or reports this data to the core processing unit for analysis. Identifying system idle periods helps to intelligently determine whether the system is currently or will be in a low-load, resource-sufficient state at a certain future time based on the monitored system resource usage data, thus providing a basis for scheduling non-urgent but computationally intensive tasks. In practical implementation, a series of predefined resource utilization thresholds can be set (e.g., when the average CPU utilization is below 20% for a continuous period and the memory idle rate is above 50%, it is determined to be a system idle period), or a combination of user behavior patterns (e.g., the user has not performed any operation for a long time, the device is in standby or sleep mode) and preset off-peak time windows (e.g., 2 AM to 5 AM) can be used to comprehensively judge and confirm the system idle period. The step of calling the generation model during the idle period aims to utilize the time when system resources are sufficient and the impact on user experience is minimal to initiate the interactive content generation task. This can be achieved through the operating system-level task scheduler (e.g., Linux Cron Job, Windows Task Scheduler) or the application's internal asynchronous task queue mechanism, triggering the execution of the pre-generated task after receiving a notification of a system idle period. Another approach is for the system's internal resource management module to continuously monitor the idle status, and once the preset idle conditions are met, directly call the generation model integrated in the pre-generation module through an internal interface. The generative model is used to generate any form of content required for interaction. This generative model refers to an algorithm or program module capable of autonomously creating or synthesizing various types of interactive content based on specific input instructions, contextual information, or preset rules. This can include, but is not limited to, deep learning-based generative adversarial networks (GANs), variational autoencoders (VAEs), or large pre-trained models (such as large language models with the Transformer architecture) for generating complex text, images, audio, video clips, or action sequences. Furthermore, it can also be a generator based on rule engines, template matching, or knowledge graphs for generating structured text reports, simple graphical elements, or preset interactive flows.

[0064] This application optimizes the timing of pre-generated interactive content by introducing refined management of system resource status. Specifically, when interactive content needs to be pre-generated and stored for subsequent interactions, the system first continuously monitors system resource usage, acquiring real-time load information for key resources such as processors, memory, and graphics processors. Based on this real-time monitoring data, the system intelligently identifies idle periods—time windows with low resource utilization that will not affect normal user operation. Subsequently, during these identified idle periods, rather than when the user is active or the system is busy, the system invokes the generation model to generate any form of content required for the interaction. This generation model is highly flexible and versatile, capable of generating various forms of interactive content, such as text, images, audio, or action sequences, based on the content requirements determined by the pre-planning decision module. In this way, this application removes the computationally intensive content generation task from the critical path of user interaction, utilizing idle system resources for preprocessing. This ensures that when the time for interaction arrives in the future, pre-generated content can be quickly and smoothly invoked for interaction, greatly improving the overall response speed and the smoothness of the user experience. This mechanism makes pre-planned interactions more efficient and resource-friendly, avoiding resource bottlenecks and delays that may occur in traditional real-time generation modes.

[0065] The following is a concrete example. In a smart home system, the system analyzes user behavior patterns and predicts that the user typically wakes up at 7:30 AM and listens to a news briefing during breakfast. Therefore, the system needs to pre-generate a personalized news briefing audio. To avoid system lag or delays caused by generating the audio immediately after the user wakes up, the system continuously monitors the CPU usage, memory consumption, and network bandwidth of the smart home hub. For example, between 2:00 AM and 5:00 AM, if the system detects that the user's device is in deep sleep and the utilization of various system resources is far below preset thresholds (e.g., CPU utilization below 10%, memory idle rate above 80%), the system recognizes this as a typical idle period. During this idle period, the system activates its built-in text generation and speech synthesis models. The text generation model generates a personalized news summary text based on user preferences and the latest news data, and then the speech synthesis model converts this text into a natural and fluent audio file. This pre-generated audio file is stored in a local cache. When the user wakes up at 7:30 the next morning, the system can directly retrieve and play the pre-generated news briefing audio from the cache, without having to perform complex generation calculations while the user is waiting, thus providing a seamless and efficient interactive experience.

[0066] Through the above technical solution, this application effectively solves the problems of resource contention and response delay that may occur during the pre-generation of interactive content. By intelligently monitoring the system resource usage status and identifying idle periods, the system can schedule computationally intensive content generation tasks to be executed during low-load periods that do not affect user experience. This not only avoids system lag and response delays caused by the instant generation of complex content during peak user activity periods, significantly improving the smoothness of interaction and user satisfaction, but also makes full use of idle system resources, improving overall resource utilization efficiency. Therefore, when the predicted future interaction opportunity arrives, the system can quickly call upon the prepared content for interaction, making the entire pre-planned interaction process more efficient and smooth, and providing users with a higher quality and more seamless experience.

[0067] In some of the solutions described above in this application, generative models are proposed to generate any form of content required for interaction during idle periods. However, in this process, the specific implementation of the generative model is not clearly defined, which may lead to low content generation efficiency or failure to cover diverse interaction needs. For example, it may not be able to efficiently adapt to different forms such as images, text, actions, or audio, affecting resource optimization and interaction richness in the pre-generation process.

[0068] In this regard, this application further proposes generative models, including but not limited to image generation models, text generation models, action generation models, or audio generation models.

[0069] Generative models are computational models capable of autonomously creating new output data with specific forms and content based on specific inputs or conditions. These models are typically based on machine learning, particularly deep learning techniques, to generate content similar to the training data but with originality by learning patterns and structures from large amounts of existing data. Their implementation can include, but is not limited to, neural network-based architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, or generators based on rules, templates, or hybrid methods. Image generation models are specifically designed to generate visual image content. These models can create realistic, stylized, abstract, or subject-specific images based on text descriptions, sketches, style references, or other images as input. For example, techniques such as diffusion models (e.g., Stable Diffusion, DALL-E), Generative Adversarial Networks (GANs), or Variational Autoencoders (VAEs) can be used to generate images. Text generation models are specifically designed to generate natural language text content. These models can generate various forms of text, such as articles, summaries, dialogues, code, and poems, based on given prompts, context, topics, or keywords. For example, large-scale language models (LLMs, such as the GPT series, BERT, and T5), recurrent neural networks (RNNs), or Transformer architectures can be used to generate text. Action generation models are specifically designed to generate dynamic action sequences or behavioral performances. These models can generate character animations, robot motion trajectories, virtual character expressions, gestures, or dance sequences based on instructions, context, or character characteristics. For example, reinforcement learning-based models, sequence generation models (such as RNNs and Transformers), or physics simulation models can be used to generate actions. Audio generation models are specifically designed to generate sound content. These models can generate speech, music, sound effects, ambient sounds, or synthesized speech based on text, melody, emotional parameters, or environmental descriptions. For example, text-to-speech (TTS) models, music generation models (such as MuseNet and Jukebox), ambient sound generation models, or speech synthesis models can be used to generate audio.

[0070] In pre-planned interactive content generation methods, pre-generating interactive content is a key step, involving invoking a generative model to create the content required for the interaction during system idle periods. To effectively address the issues of low content generation efficiency and inability to cover diverse interactive needs, this application further clarifies the specific composition of the generative model. By subdividing generative models into various types, such as image generation models, text generation models, action generation models, and audio generation models, the system, after autonomously determining the interactive content to be generated, can intelligently select and invoke the most suitable dedicated generative model based on the specific form of the content (e.g., image, text, action sequence, or audio). This mechanism ensures that content generation tasks can be completed with maximum efficiency during system idle periods. For example, when generating an image, the system will invoke a dedicated image generation model, rather than a general-purpose model to handle tasks it is not good at; when generating audio, it will invoke an audio generation model. This targeted model invocation not only significantly improves the generation efficiency and quality of various types of content but also enables the system to flexibly respond to various complex interactive scenarios, generating diverse and high-quality interactive content. Therefore, through this meticulous generative model classification and selection mechanism, the premeditated interactive content generation method of this application can make more efficient use of system resources and provide a richer and more human-like interactive experience.

[0071] The following example illustrates this. Suppose an intelligent assistant system predicts that a user's birthday will be in three days. Based on the user's historical preferences and the current context, it autonomously decides to prepare a surprise for the user, including a personalized birthday greeting, a customized birthday card image, and a "Happy Birthday" audio clip. Before the predicted future interaction opportunity (the user's birthday) arrives, the system monitors its resource usage, identifying idle periods during the night when the user is inactive. During these idle periods, the system first uses a text generation model to generate a warm birthday greeting based on the user's personal information and preferences. Then, it uses an image generation model, combining the theme of the greeting text with the user's preferences, to generate a unique birthday card image. Next, it uses an audio generation model to generate a "Happy Birthday" audio clip with the user's name. All this pre-generated content (text, image, audio) is stored. On the user's birthday, the system presents this pre-generated and stored content to the user at an appropriate interaction time, such as by broadcasting the greeting and song via voice and displaying the birthday card image on the screen, thus providing the user with a surprising and personalized birthday experience.

[0072] Through the above technical solution, this application effectively solves the problems of low content generation efficiency and inability to cover diverse interactive needs caused by the lack of clear implementation of the generation model. By introducing specialized models such as image generation models, text generation models, action generation models, or audio generation models, the system can accurately select and call the most suitable generation tool according to the specific form of the content to be generated. This significantly improves the generation efficiency and quality of various interactive content, ensuring that pre-generation tasks can be completed efficiently during system idle periods. At the same time, this diversified generation capability enables the system to provide richer and more expressive interactive content, thereby enhancing the user experience, making the interaction more emotional and human-like, and further optimizing the resource utilization and interactive richness of the pre-generation process.

[0073] In some of the solutions mentioned above in this application, pre-generated interactive content is invoked for interaction in order to present the content at a predicted future interaction time. However, in this process, the invocation operation may result in untimely interaction or presentation errors due to inaccurate timing judgment or content reading delay, affecting the accuracy and smoothness of the user experience.

[0074] In response, this application further proposes to invoke pre-generated interactive content for interaction, including reading the pre-generated content from storage and presenting it to the user when the predicted future interaction time arrives.

[0075] Specifically, this feature optimizes the precise timing of content invocation when the predicted future interaction opportunity arrives. Its purpose is to ensure that interactive content is triggered at the optimal time preset by the system, thus avoiding premature or delayed presentation. Possible implementations include: the system continuously monitors an internal clock or external event trigger, and immediately initiates the subsequent invocation process when the current time matches the predicted future interaction opportunity; or the system can set a scheduled task or event listener, which is activated when the predicted opportunity arrives, thereby precisely executing the content invocation operation. Reading pre-generated content from storage emphasizes the source and method of content retrieval. Its purpose is to directly utilize prepared interactive content stored in a specific location, avoiding the computational latency and resource consumption caused by generating content only when the interaction occurs. Possible implementations include: the system can quickly retrieve and load content from local caches (such as memory or solid-state drives) to achieve extremely low access latency; or the system can read content from distributed storage systems or cloud storage services, ensuring reading efficiency through optimized data transmission protocols and prefetching mechanisms. The feature of presenting the content to the user indicates the final delivery method of the interactive content. Its role is to display pre-prepared content in a user-perceptible manner, completing the closed loop of the entire pre-planned interaction. Possible implementations include: the system can display content on the screen through a graphical user interface (GUI), such as displaying images, text, or playing videos; or the system can play pre-generated speech or music through an audio output device, or drive a robot to complete a preset sequence of actions by controlling actuators.

[0076] This application's solution effectively addresses the inaccurate timing and content retrieval delays that can occur in traditional real-time response models by finely refining the process of calling pre-generated interactive content. Specifically, based on the system's completion of predicting future interaction opportunities, autonomously determining the content to be generated, and pre-generating and storing the content, this solution further ensures the accuracy and smoothness of the interaction. When the system detects that the predicted future interaction opportunity has precisely arrived, it immediately triggers the content retrieval mechanism. At this point, the system no longer needs to consume computing resources for real-time content generation, but instead efficiently retrieves the prepared interactive content directly from the preset storage location. This direct retrieval method greatly shortens the content acquisition time and avoids system lag or response delays caused by real-time generation of complex content. Subsequently, the system quickly and accurately presents this retrieved content to the user, ensuring the timeliness and completeness of the interaction, whether through visual, auditory, or other sensory forms. In this way, the entire pre-planned interaction process is seamlessly connected, with each step from prediction to presentation meticulously planned, thereby significantly improving the accuracy, consistency, and smoothness of the user experience.

[0077] The following is a concrete example. In an agent system, suppose the agent has predicted that the user's wedding anniversary is in three days and has pre-generated a high-resolution image of roses based on the user's historical preferences, caching it locally. On the wedding anniversary, when the system's internal clock precisely indicates that the current time matches the preset future interaction opportunity (e.g., the time when the user first interacts with the agent), the system immediately initiates the content retrieval process. At this point, the agent does not need to regenerate the image but directly and quickly reads the previously generated rose image data from its local cache. After reading, the agent presents the image data to the user through its display interface, simultaneously playing a preset audio blessing. In this way, when interacting with the agent, the user can instantly see and hear the "gift" prepared by the agent, the entire process is smooth and natural, without any noticeable delay.

[0078] Through the above technical solution, this application effectively solves the problem of untimely interaction or incorrect presentation caused by inaccurate timing judgment or content reading delays in invocation operations. By triggering content invocation only when the predicted future interaction time arrives precisely, the accuracy of the interaction is ensured, avoiding the presentation of content too early or too late. Simultaneously, directly reading pre-generated content from storage significantly reduces content retrieval time and eliminates the delays that may arise from instant generation, thereby guaranteeing the immediacy and smoothness of the interaction. Ultimately, presenting content accurately to the user significantly improves the reliability and satisfaction of the user experience, making pre-planned interaction more natural, efficient, and engaging.

[0079] In some embodiments described above, this application proposes to autonomously determine the interactive content to be generated in advance. However, during implementation, the decisions may be inaccurate and cannot be dynamically adjusted based on user feedback, resulting in a poor interactive experience. To address this, this application further proposes to include recording user feedback on the interactive content and optimizing future autonomous determination steps based on that feedback.

[0080] Recording user feedback on the interactive content refers to the system collecting user reaction data after presenting pre-generated interactive content. One implementation is to provide explicit feedback mechanisms on the user interface, such as buttons for "like," "dislike," "useful," and "useless," or allowing users to input text comments and rate the content. Another implementation involves collecting feedback by monitoring implicit user behavior, such as analyzing the duration of user interaction with the interactive content, the number of times the content is repeatedly viewed or played, whether the content is shared with others, and the user's subsequent actions after receiving the content (such as clicking recommended links or completing specific tasks). Furthermore, integrating biometric or emotion recognition technologies, such as analyzing facial expressions and voice tone through a camera, or monitoring physiological indicators like heart rate and skin conductance through wearable devices, can indirectly assess the user's emotional response and satisfaction with the interactive content.

[0081] Optimizing future autonomous content determination steps based on feedback refers to the system using collected user feedback data to adjust its decision-making strategies and parameters when autonomously determining interactive content to be generated in the future. One optimization method is for the system to dynamically adjust the decision rules or parameters for autonomously determining interactive content based on collected feedback data. For example, if a user consistently provides negative feedback on content of a particular style or theme, the system will reduce the priority or weight of that style or theme in future autonomous determination processes. Another optimization method is to use user feedback as training data to continuously retrain or fine-tune the machine learning model used for autonomous content determination. By continuously learning user preferences and contextual relationships, the model can more accurately predict the types, forms, and presentation times of content that users may be interested in. Furthermore, by establishing user preference profiles and updating user interest tags, content preference weights, and other information in real time based on feedback, the system can more accurately match users' personalized needs in the next autonomous content determination.

[0082] This application's solution achieves continuous optimization of the autonomously determined steps by introducing a feedback learning mechanism into the pre-planned interactive content generation process. Specifically, after the system predicts future interaction opportunities, autonomously determines the interactive content to be generated, pre-generates and stores the content, and invokes the pre-generated interactive content for interaction when the opportunity arises, the system further records user feedback on this interactive content. This feedback data, whether explicit user evaluations or implicit behavioral patterns, provides valuable learning samples for the system. Subsequently, the system adjusts and optimizes its strategy for autonomously determining future interactive content based on this collected feedback. For example, if users show a positive response to a certain type or style of content, the system will tend to generate similar content in subsequent autonomous determination processes; conversely, if the feedback is poor, the system will adjust its strategy to avoid or improve the generation of related content. This mechanism enables the system to learn from each interaction with users, continuously improving the accuracy, personalization, and user satisfaction of autonomously determined content, thereby transforming pre-planned interaction from a static decision-making process into a dynamic, adaptive, and continuously evolving intelligent process. This effectively solves the problem of potential biases in initial decision-making and significantly enhances the anthropomorphism and emotional connection of the interaction.

[0083] As a specific implementation method, an intelligent agent system can be considered. This system predicts that the user's wedding anniversary is in three days and, based on the user's historical preferences (e.g., the user has repeatedly mentioned liking roses) and the current season (spring), autonomously determines to prepare a "rose image" as a gift. During the early morning hours when the user is asleep and system resources are idle, the agent calls a local image generation model to pre-generate a high-resolution rose image and caches it locally. On the anniversary, when the user interacts with the agent, the agent proactively sends the pre-generated rose image along with a blessing: "Happy Anniversary! I prepared this rose for you in advance; I hope you like it." At this time, the system records the user's feedback on this interaction. For example, the user might click a "like" button or express "I like it!" via voice. The system records this positive feedback data. Based on this feedback, in similar future scenarios, such as the next anniversary or birthday, when autonomously determining the content to be generated, the system will prioritize generating image-based content, or specifically rose-themed images, because historical data shows that users have a positive preference for such content. Conversely, if a user shows negative feedback on pre-generated content (e.g., clicking "dislike" or remaining inactive for an extended period), the system will record the negative feedback and reduce the priority of generating such content in future self-determined steps, instead trying other forms or themes. This continuously optimizes its self-determined strategy, making future pre-planned interactions more aligned with user needs.

[0084] Through the aforementioned technical solution, the system can obtain real-time user feedback on pre-planned interactive content, thus avoiding the blindness of autonomously determining steps. Based on this feedback, the system can dynamically adjust and optimize its strategy for autonomously determining the interactive content to be generated, making future interactive content more accurately match user preferences and contextual needs. This significantly improves the personalization of the interaction and user satisfaction, transforming static pre-planned decision-making into a continuous learning and evolutionary process, ultimately achieving a more natural and emotionally resonant interactive experience. It effectively solves the problems of potentially inaccurate decision-making and the inability to dynamically adjust based on user feedback, leading to poor interactive experiences.

[0085] In some of the solutions mentioned above in this application, interactive content is proposed to be pre-generated and presented when it is called up in the future. However, in this process, the specific form of the interactive content is not specified, which may lead to the content generation being monotonous and unable to effectively cover diverse media types such as images, text, audio or action sequences. This limits the richness of the interaction, the depth of emotional expression and the applicability of the system, and affects the user experience and resource optimization effect.

[0086] In this regard, this application further clarifies that the interactive content includes, but is not limited to, images, text, audio, or action sequences.

[0087] Specifically, interactive content refers to the information carriers presented by the system to the user or received by the user during human-computer interaction. It serves as the medium for information transmission, emotional exchange, and functional operation between the system and the user. Images refer to static or dynamic visual information received through visual senses, and their implementation can include, but is not limited to, photographs, illustrations, charts, or animation frames. For example, a system can generate a personalized birthday card image or a diagram displaying a weather forecast. Text refers to linguistic information composed of characters, words, sentences, etc., and its implementation can include, but is not limited to, short messages, long reports, dialogue content, or news summaries. For example, a system can generate a warm greeting or a detailed product description. Audio refers to sound information received through auditory senses, and its implementation can include, but is not limited to, speech, music, or sound effects. For example, a system can generate personalized background music or a news briefing delivered via voice. Action sequences refer to a series of preset or generated dynamic behaviors or posture changes, and their implementation can include, but is not limited to, specific actions performed by a robot, facial expressions of a virtual avatar, or behavioral patterns of an animated character. For example, a service robot can perform a dance to welcome users, or a virtual assistant can display an animated expression of approval. By using the phrase "but not limited to," this application maintains the flexibility of the system, allowing for future expansion to other unlisted content formats and avoiding the rigidity of the technical solution.

[0088] This application's solution explicitly allows interactive content to encompass various forms such as images, text, audio, or action sequences. This enables the system to autonomously determine the interactive content to be generated after predicting future interaction opportunities. The system can select the most suitable media type based on factors such as the characteristics of the predicted opportunity, user historical preferences, current context, or relationship parameters. For example, for a holiday greeting requiring visual presentation, the system will generate an image; for a scenario requiring information transmission, it will generate text or audio; and for an interaction requiring the participation of a physical robot, it will generate an action sequence. Once the content type is determined, in the step of pre-generating and storing the interactive content, the system can selectively call the corresponding generation model (such as an image generation model, text generation model, audio generation model, or action generation model) to create specific content. When the future interaction opportunity arrives, the system calls the pre-generated interactive content for interaction, presenting the user with pre-prepared content in various formats. This allows the autonomous determination step to make more refined decisions and guides the pre-generation step to call appropriate generation mechanisms, thereby significantly improving the flexibility and expressiveness of the entire pre-planned interaction method.

[0089] The following example illustrates this. In a smart home system, the system analyzes user behavior patterns to predict that the user will wake up at 7:00 AM the next morning. The system autonomously determines the interactive content to be generated, considering that users typically prefer to receive weather information and relaxing music in the morning, and if a smart display screen is present, they will prefer a visual greeting. Therefore, the system decides to generate text containing the current weather information, an image matching the weather context, and soft background music audio. At 2:00 AM, when system resources are idle, the system uses a text generation model to generate the weather forecast text, an image generation model to generate a weather icon and background image, and an audio generation model to generate soothing music. All generated content is pre-stored. The next morning at 7:00 AM, when the user wakes up, the smart home system automatically displays the pre-generated weather image and text on the smart display screen and plays the pre-generated background music through the speakers, providing the user with a multimodal morning greeting.

[0090] Through the aforementioned technical solutions, the system can flexibly select and generate diverse interactive content, including images, text, audio, or action sequences, based on different interaction needs and scenarios. This solves the problem of monotonous content generation, greatly enriching the expressive forms and depth of interaction, enabling the system to provide a more human-like and emotionally resonant interactive experience. Simultaneously, this diversified content pre-generation allows the system to make fuller use of idle resources, avoiding resource contention and response delays caused by generating complex multimedia content during interaction, thereby improving user experience and overall system efficiency.

[0091] Traditional interactive systems generate content only when a user initiates an interaction, resulting in technical problems such as a lack of premeditation in immediate responses, response delays due to resource contention, and weak emotional connections. To address this, this application provides a premeditated interactive content generation system, including an event prediction module, a premeditated decision-making module, a pre-generation module, and a triggering module. The event prediction module predicts potential future interaction opportunities, such as potential interaction points identified based on calendar events, user behavior patterns, or external data sources. Before the predicted future interaction opportunity, the premeditated decision-making module autonomously determines the interactive content to be generated based on the user's historical preferences and the current context. The pre-generation module monitors system resource status, identifies idle periods, and calls the content generation model to pre-generate interactive content during these periods and stores it in a cache. When the predicted future interaction opportunity arrives, the triggering module retrieves the pre-generated interactive content from the cache for presentation.

[0092] The core innovation of this embodiment lies in its systematic combination of predicting future interaction opportunities with autonomous decision-making, and the introduction of a pre-generation mechanism for idle time slots. This enables a technical approach that proactively plans and prepares content before user interaction. Specifically, the event prediction module allows the system to identify interaction needs in advance, avoiding the mechanical nature of passive responses; the pre-planning decision-making module generates personalized content based on users' historical preferences, enhancing emotional depth; the pre-generation module utilizes idle system resources for content preprocessing, effectively avoiding resource contention during peak interaction periods; and the triggering module ensures seamless content presentation at the appropriate time, optimizing response speed. Because the system completes content generation during idle time slots, there is no need to immediately call upon computing resources during actual interaction, thus significantly reducing response latency.

[0093] Taking the pre-generation of smart home morning briefings as an example, the event prediction module predicts the timing of information pushes at 7:30 AM the next morning by analyzing user behavior patterns; the pre-planning decision-making module determines the generation of news summaries and weather forecast audio based on user habits; the pre-generation module calls text and speech generation models to pre-generate content and store it when the system load is low at 2:00 AM; and the triggering module directly calls the pre-generated audio for playback when the user wakes up at 7:30 AM. Through the above technical solutions, the system exhibits "pre-planned" behavioral characteristics, which not only enhances the anthropomorphism of the interaction but also improves system efficiency through smooth resource utilization, creating a timely and smooth experience for users.

[0094] Through the above technical solution, the interactive system realizes a complete closed-loop mechanism of prediction-decision-pre-generation-triggering, effectively solving the problems of lack of planning, resource constraints, and weak emotional connection in existing technologies. Compared with the traditional instant response mode, this embodiment has significant advantages in terms of interactive initiative, resource utilization efficiency, and depth of emotional connection, providing users with a warmer and more surprising interactive experience, while optimizing the overall system performance.

[0095] In some of the solutions mentioned above in this application, a pre-planning decision-making module is proposed to autonomously determine the interactive content to be generated. However, in its implementation, the decision-making strategy lacks dynamic optimization based on user feedback, which may result in the interactive content not conforming to the user's current preferences, affecting the personalization effect and emotional connection.

[0096] In this regard, this application further proposes that, based on the aforementioned premeditated interactive content generation system (including an event prediction module, a premeditated decision-making module, a pre-generation module, and a triggering module), a feedback learning module is also included, which is used to record user feedback on interactive content and optimize the decision-making strategy of the premeditated decision-making module.

[0097] The feedback learning module is a component specifically designed to collect, process, and analyze user responses to system-generated interactive content. This module can be a standalone software module integrated into the system, responsible for monitoring user interface events such as clicks, swipes, and voice commands, or analyzing user behavior data such as dwell time and replay counts. Alternatively, the feedback learning module can be a machine learning model-based component, trained to identify patterns in user feedback, such as using sentiment analysis models to identify user satisfaction with text or voice content. This module provides the system with quantitative or qualitative data on the effectiveness of interactive content, forming the foundation for optimizing decision-making strategies.

[0098] Recording user feedback on the interactive content aims to obtain direct or indirect user evaluations of the premeditated interactive content, providing data support for subsequent decision-making optimization. This can be achieved in several ways. On the one hand, explicit feedback mechanisms can be used, such as providing "like / dislike" buttons, satisfaction rating stars, or comment input boxes on the interactive interface, allowing users to actively express their preferences. On the other hand, implicit feedback mechanisms can also be used, such as monitoring user interaction behavior with the content, such as content playback duration, number of repeated views, whether it is shared, whether subsequent actions are taken (such as clicking links, purchasing products), and even inferring user emotions through facial expression recognition or voice tone analysis.

[0099] Optimizing the decision-making strategy of the pre-planning decision-making module involves adjusting its behavioral logic and parameters based on user feedback to generate more user-friendly and engaging interactive content in the future. This optimization can be rule-based; for example, if users repeatedly provide negative feedback on a certain type of interactive content (such as images of a specific style), the generation priority of that type of content can be reduced or its generation parameters adjusted in future decisions. Alternatively, optimization can be based on machine learning. For instance, user feedback can be used as training data, and methods such as reinforcement learning, supervised learning, or semi-supervised learning can be used to iteratively update the recommendation algorithm or decision-making model within the pre-planning decision-making module, enabling it to learn more refined user preference patterns.

[0100] Building upon the aforementioned premeditated interactive content generation system (including an event prediction module, a premeditated decision-making module, a pre-generation module, and a triggering module), this application further introduces a feedback learning module. When the triggering module invokes pre-generated interactive content for interaction and presents it to the user, the feedback learning module begins to function, recording the user's feedback on this interactive content in real-time or asynchronously. This feedback data is then processed and analyzed by the feedback learning module to form an evaluation of the effectiveness of the decisions made by the premeditated decision-making module. Based on this evaluation, the feedback learning module provides optimization instructions to the premeditated decision-making module or updates its internal parameters, thereby dynamically adjusting the strategy of the premeditated decision-making module when autonomously determining the interactive content to be generated in the future. For example, if a user shows high interest in text content on a specific topic, the feedback learning module will prompt the premeditated decision-making module to prioritize generating text content on that topic in similar situations. This closed-loop mechanism enables the entire premeditated interactive content generation system to continuously learn and adapt to the user's dynamic preferences, thereby enhancing the personalization and emotional connection of the interaction.

[0101] As a specific implementation method, the scenario of planning an anniversary gift can be used as an example. In the agent system, the agent predicts that the user's wedding anniversary is in three days through the event prediction module. The planning decision module then decides to prepare a "rose image" as a gift based on the user's historical preferences and the current season. The pre-generation module calls the image generation model to pre-generate and store the rose image during system idle periods. On the anniversary, the trigger module causes the agent to actively send the pre-generated rose image along with a blessing. If the user shows explicit positive feedback ("surprise like") to this interaction, the feedback learning module immediately records this feedback event. The feedback learning module analyzes this feedback, for example, identifying the user's preferences for "anniversary gift" and "image format." Subsequently, the feedback learning module passes these analysis results to the planning decision module to optimize its decision-making strategy. Specifically, the internal algorithm or model of the planning decision module adjusts its weights or parameters based on this positive feedback, so that when similar "anniversary" or "gift-giving" interaction opportunities arise in the future, the priority or inclination to generate an "image" as interaction content will be increased. Conversely, if a user exhibits negative feedback to a pre-planned interaction (e.g., product information recommended by the agent on a non-anniversary occasion), such as quickly closing or ignoring it, the feedback learning module records this implicit negative feedback and adjusts the strategy of the pre-planning decision-making module accordingly to avoid generating similar content that the user is not interested in in the future. This continuous feedback loop ensures that the agent can continuously learn the user's true preferences, making its pre-planned interactions increasingly accurate and personalized.

[0102] Through the above technical solution, this application effectively solves the problem that the decision-making strategy of the pre-planning decision-making module lacks dynamic optimization based on user feedback, which may lead to interactive content that does not conform to the user's current preferences, affecting personalization and emotional connection. The introduction of the feedback learning module enables the system to continuously collect and utilize user feedback information on interactive content, thereby dynamically adjusting and optimizing the decision-making strategy of the pre-planning decision-making module. This significantly improves the personalization and accuracy of the interactive content generated by the system, ensuring that future pre-planned content can more accurately match the user's actual preferences and needs. Ultimately, the solution of this application not only enhances the emotional connection between the user and the system, making the interaction warmer and more human-like, but also further improves the overall satisfaction and user stickiness of the interactive experience through continuous learning and adaptation.

[0103] In some of the solutions mentioned above in this application, a pre-generation module is proposed to generate interactive content in advance during idle periods. However, in this process, the system may not be able to efficiently generate various forms of interactive content, resulting in insufficient resource utilization, low generation efficiency, and affecting user experience and interaction diversity.

[0104] In this regard, this application further proposes that the pre-generation module includes, but is not limited to, an image generation unit, a text generation unit, an action generation unit, or an audio generation unit.

[0105] The pre-generation module is a core component of the pre-planned interactive content generation system 9. Its main function is to pre-generate interactive content during system idle periods before future interaction opportunities and store it for later retrieval. By shifting content generation tasks from immediate interaction periods to system idle periods, this module effectively avoids resource contention and response delays, thereby improving the user experience. It can be implemented through a central scheduler managing multiple content generation services or through a unified service interface integrating various generation capabilities. The image generation unit is a dedicated component within the pre-generation module, responsible for generating interactive content in various visual forms, such as images, charts, animation frames, or visual effects. Its role is to provide users with intuitive and rich visual information, enhancing the attractiveness of interaction and the efficiency of information delivery. This unit can generate images based on deep learning models (such as Generative Adversarial Networks (GANs) or Diffusion Models) according to text descriptions or specific contexts, or it can render images by calling preset graphics libraries or templates, or generate 3D models or scenes using computer graphics technology. The text generation unit, another specialized component within the pre-generation module, is responsible for generating interactive content in various text formats, such as greetings, news summaries, blessings, reports, or dialogue replies. Its role is to provide clear and accurate textual information, support language-based interaction, and convey emotional and personalized information. This unit can perform natural language generation based on large language models (LLMs), generating coherent and meaningful text according to the input context and user preferences. It can also generate structured or semi-structured text content through template filling, rule matching, and other methods. The motion generation unit, another specialized component within the pre-generation module, is responsible for generating various motion sequences or behavioral patterns, such as robot limb movements, virtual character animations, dynamic effects of interface elements, or operation instructions. Its role is to enhance the vividness, anthropomorphism, and usability of interactions through dynamic behavior, especially suitable for physical robots or virtual assistants. This unit can perform motion synthesis based on motion capture data, control multi-joint robot movements through inverse kinematics algorithms, or utilize reinforcement learning to train models to generate motion sequences that conform to specific tasks or emotional expressions. The audio generation unit is a dedicated component within the pre-generation module, responsible for generating interactive content in various audio formats, such as speech synthesis, background music, sound effects, or ambient sounds. Its role is to enrich the interactive experience through auditory information, convey emotions, or provide non-visual cues and feedback. This unit can convert text content into natural speech based on text-to-speech (TTS) technology, generate background music or melodies through a music synthesizer, or generate specific ambient sounds or cue sounds using sound effect libraries and audio processing algorithms.

[0106] The pre-generation module, comprising various specialized generation units such as image generation, text generation, action generation, and audio generation, enables the pre-planned interactive content generation system 9 to efficiently and flexibly handle different types of interactive content generation tasks. When the pre-planning decision-making module autonomously determines the interactive content to be generated based on predicted future interaction opportunities, it not only determines the theme and intent of the interactive content but also specifies the required content format. At this point, the pre-generation module can distribute specific generation tasks to the corresponding specialized generation units according to the instructions of the pre-planning decision-making module. For example, if visual content needs to be generated, the image generation unit is responsible; if language content needs to be generated, the text generation unit is responsible; if dynamic behavior needs to be generated, the action generation unit is responsible; and if auditory content needs to be generated, the audio generation unit is responsible. This modular design allows each generation unit to focus on its area of ​​expertise, thereby optimizing the generation algorithm and resource allocation. During system idle periods, these specialized units work in parallel or sequentially, efficiently completing their respective content generation tasks and storing the generated results uniformly in the pre-generation module. This collaborative working mechanism not only ensures that the pre-generation module can handle diverse interactive content needs, but also significantly improves the efficiency and quality of content generation through specialized division of labor. It avoids the performance bottleneck of a single general-purpose generator when dealing with complex multimodal content, thus effectively solving the problem that the system cannot efficiently generate various forms of interactive content.

[0107] The following example illustrates this concept. In a smart home system, the event prediction module predicts that the user will wake up at 7:30 AM the next morning and habitually receive a personalized morning briefing during breakfast. The pre-planning decision-making module, based on the user's historical preferences and the current context, decides to prepare a briefing for the user that includes a weather forecast image, a summary of the day's news text, relaxing background music, and an animation of a virtual assistant delivering the news. At 2:00 AM, during a low-load period, the pre-generation module begins its work. Specifically, the image generation unit generates an image showing the weather trend for the next 24 hours based on weather data; the text generation unit uses a large language model to generate a concise news summary based on the day's hot news topics; the audio generation unit synthesizes soft background music and uses text-to-speech technology to convert the news summary into natural speech; and the motion generation unit generates lip-sync and body movement sequences for the virtual assistant to deliver the news. All of this pre-generated content, including images, text, audio files, and motion sequence data, is stored in the pre-generation module's cache. When the future interaction opportunity arrives at 7:30 a.m. the next morning, the triggering module directly retrieves these pre-generated contents from the cache and integrates them to present to the user, such as displaying weather pictures and virtual assistant animations on the smart screen, while playing news broadcast audio and background music.

[0108] Through the aforementioned technical solution, the pre-generation module can efficiently generate various forms of interactive content based on the instructions of the pre-planning decision-making module. This modular design allows the system to call specialized generation units for different types of content (such as images, text, actions, and audio), thus avoiding the inefficiency of a single general-purpose generator when handling multimodal content. During system idle periods, the generation units can work in parallel or sequentially, making full use of system resources to ensure that the pre-prepared, diverse content can be presented quickly and smoothly when the predicted future interaction opportunity arrives. This not only significantly improves the efficiency and quality of content generation and enriches the forms of interaction, but also enhances the personalization and immersion of the user experience, making pre-planned interactions more human-like and emotionally resonant.

[0109] In some of the solutions mentioned above in this application, a pre-generation module is proposed to pre-generate content during idle periods. However, in its implementation, without an effective resource monitoring mechanism, the system may not be able to accurately identify idle periods, causing the pre-generation operation to be performed during non-idle periods, resulting in resource contention, response delays, and a decrease in system efficiency.

[0110] In this regard, this application further proposes that the premeditated interactive content generation system also includes a resource monitoring module for monitoring system idle periods.

[0111] The resource monitoring module is responsible for collecting and analyzing the usage of various system resources. This module can be a standalone software service, obtaining real-time resource data such as CPU, memory, disk I / O, and network bandwidth through APIs provided by the operating system; alternatively, it can be a functional module embedded in the core system components, directly accessing hardware registers or low-level drivers to obtain more granular resource usage information. The resource monitoring module monitors system idle periods, ensuring that pre-generated tasks can execute without impacting system performance and user experience. Specifically, this module can determine system idleness by setting predefined resource utilization thresholds, such as when CPU utilization is consistently below a certain percentage or available memory is above a certain percentage; alternatively, it can combine user behavior patterns and preset time windows, such as during nighttime when users are typically inactive or during specific periods of low system load, further integrating resource utilization data to confirm idle status; or it can employ a predictive model based on historical data analysis to learn the periodic patterns of system resource usage and user behavior habits, thereby predicting future idle periods.

[0112] This application's solution introduces a resource monitoring module, enabling the pre-planned interactive content generation system to intelligently schedule resource-intensive pre-generation tasks. The resource monitoring module continuously or periodically collects usage data of various system resources (e.g., CPU, memory, GPU, network bandwidth), and analyzes this data based on preset strategies or models to identify idle periods when the system is under low load. Once a suitable idle period is identified, the resource monitoring module issues instructions or provides idle period information to the pre-generation module (which pre-generates and stores interactive content), allowing the pre-generation module to start or continue its content generation tasks during this period. This mechanism ensures that the pre-generation module can work efficiently without competing for resources with users in real time, thereby optimizing the overall system's resource utilization efficiency. In this way, the pre-generation module no longer blindly executes tasks but intelligently schedules tasks based on the system's current "health" status, significantly improving the stability and response speed of the pre-planned interactive content generation system.

[0113] The following is a concrete example. In an intelligent in-vehicle system, the system needs to pre-generate a series of personalized podcasts and high-resolution map data for a user's upcoming long-distance drive. The resource monitoring module continuously monitors the in-vehicle system's CPU load, memory usage, and network bandwidth consumption. When the vehicle is charging and the user has left the vehicle (e.g., at night), and the resource monitoring module detects that the CPU load is consistently below 15%, the memory idle rate is above 60%, and there is sufficient network bandwidth, it marks this period as an idle period. Subsequently, the resource monitoring module notifies the pre-generation module, allowing it to invoke the generation model, download and process the podcast content, and pre-render map data during this idle period.

[0114] Through the above technical solution, this application effectively solves the problems of resource contention and response delay that may occur when the pre-generation module lacks an effective resource monitoring mechanism. The introduction of the resource monitoring module enables the system to proactively and intelligently identify and utilize idle periods for content pre-generation, avoiding resource-intensive operations during peak user activity times, thereby significantly reducing system response latency and improving the smoothness of the user experience. Simultaneously, by optimizing resource allocation, the overall system efficiency is improved, idle resources are effectively utilized, and the pre-planned interactive content generation process becomes smoother and more efficient.

[0115] In some of the solutions mentioned above in this application, a resource monitoring module is proposed to monitor the idle periods of the system. However, in its implementation, how to accurately identify the idle periods to ensure that the pre-generated operations are executed when the system is truly idle, thereby avoiding resource waste and system interference, is a problem that needs to be solved.

[0116] In response, this application further proposes that the resource monitoring module be configured to identify idle periods based on the system's CPU, memory, or GPU usage, and the pre-generation module perform pre-generation during the idle periods.

[0117] The resource monitoring module is responsible for real-time monitoring of system hardware resource usage. Its function is to provide objective, real-time system load information as a basis for determining whether the system is in an "idle" state. This module can utilize operating system APIs, such as reading the ` / proc / stat` file in Linux to obtain CPU usage, or using `Performance Counters` in Windows to obtain memory usage data. Alternatively, it can integrate a dedicated hardware monitoring chip or software agent to directly read load data from hardware sensors. The pre-generation module is responsible for actually calling the generative model to create interactive content. Its function is to utilize idle periods identified by the resource monitoring module to execute time-consuming content generation tasks, thereby avoiding resource consumption during busy system periods. The pre-generation module can receive "idle signals" from the resource monitoring module; upon receiving this signal, it starts the content generation task. Alternatively, the pre-generation module can periodically query the current status of the resource monitoring module, and once it detects that the system is in an idle state, it begins executing the pre-generation task.

[0118] The premeditated interactive content generation system of this application comprises an event prediction module for predicting future interaction opportunities, a premeditated decision-making module for autonomously determining the interactive content to be generated before the future interaction opportunity, a pre-generation module for pre-generating and storing the interactive content, and a triggering module for invoking the pre-generated interactive content to perform the interaction when the future interaction opportunity arrives. Based on this, a resource monitoring module continuously or periodically monitors the utilization rate of the system's CPU, memory, or GPU, which are key indicators for measuring system load. By acquiring this data, the resource monitoring module can dynamically and objectively determine the current busy level of the system. When the utilization rate of these key resources is lower than a preset threshold, the resource monitoring module identifies that the system is in an idle period. The pre-generation module no longer blindly executes pre-generation tasks but works closely with the resource monitoring module. It receives or queries the idle period information identified by the resource monitoring module. Once it confirms that the system is in an idle state, the pre-generation module initiates the operation of pre-generating interactive content. This collaborative working mechanism ensures that time-consuming content generation tasks are only performed during periods when system resources are sufficient and the impact on user experience is minimal. It avoids the pre-generated tasks competing for CPU, memory, or GPU resources with these tasks when users interact or the system executes high-priority tasks, thus effectively solving the problems of resource contention and response latency in the traditional real-time response mode. In this way, the solution of this application not only realizes pre-planned interaction, but also optimizes resource scheduling, minimizing the impact of the pre-generation process on system performance and improving the overall system efficiency and user experience.

[0119] As a specific implementation, in a smart home system, the resource monitoring module can be a background service running on the smart home hub (such as a smart speaker or home server). This service continuously monitors the CPU load, memory usage, and GPU (if present) usage of the smart home hub. For example, when the CPU usage is below 10% for five consecutive minutes and the memory idle rate is above 70%, the resource monitoring module sends a "system idle" signal to the pre-generation module. Upon receiving this signal, the pre-generation module initiates pre-generation tasks. For instance, between 2:00 AM and 4:00 AM, when the smart home hub is in a deep idle state, the pre-generation module calls the text generation model and speech synthesis model to generate audio files for the next morning's news summary and weather forecast. These generation tasks make full use of the idle CPU and memory resources at this time without affecting the user's response speed when using other smart home functions during the day. When the user wakes up at 7:30 AM, the trigger module plays the pre-generated audio briefing, providing a smooth user experience without any delay.

[0120] By configuring the resource monitoring module to identify idle periods based on system CPU, memory, or GPU usage, this application's solution can accurately and dynamically determine system resource status, avoiding the limitations of judging idle periods based on fixed time windows or simple rules. The pre-generation module performs pre-generation during the identified idle periods, ensuring that time-consuming content generation tasks are performed when system load is lowest. This effectively avoids contention for critical computing resources (such as CPU, memory, and GPU) during busy system periods, such as user interactions or other high-priority tasks, thus significantly reducing system response latency and improving user experience smoothness. This precise resource scheduling mechanism makes the pre-planned interactive content generation process more efficient and resource-friendly, not only solving the problem of resource waste and system interference caused by inaccurate idle period identification but also further optimizing the performance and stability of the entire pre-planned interactive system.

[0121] In some of the solutions mentioned above in this application, a premeditated interactive content generation method is proposed to address the problems of instant response, resource scarcity, and weak emotional connection. However, when implementing this method, a storable, distributable, and executable medium is needed to ensure the deployment, persistence, and cross-device application of the method; otherwise, the method will be difficult to implement and promote effectively in real-world systems.

[0122] In response, this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements a premeditated interactive content generation method.

[0123] Computer-readable storage media refers to physical or non-physical carriers capable of storing digital data and readable by a computer system. As a specific implementation, this medium can be a non-transitory storage medium, such as a hard disk drive (HDD), a solid-state drive (SSD), flash memory (e.g., USB flash drive, SD card), optical disc (e.g., CD-ROM, DVD), read-only memory (ROM), or programmable read-only memory (PROM). These media provide persistent storage space, enabling computer programs to be preserved, distributed, and loaded for extended periods. Alternatively, this medium can also be network storage space, accessed and stored via network protocols.

[0124] A computer program is a set of instructions that, when executed by a processor, can perform a specific task or achieve a specific function. This program encapsulates the logic and algorithm of a premeditated interactive content generation method and serves as the carrier for executing that method. Specifically, the program can exist in the form of compiled executable code, such as a binary file, which can run directly on the operating system; or it can be interpreted script code, such as Python or JavaScript code, which requires a corresponding interpreter to run.

[0125] The processor is a hardware unit capable of executing computer program instructions and is the core computing component of a computer system. It is responsible for reading and executing instructions from the computer program, driving the various steps of the premeditated interactive content generation method. As a specific implementation, the processor can be a central processing unit (CPU), such as an Intel Core or AMD Ryzen processor in a personal computer, or an ARM Cortex processor in an embedded system. Alternatively, in scenarios requiring extensive parallel computing (such as content generation models), the processor can also be a graphics processing unit (GPU) as the primary execution unit.

[0126] When the program is executed by the processor, it implements a pre-planned interactive content generation method. This means that the processor, based on program instructions stored on the medium, executes a complete process of predicting future interaction opportunities, autonomously determining the interactive content to be generated, pre-generating and storing the interactive content, and invoking the pre-generated interactive content to perform the interaction when the future interaction opportunity arrives.

[0127] This application addresses the practicality issues of deploying and executing premeditated interaction methods by providing a computer-readable storage medium, ensuring that the method can be persistently stored, distributed, and run on various devices. Specifically, the computer-readable storage medium, as a physical or digital carrier, allows the method code to be stored and transmitted long-term, solving the deployment difficulties caused by the lack of method persistence; the computer program stored on it contains executable code that implements the premeditated interaction content generation method, ensuring that the method logic can be loaded and executed; when the program is executed by a processor, it implements a complete process of predicting future interaction opportunities, autonomously determining the content to be generated, pre-generating content during idle periods, and invoking the content for interaction when the opportunity arises, thereby optimizing resource utilization, improving response speed, and enhancing emotional connection. Through the combination of storage medium and processor, this method can be flexibly applied in scenarios such as industrial equipment and smart homes.

[0128] The following is a concrete example. In a smart home system, its core controller (such as a smart speaker or smart gateway) integrates an embedded flash memory as a computer-readable storage medium. This flash memory contains a computer program that implements a pre-planned interactive content generation method, either pre-installed or downloaded and stored over a network. A low-power processor configured inside the smart home controller is responsible for loading and executing this computer program. When the smart home controller starts up, the processor begins executing program instructions. For example, the program might predict that the user typically wakes up at 7:00 AM based on their daily routine and decide to play a personalized news briefing upon waking, based on the user's historical preferences. During the system's idle period at 3:00 AM, the processor, according to program instructions, calls the text generation model and speech synthesis model to pre-generate and store the audio file of the news briefing. When the user wakes up at 7:00 AM the next morning, the processor, according to program instructions, reads the pre-generated audio file from the flash memory and plays it to the user through the smart speaker.

[0129] Through the aforementioned technical solutions, the premeditated interactive content generation method can be persistently stored, conveniently distributed, and reliably executed. This allows the method to be transformed from a theoretical concept into a practically operable system, leveraging its advantages in various smart devices and application scenarios. For example, in smart homes, in-vehicle systems, and service robots, the method can be deployed on the device's local storage medium and executed by the device's own processor to achieve localized premeditated interaction. This not only solves the practicality issues of method deployment and execution, but more importantly, it enables the anthropomorphic interaction, smooth resource utilization, surprise creation, and personalized optimization effects brought about by the aforementioned premeditated interactive content generation method to be truly realized, greatly improving user experience and system efficiency.

[0130] Other application scenarios The following simplified embodiments illustrate the application of the present invention in other fields. Specific implementations of these embodiments can be found in the examples described in the detailed embodiments above, and will not be repeated here.

[0131] Simplified Example 1: Predictive Maintenance of Industrial Equipment In an industrial production line, the system predicts that a critical piece of equipment will reach its maintenance cycle in two days. During nighttime production line downtime, the system pre-generates maintenance guidance images, spare parts lists, and operating instructions, and pushes these to maintenance personnel's terminals. On the day of maintenance, staff can directly access the pre-generated content without waiting for it to be generated, thus improving efficiency.

[0132] Simplified Example 2: Course Preview Generation on an Online Education Platform (Education Sector) The online education platform predicts which module users will study next week based on their learning progress. During weekends when server load is lower, the platform pre-generates course videos, exercises, and interactive content for this module. When users log in on Monday, the course is ready, eliminating the need to wait for generation and improving the learning experience.

[0133] Simplified Example 3: Pre-generation of promotional content for e-commerce platforms (e-commerce field) E-commerce platforms anticipated the upcoming "Double Eleven" shopping festival. A week before the event, they pre-generated a large number of product recommendation images, personalized coupons, and promotional copy during off-peak nighttime hours. On the day of the event, users could directly access the pre-generated content, avoiding peak-hour computing resource pressure.

[0134] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0135] This solution is applicable not only to local devices but can also be deployed on cloud servers. Those skilled in the art should understand that the technical solution of this invention is not limited to a specific deployment environment, and any implementation based on the technical concept of this application should be considered to fall within the protection scope of this invention.

Claims

1. A method for generating premeditated interactive content, characterized in that, Includes the following steps: Predict future interaction opportunities; Before the future interaction opportunity, autonomously determine the interaction content to be generated; The interactive content is generated and stored in advance; When the future interaction opportunity arrives, the pre-generated interaction content will be invoked to perform the interaction.

2. The method according to claim 1, characterized in that, The prediction of future interaction opportunities includes, but is not limited to, predictions based on calendar events, user behavior patterns, or periodic needs.

3. The method according to claim 1, characterized in that, The autonomous determination of the interactive content to be generated includes, but is not limited to, based on user historical preferences, current context, or relationship parameters.

4. The method according to claim 1, characterized in that, The pre-generated interactive content includes monitoring system resource usage status, identifying system idle periods, and calling a generation model during the idle periods. The generation model is used to generate any form of content required for the interaction.

5. The method according to claim 4, characterized in that, The generative models include, but are not limited to, image generation models, text generation models, action generation models, or audio generation models.

6. The method according to claim 1, characterized in that, The act of invoking pre-generated interactive content includes reading the pre-generated content from storage and presenting it to the user when the predicted future interaction time arrives.

7. The method according to claim 1, characterized in that, It also includes recording user feedback on the interactive content and optimizing future autonomous decision-making steps based on the feedback.

8. The method according to any one of claims 1 to 7, characterized in that, The interactive content includes, but is not limited to, images, text, audio, or action sequences.

9. A premeditated interactive content generation system, characterized in that, include: The event prediction module is used to predict future interaction opportunities; The pre-planning decision-making module is used to autonomously determine the interaction content to be generated before the future interaction opportunity. The pre-generation module is used to pre-generate and store the interactive content; The trigger module is used to invoke pre-generated interactive content to perform the interaction when the future interaction opportunity arrives.

10. The system according to claim 9, characterized in that, It also includes a feedback learning module, which records user feedback on the interactive content and optimizes the decision-making strategy of the planning module.

11. The system according to claim 9, characterized in that, The pre-generation module includes, but is not limited to, an image generation unit, a text generation unit, an action generation unit, or an audio generation unit.

12. The system according to claim 9, characterized in that, It also includes a resource monitoring module, which is used to monitor the system's idle periods.

13. The system according to claim 12, characterized in that, The resource monitoring module is configured to identify idle periods based on the system's CPU, memory, or GPU usage, and the pre-generation module performs pre-generation during the idle periods.

14. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1 to 8.

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