Multi-source data-based selling point generation method for AI intelligent marketing

Abstract

The invention discloses a multi-source data-based selling point generation method for AI intelligent marketing, and relates to the technical field of big data analysis and application, and the method comprises the following steps: obtaining a unified time scale matrix in a multi-source data access stage, building an extreme value inhibition prior based on the unified time scale matrix, and building an acquisition synchronization baseline; full-link time alignment is realized by collecting a synchronous base line; and acquiring an extreme value diagnosis fingerprint under the constraint of full-link time alignment, generating a threshold track based on a quantile residual spectrum and kurtosis skewness, and capturing and calibrating a burst extreme value by taking the threshold track as a dynamic reference. According to the method, a time sequence consistency basis is constructed, a data extreme value is accurately identified and corrected, the content is ensured to be true and credible in combination with causal correction and semantic screening, dynamic closed-loop optimization of content generation and delivery is realized through closed-loop feedback and phase regulation and control, and the authenticity, stability and compliance of AI marketing content are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of big data analysis and application technology, specifically to an AI-powered intelligent marketing method for generating selling points based on multi-source data. Background Technology

[0002] "AI-powered intelligent marketing based on multi-source data for generating selling points" refers to using artificial intelligence technology to integrate and analyze multi-dimensional data from different sources (such as user profiles, consumer behavior, market feedback, competitor information, social media reviews, and product attributes). This automatically identifies key advantages that can resonate with target users and transforms these advantages into clear, accurate, and marketing-valuable selling point descriptions through natural language generation or knowledge-driven content creation methods. Its core lies in leveraging the dynamic interaction of multi-source data and the reasoning capabilities of deep learning models to avoid the inefficiency and bias of traditional marketing that relies on manual experience to select selling points. This achieves automated, personalized, and real-time selling point generation, thereby improving the targeting and conversion rate of marketing content.

[0003] The existing technology has the following shortcomings: In existing technologies, the generation of selling points in AI-powered marketing generally relies on real-time input of multi-source data and the calculation of results through feature analysis. However, when unexpected events occur in the external market environment or user behavior changes drastically, the input data often fluctuates dramatically. In this situation, extreme signals can easily enter the feature calculation stage without effective filtering. Due to the lack of dynamic anomaly suppression and robustness mechanisms, such extreme values ​​may be erroneously amplified by the model during weight calculation, leading to a reverse exaggeration in the generated selling points. Specifically, a short-term surge in sales may be misjudged by the model as a long-term best-selling trend, and a momentary peak in negative information may be expanded by the model into an overall failure assessment, resulting in a significant deviation between the generated content and the actual situation. Once these deviations spread in large-scale deployment scenarios, they are easily tracked and verified by users and regulators, potentially triggering a crisis of trust and leading to legal and compliance risks such as being identified as false advertising.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an AI-powered intelligent marketing method for generating selling points based on multi-source data, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an AI-powered intelligent marketing method for generating selling points based on multi-source data, comprising the following steps: In the multi-source data access phase, a unified time-stamped matrix is ​​obtained. Based on the unified time-stamped matrix, an extreme value suppression prior is constructed and a data acquisition synchronization baseline is established. The data acquisition synchronization baseline is used to achieve end-to-end time alignment in order to provide stable time-series consistency. Under the constraint of end-link time alignment, extreme value diagnostic fingerprints are obtained, and threshold trajectories are generated based on quantile residual spectra and kurtosis skewness. The threshold trajectory is used as a dynamic benchmark to capture and calibrate sudden extreme values. Under threshold trajectory constraints, a causal debiased view is obtained. The instrumental variable method is used to perform time-series backtracking on promotional interventions. The backtracking results are jointly corrected with extreme value diagnostic fingerprints to generate a true trend mapping. Under the guidance of real trend mapping, semantic consistency constraints are obtained, a cross-domain comparison encoding scorer is constructed, and the selling point description is screened by combining causal debiasing view to ensure that the generated semantic output is consistent with the real trend. Under the constraint of semantic consistency, a closed-loop feedback loop is obtained, a small sample empirical stream is injected into the weight update stage of the real trend mapping, and the dominant indicator is reweighted to generate a stable weight field to ensure the balance and continuity of the output. Driven by a stable weight field, a phase-programmable delivery control mechanism is obtained. Through spectrum return traction, time difference micro-injection, and gain trajectory return strategies, a self-triggered dynamic closed loop is formed. In the dynamic closed loop, the generation of selling points is continuously optimized to suppress the phenomenon of reverse exaggeration.

[0007] Preferably, the steps to achieve end-to-end time alignment are as follows: Raw data is collected from multiple data sources, and a set of time fields from multi-source heterogeneous marketing-related data is extracted and uniformly converted into Coordinated Universal Time (UTC) format. This includes time fields from user purchase behavior data, marketing campaign execution data, product dimension information, user review content, social media content, and market feedback data, forming a unified time series with milliseconds as the minimum interval. Based on a unified time series, the behavioral density changes of data from various sources within the same time period are extracted, an extreme value suppression prior is constructed, and a sudden increase interval is identified and a risk label is generated through residual spectrum analysis. Based on a unified time series and risk labels, a synchronous baseline for data collection is established, and time sequence rearrangement and behavior node closure are achieved through sliding window analysis and behavior chain relationship matrix. Based on the data collection and synchronization baseline, a full-link time alignment process is constructed. All incremental data are compared for timestamp validity, behavioral node rationality, and risk label correlation to achieve data standardization and semantic integration.

[0008] Preferably, under the constraint of end-to-end time alignment, the steps for obtaining the extreme value diagnostic fingerprint and generating the threshold trajectory based on the quantile residual spectrum and kurtosis skewness are as follows: Based on a unified time series, marketing behavior events within the extreme value suppression prior first-level risk label segment are extracted, and a behavior density map containing event type, triggering method and data source identifier is constructed. Based on the behavior density map, the quantile residuals, kurtosis and skewness statistical features of time-series behavior data are extracted, and an extreme value diagnostic fingerprint containing event type, intensity value, behavior directionality and time location is constructed. Based on extreme value diagnostic fingerprints, the confidence interval boundary of behavior density is calculated through a high-low behavior comparison window, and then connected to form a threshold trajectory that changes dynamically over time. Under the influence of threshold trajectories, behavioral density is compared and sudden extreme value nodes are identified. The behavioral nodes are attributed and classified by combining the event link diagram and the marketing campaign strategy table, and dynamic labels are output.

[0009] Preferably, the steps for generating the true trend map are as follows: Based on the extreme events that overlap with the threshold trajectory screening and promotional behavior, we extract the promotional methods, channel sources, user reach paths and target audience tags; A set of instrumental variables was constructed around the extreme value segment of the intervention, and a time-series backtesting analysis was conducted to obtain the differences in behavioral trends before and after the promotional behavior; The instrumental variable backtracking results are compared bidirectionally with the extreme value diagnostic fingerprint. The baseline backtracking method is used to remove the net effect of promotional intervention and reconstruct the trend path without intervention. A trend mapping map is constructed based on the corrected net effect behavior sequence, marking the response time difference, consistency of change and intensity of correlation between behavioral variables, forming a true trend mapping for semantic optimization.

[0010] Preferably, the steps for constructing semantic consistency constraints and screening selling point descriptions are as follows: A semantic consistency constraint structure is constructed based on real trend mapping, which maps the trend changes of behavioral variables to language expressions one by one, and establishes language boundary standards. Based on the semantic consistency constraint structure, a cross-domain comparison coding scorer is constructed to extract language elements, match trends, and provide cross-scoring feedback for the selling point description text, and output a five-dimensional score table containing semantic strength value and trend support score. Based on the scores, all marketing language was screened, and sentences whose expressions were inconsistent with the data trends were semantically rewritten to ensure that all language outputs met the three semantic constraints of reasonable structure, trend support, and sentiment verifiability.

[0011] Preferably, the semantic rewriting process is based on the offset results output by the cross-domain comparison encoding scorer. The automatic replacement operation of the corresponding selling point description is triggered only when the semantic expression offset exceeds the preset threshold and the trend variable does not meet the minimum change range of the modifier.

[0012] Preferably, the stable weight field generation steps are as follows: Construct a semantic trend mapping alignment index table to map each expression in the marketing text to the corresponding behavioral variable in the real trend map; Collect user behavior data associated with the mapping expression as a small sample empirical stream, and bind timestamps and behavior paths; Inject small-sample empirical data into the trend graph weight revision stage, and perform weighted adjustments on variable paths based on the intensity of behavioral feedback. Based on the weight revision results, the dominant variable is identified, and a reweighting operation is performed on the dominant variable and its related variables; The revised trend graph structure generates a stable weight field, which is then embedded into subsequent text generation tasks as the basis for sovereign re-input.

[0013] Preferably, a self-triggered dynamic closed loop is formed through spectrum foldback traction, time difference micro-injection, and gain trajectory foldback strategies. The following steps are taken to continuously optimize the generation of selling points and suppress the phenomenon of reverse exaggeration within the dynamic closed loop: After the stable weight field is constructed, the periodic response features of each behavioral variable in the time dimension are extracted to construct a phase behavior window; Based on the phase behavior window, a spectrum return and traction operation is performed, and a staggered delivery rhythm model is designed according to the frequency structure of user behavior. After the spectrum return-to-traction is completed, a time difference micro-injection mechanism is introduced to bind the phase point and delay offset value to the content based on the semantic strength and user response latency characteristics. After time-difference micro-injection, a gain trajectory reversal strategy is executed, and the path priority of trend variables is adjusted based on the feedback results of delivery behavior to achieve self-correction of content generation direction.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes a foundation for data temporal consistency by using a unified time-scale matrix and a synchronous baseline for data collection, avoiding disturbances to model judgments caused by asynchronous data. It achieves accurate identification and correction of sudden data fluctuations through extreme value diagnostic fingerprints and dynamic threshold trajectory capture mechanisms. Combined with a causal debiasing view, it retrospectively corrects promotional interventions, effectively distinguishing between occasional behaviors and true trends. A cross-domain comparative coding scorer implements semantic consistency constraints, ensuring that generated content strictly conforms to actual trends, enhancing the authenticity and credibility of semantic expression. A closed-loop feedback mechanism enables small-sample empirical back-injection and reweighting of dominant indicators, dynamically optimizing the semantic generation weight distribution to ensure the stability and continuous evolution of the output structure. Finally, a phase-programmable delivery mechanism forms a dynamic control system based on user behavior frequency and content response rhythm, achieving adaptive closed-loop control throughout the entire process from content generation to delivery. In summary, this method effectively improves the authenticity, controllability, and compliance of AI-powered intelligent marketing content, reducing the probability of false advertising and trust risks. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart of the AI-powered intelligent marketing method for generating selling points based on multi-source data, as described in this invention. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0018] This invention provides, for example Figure 1 The AI-powered intelligent marketing method for generating selling points based on multi-source data, as shown, includes the following steps: In the multi-source data access phase, a unified time-stamped matrix is ​​obtained. Based on the unified time-stamped matrix, an extreme value suppression prior is constructed, and a data acquisition synchronization baseline is established. The data acquisition synchronization baseline is used to achieve end-to-end time alignment to ensure stable temporal consistency in subsequent dynamic feature analysis. To address the issue of distorted selling point generation results due to time inconsistencies in multi-source data in AI-powered marketing scenarios, a method for achieving end-to-end time alignment is proposed, specifically including the following steps: Raw data was collected from multiple sources and time standardization was performed. The data sources included: (1) user purchase behavior data, including order time, payment time, receipt time, and review time; (2) marketing campaign execution data, including campaign start time, end time, and phased push time points; (3) product dimension information, including listing time, price adjustment time, and inventory change time; (4) user comment content, including comment posting time, like response time, and reply interaction time; (5) social platform content, including posting time, forwarding time, commenting time, and emotional change recording time; (6) market feedback data, including sales fluctuation posting time and competitor posting event time on third-party e-commerce platforms. While collecting the above data, the original timestamp corresponding to each data point was extracted and uniformly converted to Coordinated Universal Time (UTC) format with millisecond precision. Subsequently, the time distribution of each type of data was checked, and the formatted timestamps were inserted into a unified master time series. This series, with milliseconds as the minimum interval, spans all data sources to form a unified time-stamped matrix. This matrix arranges the distribution of various data types along the time axis in the horizontal direction and retains the data content and its source labels in the vertical direction, forming a basic structure that can be used for subsequent alignment processing.

[0019] An extreme value suppression prior is constructed based on a unified time-scaled matrix. Specifically, using the unified time-scaled matrix as the coordinate axis, the behavioral density of data from various sources within the same time period is analyzed to extract data surge intervals. For example, if between 18:00 and 20:00 on a certain day, there is a surge in user orders, a sharp increase in ad clicks, a simultaneous decrease in product inventory, and a dramatic increase in mentions of the product on social media platforms, and there are no normal marketing activities scheduled during this period, the system determines that this time period is driven by an abnormal event. In this case, a residual spectrum is constructed by combining historical statistical indicators such as the mean, median, range, and standard deviation of behavioral density for the same period, identifying abnormal offset values, and generating a risk classification based on the degree of offset. For the risk areas identified above, an "extreme value risk label" is marked in the unified time-scaled matrix, and a warning coefficient is set for it. For example, when the sales growth rate exceeds three times the average of the same period, the inventory consumption rate exceeds twice the average, and user comments contain high-intensity emotional words such as "urgent," "sold out," and "flash sale," this segment will be marked as a level one risk area. The extreme value suppression prior is used in subsequent processing to guide the adjustment of data processing priorities, participation levels, and sampling strategies.

[0020] A data acquisition synchronization baseline is established based on a unified time-stamped matrix and extremum suppression prior labels. This process begins with point-to-point data comparison to check for delayed timestamp backfilling. For example, if a user places an order at 10:00 AM, but the platform records the time as 10:07 AM, and the comment is posted at 11:00 AM, but the same user's comment appears on social media at 10:30 AM, a preliminary judgment can be made that there is a data write delay. At this point, a sliding window analysis is performed on all source data, and a pairwise rearrangement strategy is used to adjust the time order. Next, key behavioral nodes are extracted according to the data event content, including the time node of each behavior in the "exposure → click → add to cart → place order → comment" chain. By constructing a behavioral chain relationship matrix, the sequential order between behavioral nodes is mapped. Nodes that do not conform to the temporal logic are time-compressed or extrapolated to ensure that each data chain is closed and coherent in the time dimension. Based on this, a minimum common integer multiple time window is established according to factors such as the synchronization accuracy of each data source, network upload cycle, and event response timeliness, as the time frame for the data acquisition synchronization baseline, so that data from different sources must be aligned and mapped before they can participate in subsequent processing.

[0021] Based on the data acquisition synchronization baseline and extreme value suppression prior labels, a full-link time alignment process is constructed. In this process, all subsequently entering incremental data must first be mapped and compared with the main time series to verify the validity of its timestamps, the rationality of behavioral nodes, and the correlation with risk labels. Specific operations include: extracting the timestamp field of new data, finding adjacent data items in the unified time-stamp matrix, and calculating the time difference and behavioral correlation; if it falls within the extreme value risk label range, its sampling frequency is adjusted to reduce its dominant influence on the overall feature value; if it is outside the time window set by the data acquisition synchronization baseline, it is delayed or processed with low weight. This process ensures that all data has undergone temporal standardization and semantic integration before entering the AI ​​intelligent marketing engine.

[0022] Under the constraint of end-to-end time alignment, the extreme value diagnostic fingerprint is obtained. The threshold trajectory is generated based on the joint judgment method of quantile residual spectrum and kurtosis skewness. The threshold trajectory is used as the dynamic benchmark for extreme value identification. The dynamic benchmark is used to achieve accurate capture and calibration of sudden extreme values. To address the issue of distorted selling point identification results due to sudden data fluctuations in AI-powered intelligent marketing, this method, based on end-to-end time alignment, constructs an extreme value diagnostic fingerprint and establishes a threshold trajectory to achieve accurate identification and behavioral labeling of abnormal extreme values. The specific steps of this method are as follows: Using a unified time series as a reference, all marketing behavior events within the extreme value suppression prior level-one risk label segment are extracted to construct a high-precision behavior density map. The data includes user browsing behavior, click behavior, adding to cart, order submission, payment completion, review submission, and after-sales service requests generated during the marketing path; it also includes platform-side behavior, such as ad push time, promotional activity nodes, price adjustment time, and inventory change time; and external dissemination behavior, such as keyword mention time, forwarding time, like time, and comment posting time on social media platforms. All of the above events are mapped to a unified time axis, with milliseconds as the time granularity, and a sliding time window is constructed. The behavior density of each type of event is normalized, and the results are superimposed to form a multi-source behavior density master map. The behavior density map must retain event type labels, behavior triggering methods, and data source identifiers within each time slice to support subsequent semantic reasoning processes.

[0023] Based on behavioral density maps, statistical features of time-series behavioral data are extracted along the time axis to construct an extreme value diagnostic fingerprint. Specifically, for each time slice, the quantile residual of that slice in the overall time series is calculated, i.e., the degree of deviation from the mean and median of behaviors in the same historical time period. Then, the kurtosis of that behavioral segment is combined to determine whether the behavior exhibits a sharp concentration trend. Simultaneously, skewness is used to determine whether the behavioral density has shifted, i.e., whether behaviors are excessively shifted forward or backward in time. These three types of feature parameters are jointly analyzed to form a multidimensional extreme value behavioral feature structure, marking high-risk behavioral nodes with significantly higher behavioral concentration than the baseline. Each high-risk node carries structured information such as event type, intensity value, behavioral directionality, and temporal location, serving as a component of the extreme value diagnostic fingerprint.

[0024] Based on extreme value diagnostic fingerprints, a dynamically adjustable threshold trajectory is constructed for benchmark comparison in subsequent extreme value judgments. The specific steps are as follows: Centering on each extreme value behavior fingerprint, extend a specified time period forward and backward, sampling normal behavior data of the same type to construct a high-low behavior comparison window. Within each window, calculate the cumulative probability distribution of behavior density, extracting the upper and lower limits between the 25th and 75th quantiles as fluctuation intervals, forming a confidence interval. Using the upper and lower boundaries of the confidence interval as boundaries, mark the threshold boundaries for behavior intensity within consecutive time periods, connecting these points to obtain a complete threshold trajectory. The trajectory dynamically changes across the entire unified time axis, automatically reflecting the boundary range of high-density behavior. Compare the actual behavior density with this trajectory; if the behavior density within a consecutive time slice exceeds the upper threshold and matches extreme value diagnostic features, then the behavior node is determined to be a sudden extreme value.

[0025] Under the influence of threshold trajectories, the behavior capture and attribution labeling of sudden extreme values ​​are completed. Specific methods include: extracting identified extreme value behavior nodes, performing temporal linkage analysis on these nodes with other behavior nodes, constructing an event chain diagram, and identifying the causal sequence relationship between behaviors. Further, in conjunction with a marketing campaign strategy table, it is determined whether the extreme value occurred before or after promotional intervention or sudden market information input. If it is closely coupled with external driving events, it is classified as a non-structured extreme value, and a weight suppression strategy is set for it in subsequent feature modeling. If the extreme value behavior is continuous and originates from the user's natural behavior trajectory, such as a large amount of positive feedback generated by product attribute optimization or natural word-of-mouth fermentation, it is defined as a structured extreme value, retaining all its behavioral intensity information. Finally, all identified extreme value nodes are re-embedded into a unified time series and output as dynamic labels, providing credible behavioral evidence and boundary control references for subsequent causal debiasing analysis and semantic output.

[0026] Under the constraint of threshold trajectory, a causal debiased view is obtained. The instrumental variable method is used to perform time-series backtracking on promotional interventions. The time-series backtracking results are jointly corrected with extreme value diagnostic fingerprints. Based on the correction, a true trend mapping is generated to provide a reliable basis for subsequent semantic-level selling point optimization. To avoid misleading influences of promotional activities on data trends, a causal bias-correcting view needs to be constructed based on dynamic threshold trajectory identification to achieve root cause identification of extreme events and realistic trend modeling. This includes the following steps: Under the constraints of dynamic threshold trajectories, all time periods marked as extreme events are identified, and the portions highly overlapping with promotional activities are filtered and structurally extracted. This operation, based on a timestamp correspondence mechanism, verifies the time range, behavioral variable type, peak intensity, and residual characteristics of each extreme record to identify any overlap with the preset promotional activity time window. For example, if, between 2 PM and 6 PM on a certain day, the number of orders, page views, and comments all exceed their respective dynamic threshold limits, and this period coincides with the product's participation in a "limited-time 30% discount" promotion, then the extreme event can be determined to be influenced by promotional intervention. After filtering, the promotional background information of the interventional extreme behavior is extracted, including promotional methods (e.g., price discounts, gift stacking), channel sources (e.g., in-site notifications, social media pushes), user reach paths (e.g., active search, passive exposure), and target audience tags (e.g., new users, high-frequency repeat customers). This process provides a quantifiable basis of intervention variables for the next step of establishing a causal analysis structure.

[0027] Around the selected intervention-related extreme value segments, an instrumental variable set was constructed and a time-series backtesting analysis was conducted to identify differences in behavioral structure before and after the promotional activity. First, the time period before the promotional activity was implemented was selected, and auxiliary variables with behavioral characteristics related to the current extreme variable but not directly affected by the promotion were extracted, such as user daily activity levels, natural product browsing trends, ad exposure stability, median historical daily sales, and average conversion rate in the previous period. Then, a behavioral trend sequence before and after the intervention was constructed, using the promotion trigger time as a dividing line. The stable behavior before the intervention served as the control group, and the intervention period and subsequent behavior served as the experimental group. A sliding window segmentation was performed based on the timestamp, and the slope, amplitude, and directional shift of the behavioral variables were calculated within each window, and the differences were compared with the instrumental variable change characteristics of the control group. For example, if the sales slope of a product doubled during the promotion period compared to before the promotion, while its pageview growth did not exceed one standard deviation of the historical fluctuation range, it indicates that the increase in sales was mainly driven by external factors rather than an internal trend. Through backtesting and comparison, without introducing a predictive model, the specific shift intensity caused by the marketing activity to the behavioral variables was identified.

[0028] Based on the backtracking results of instrumental variables and the content of extreme value diagnostic fingerprints, a bidirectional correction is performed to construct a causal net effect sequence after offset removal. First, event information matching the current variable in the extreme value diagnostic fingerprint is retrieved, including the start and end times of the event, volatility intensity, kurtosis, skewness, and the degree of resonance of covariates. Then, the behavioral offset trend estimated by the instrumental variable is compared value by value with the actual volatility amplitude in the extreme value fingerprint to determine whether there is any overlap in volatility caused by factors unrelated to promotion. If the natural trend reflected by the instrumental variable is significantly lower than the volatility level of the extreme value fingerprint, it indicates that the behavioral variable has a significant intervention effect. This offset is quantitatively corrected by reconstructing the trend path under the uninterrupted condition using a baseline backtracking method: starting from the natural trend line before the promotion, a predicted trajectory without the influence of promotional intervention is extended, and the difference between the actual observed values ​​and the predicted trajectory during the promotion period is removed by using a difference method. For example, if the actual sales exceed the sum of the predicted values ​​by 30% within three consecutive hours, this difference can be considered as the net effect of promotional intervention and removed from the trend data. Finally, a purified data sequence with promotional behavioral interference removed is constructed as input for subsequent trend mapping.

[0029] Based on the corrected net effect behavioral sequence, a true trend map of multivariate synchronous evolution is constructed. In this process, all behavioral variables are first sliced ​​into uniform time windows to ensure the comparability of values ​​at the same point in time. Then, trend smoothing is performed on each type of variable to remove small, high-frequency fluctuations and retain their trend trajectory on a medium-term scale. Next, causal order mapping relationships are established between different variables, such as ad impressions affecting page views, page views driving add-to-cart behavior, add-to-cart behavior affecting order conversion, and order conversion driving the number of comments and changes in sentiment. Based on this variable influence path, a trend chain is constructed, marking the response time difference, consistency of change direction, and intensity of correlation between different variables within each time period, forming a well-structured trend graph. In this graph, each node represents the true state of a specific variable at a specific time, and each edge represents the behavioral influence path confirmed after causal bias correction between variables. For example, if a stable correspondence is confirmed between a peak in ad clicks and the subsequent increase in order numbers within 5 minutes after promotional stripping, this path will be retained as a high-confidence link in the trend map. The final trend mapping results not only eliminated the bias and misleading effects introduced by marketing methods, but also preserved the collaborative change patterns of multidimensional behavioral variables under natural evolution, providing a stable and highly reliable data foundation for accurately identifying the "essential selling points" in subsequent semantic expression.

[0030] Under the guidance of real trend mapping, semantic consistency constraints are obtained, a cross-domain comparison coding scorer is constructed, and the cross-domain comparison coding scorer is used in conjunction with the causal debiasing view to screen the selling point description item by item to ensure that the generated semantic output is consistent with the real trend, thereby eliminating the reverse exaggeration phenomenon caused by extreme value amplification. To avoid semantic biases and exaggerated descriptions that mislead users during natural language generation, a semantic screening and enhancement mechanism for selling points oriented towards data consistency constraints is proposed, based on the existing construction of a true trend mapping. This mechanism includes the following steps: Based on the real trend mapping, a semantic consistency constraint structure is constructed to strictly map the trend changes of behavioral variables to linguistic expressions. The real trend mapping records the direction, magnitude, frequency, and cycle of change of each behavioral variable over a unified timeline, as well as the mutual response relationships between variables. Specifically, for the payment amount variable, the percentage increase or decrease within a continuous time period is extracted; for the order quantity variable, the average rate of change and abnormal offset intervals within a day are calculated; for the comment quantity variable, sentiment categories are labeled and the positive comment growth rate within a continuous time period is extracted; for the page view variable, high-frequency access times, page bounce rate, and dwell time are statistically analyzed; for the number of items added to cart, the synchronization ratio between it and conversion behavior is calculated. In constructing the semantic constraints, the above data trends need to be semantically mapped and categorized. To describe the semantics of "rapid growth," the corresponding increase in page views or order volume must exceed 30% within 20 minutes, with the trend slope continuously rising for at least three time windows. To describe the semantics of "enthusiastic user feedback," the corresponding increase in total comments must exceed the average level of the previous week, with positive sentiment words accounting for over 70%, and involving more than three dimensions of keywords (such as "satisfied," "good value," and "recommended"). This establishes a data-driven language boundary standard. All subsequently generated selling point language structures must operate within this boundary. If terms like "explosive," "record-breaking," or "continuously rising" are used, at least two variables must increase simultaneously, and the duration of this increase must exceed a certain range.

[0031] Based on a semantically constrained structure, a cross-domain comparative encoding scorer is constructed to structurally score each generated selling point text based on its semantic strength and actual trend. The scorer consists of three parts: language element extraction, trend mapping matching, and cross-scoring feedback. In the language element extraction stage, the marketing selling point text is decomposed into five types of language units: main statement subject (e.g., product name), action description (e.g., "growth," "positive reviews"), degree modifiers (e.g., "rapid," "significant"), temporal modifiers (e.g., "short time," "continuous"), and sentiment modifiers (e.g., "enthusiastic," "unprecedented"). In the trend mapping matching stage, each text unit is mapped to a corresponding relationship in the trend variable set. For example, "significant order growth" is mapped to two variables: payment amount and order quantity; "enthusiastic user feedback" is mapped to two variables: comment quantity and comment sentiment. The trend direction and offset slope within the mapped time period are read respectively. By setting threshold rules, such as "growth" must correspond to a continuous growth rate of more than 5%, "continuous" must reflect that the indicator has not declined for three consecutive time periods, and "explosive" must be accompanied by the historical maximum increase or the peak position of behavior. During the cross-scoring feedback phase, the scorer outputs a five-dimensional score table, including the strength value of semantic expression, the support value of the corresponding data trend, the deviation of expression from the facts, the trend synchronization score, and the sentiment strength reasonableness score. If the deviation exceeds the allowed threshold (e.g., the expression uses "explosion" but the trend amplitude is only 8%), the statement is marked as "potentially exaggerated risk" and a downgrade suggestion is given, such as replacing "explosive growth" with "rapid rise".

[0032] After completing cross-domain scoring, all candidate selling point language is screened item by item, and semantic rewriting is performed on sentences whose expressions deviate from the true trend. During the screening process, each marketing language is treated as a unit, and the trend source of each keyword in the sentence is confirmed by reverse mapping using a causal bias-reduction diagram and a true trend map. If an emotional expression is found not to be supported by behavioral data, the risk level of the expression is recorded, and it is replaced according to the rewriting suggestions provided by the scorer. For example, if "sales hit a new high" does not coincide with the historical maximum sales peak, it needs to be replaced with "sales are significantly higher than the recent average level"; if "widely recognized by users" has insufficient number of comments or insufficient user coverage, it needs to be replaced with "positive user feedback". This rewriting mechanism uses trend variables as constraints and the scoring mechanism as the feedback source to achieve a closed-loop verification between language expression and data facts. Each language output must meet three constraints: (1) reasonable structure, that is, the subject, verb and object semantics are clear and do not jump; (2) trend support, that is, all modifiers are traceable in the data; (3) verifiable emotion, that is, positive and negative polarity language must be supported by comment emotion or behavioral response data.

[0033] Under the constraint of semantic consistency, a closed-loop feedback loop is obtained, and a small sample empirical stream is injected into the weight update stage of the real trend mapping. Under the guidance of semantic consistency, the dominant index is reweighted, and a stable weight field is generated through reweighting to ensure the balance and continuity of the global output. To ensure a closed-loop feedback mechanism between trend-driven and semantic-driven marketing content generation, and to further enhance the stability of the generated results and the consistency of long-term performance, a closed-loop feedback and weight stabilization mechanism is proposed, with the following steps: Under the semantic consistency constraint mechanism, marketing selling point texts that have passed screening and scoring are selected, and combined with the real trend mapping structure, corresponding behavioral variable paths are extracted to construct a semantic trend mapping alignment index table. This index table establishes a one-to-one correspondence between core expressions in the text and trend variables. For example, "order volume continues to rise" corresponds to the behavioral variable "number of orders per unit time," and "user review popularity continues to climb" corresponds to the variables "review growth rate" and "positive sentiment rate." By using information such as variable paths, start times, maximum slope positions, growth duration, and feedback response delay times marked in the trend graph, the semantic expressions are structurally located on trend data nodes. This process not only confirms whether the current text maintains true trend consistency but also serves as the trigger entry point for subsequent feedback mechanisms, providing a basic mapping structure for small-sample feedback access and weight structure revision.

[0034] After establishing the semantic trend index structure, the small-sample empirical stream collection phase begins. This empirical stream refers to the behavioral data of real users generated within a short period of time, associated with the generated text. Collected metrics include: page dwell time after the marketing text is viewed, whether users added the text to their cart after reading, the difference between click-through rate and bounce rate, whether it was actively shared, the distribution density of likes across different user groups, and the difference in the ratio of positive sentiment words to neutral or negative words in comments. Each behavioral data point must be bound to a specific timestamp and user behavior path, and categorized according to the source channel, such as e-commerce homepage browsing path, search path, and content recommendation path. Subsequently, these behavioral metrics are mapped back to the corresponding variable structure in the semantic trend index table. For example, for the statement "payment conversion rate improvement," the feedback path should collect data on whether the order conversion was successful, the conversion time comparison, and the continued behavioral performance after the payment success page. This small-sample stream is not used for overall model training but serves as an important signal source for adjusting feedback weights, reflecting whether the current language expression accurately triggers the user's expected behavior.

[0035] After obtaining structured small-sample feedback data, it is injected into the trend mapping weight correction stage to construct a "feedback-variable" linkage structure. Each type of feedback behavior is weighted and quantitatively scored based on its intensity, rate of change, and consistency. For example, if a selling point description receives significantly higher click-through rate, comment popularity, and repeat purchases than similar texts within 24 hours, and its trend mapping path is "order growth + increased product review popularity," then this combination of trend variables can be judged to have real conversion value. In this case, the base weight of the "order growth" path in the original trend graph is increased from 0.78 to 0.89. If the "increased product review popularity" path performs poorly in the feedback data, its initial weight is retained. If there is a discrepancy between actual user behavior feedback and the semantic expression of the selling point, such as an increase in bounce rate after clicking or a concentration of negative words in comments, the weight of the relevant variable path in the trend graph is reduced. For example, if it was originally set to 0.81, it can be reduced to 0.66 based on the offset. The entire weight revision process is built on a trend mapping graph structure. A sliding time window mechanism is used to avoid short-term extreme feedback from causing fluctuations in long-term trend weights, thereby achieving robust weight updates with dynamic adjustment and control of drift.

[0036] After revising the trend graph weights, the updated set of behavioral variables is subjected to dominant variable identification and reweighting based on the semantic consistency constraint mechanism. The identification criteria include: first, the variable is frequently mentioned in multiple selling point texts; second, the variable exhibits strong response clustering in the small sample feedback behavior data associated with it; and third, the variable shows a clear synchronous change relationship with other behavioral variables on the trend timeline. Using these criteria, variables with "content response center" characteristics within the current marketing cycle are identified and defined as dominant variables. For example, if during a promotional period, "cart add-to-cart percentage" and "shortened order conversion cycle" show a synchronous increase for three consecutive days and are mentioned in multiple high-rated marketing texts, then it is considered a dominant behavioral variable. Based on this, reweighting is performed, further increasing the weight of the dominant variable to 1.0, forming a weight anchor point in the trend graph. Simultaneously, related auxiliary behavioral variables are kept in the range of 0.85-0.9, constructing a trend-guided main structure, making it easier to focus on key behavioral changes in subsequent marketing text generation and reducing content style divergence or conversion rate decline caused by non-key variables.

[0037] After the dominant variables are reweighted, a stable weight field is generated based on the full trend graph structure. This stable weight field is a continuous variable-weight space structure that has undergone multiple rounds of feedback correction, variable screening, and behavioral response induction. Its characteristics are: the weight change of the same variable in multiple time windows is controlled within a threshold, for example, no more than ±5%; the weight of the dominant variable remains in the peak range and no longer fluctuates drastically with unstructured feedback; marginal variables are dynamically removed or marginalized based on actual feedback results. The establishment of the stable weight field signifies that the trend graph has completed the dynamic adaptation between semantic content, real feedback, and behavioral paths, and can serve as a primary reweighting reference input in subsequent semantic generation, copywriting screening, and trend matching processes. The structure of the weight field will be embedded in the data input process of future content generation tasks, driving the generation engine to prioritize the selling point expression structure associated with high-weight variables, thereby improving content conversion rate, user experience, and expression stability. Through continuous iteration, this weight field can also absorb new rounds of feedback information, forming self-updating, self-reinforcing, and adaptive capabilities, serving as the central mechanism for realizing the integration of intelligent marketing semantic generation and real user behavioral feedback.

[0038] Driven by a stable weight field, a phase-programmable delivery control mechanism is obtained. By introducing spectrum return traction, time difference micro-injection, and gain trajectory return strategies, the delivery control process forms a self-triggered dynamic closed loop. In the dynamic closed loop, continuous optimization of selling point generation is achieved, thereby suppressing the reverse exaggeration phenomenon during the entire chain operation. To optimize and precisely control the pace of marketing content generation under the guidance of real trend data, a phase-programmable delivery control mechanism is proposed based on the established stable weight field. This mechanism completes the delivery loop through time-series deduction, dynamic reversal, and feedback linkage, and specifically includes the following steps.

[0039] After the stable weight field is constructed, the periodic response characteristics of each behavioral variable over time are obtained, and a phase behavior window is established based on these characteristics. In the stable weight field, behavioral variables such as "product pageview growth rate," "add-to-cart conversion rate," and "frequency of positive user reviews" have been weighted and corrected over multiple feedback cycles, forming a stable and clearly trending expression structure. The behavioral intensity changes of each variable over multiple time periods are extracted as raw time series data, and then periodic pattern recognition is performed to identify its peak and trough points in 24-hour, 7-day, and other periods. Taking "product pageview growth rate" as an example, if the analysis finds that the variable shows a significant concentrated upward trend between 12:30 PM and 1:30 PM and between 8:00 PM and 9:00 PM every day, then the variable is determined to have an active response phase in these two time periods. These high-response periods are defined as "phase behavior windows," and the phase windows of all variables are uniformly constructed into a time-behavioral variable dual-axis structure table, which will serve as a reference map for the starting rhythm of content delivery.

[0040] Based on the phase behavior window, a spectrum backtracking and traction operation is performed to adjust the actual delivery rhythm of marketing content. This operation centers on the identified high-frequency phase window, extending the content push time before and after it, ensuring effective exposure of content in the upper and lower bands of near-high-activity periods, thereby dispersing peak delivery concentration and reducing user information load. In this process, the user behavior frequency sequence is converted into a frequency domain expression, identifying the main behavior concentration cycle and its harmonic structure, and constructing a traction trajectory based on the user response curve. For example, if the concentration of "add-to-cart conversion rate" significantly increases between 20:00 and 21:00, but there are also weak frequency activation fluctuations at 19:45 and 21:15, a backtracking and traction method can be used to advance some content to 19:45 and delay some to 21:15, forming a staggered delivery strategy. Simultaneously, the amplitude and step size of the traction are adaptively determined based on the spectrum energy distribution. The design of the traction rhythm must maintain a balanced content delivery density, a reasonable user perception rhythm, and a coherent upper and lower rotation logic, ultimately constructing a delivery rhythm model that fits the user's rhythm.

[0041] After completing the spectrum foldback traction, a time-difference fine-tuning mechanism is injected, combining semantic strength and user response latency characteristics, to achieve precise content delivery with fine granularity. This mechanism semantically categorizes all selling points based on the complexity of the content's semantic structure, the strength of its emotional guidance, and data-driven weights, and injects time differences to match the suitability of different content levels. For example, for descriptive content emphasizing brand trust and service responsiveness, which have strong emotional stickiness, it is set to be exposed earlier based on historical user response latency characteristics, so as to pre-lay awareness before users form a clear purchase decision; while for content emphasizing price advantages and limited inventory, which has an immediate conversion orientation, it is set to be exposed synchronously or slightly delayed at peak times, triggering user behavior at the decision threshold. In specific implementation, each content statement is bound to a delivery phase point and a delay offset value. For example, content such as "the number of orders is continuously increasing" is delayed by 12 minutes to coincide with the peak of price fluctuations, achieving precise overlap between the content and the trend peak, thereby maximizing conversion potential. This time-difference micro-injection strategy controls the timing of semantic content triggering while establishing a temporal coupling relationship between the semantic strength of the content and trend variables, significantly improving the matching degree between content effectiveness and behavioral response.

[0042] After completing the rhythm-driven and time-difference micro-injection, a gain trajectory reversal strategy is executed to achieve a closed loop for content generation direction control based on the campaign results. This strategy uses post-campaign user behavior as the evaluation basis, extracting behavioral data such as content click-through rate, comment density, conversion rate, and dwell time, and classifying and statistically analyzing them according to the content-variable path mapping structure. For content with excellent post-campaign performance, its path source in the stable weight field is traced, its dominant trend variable is extracted, and the corresponding path weight priority is increased in the next round of content generation. For content with weak performance, its trend variable path is located, and it is determined whether it is due to mismatch in campaign timing, insufficient semantic expression, or deviation in trend weight. If a deviation is confirmed, a reversal operation is executed, that is, in the next round of content generation, the expression structure is reconstructed by switching to the covariate path of that variable. For example, if the original content is generated based on "improving the favorability rate," but there are many negative comments, the next round of generation will switch to generating new content based on covariates such as "customer service response timeliness" or "logistics delivery accuracy." This gain reversal not only controls the generation direction of the semantic path but also implements a self-correction mechanism based on user behavior data in the content strategy. Ultimately, through the linkage of spectrum rhythm control, semantic level injection, and behavioral feedback, a dynamic, programmable, rhythm-evolving, and expression-adjustable delivery mechanism is constructed to ensure that marketing content achieves continuous optimization and behavioral closure under the guidance of complex trend variables.

[0043] This invention establishes a foundation for data temporal consistency by using a unified time-scale matrix and a synchronous baseline for data collection, avoiding disturbances to model judgments caused by asynchronous data. It achieves accurate identification and correction of sudden data fluctuations through extreme value diagnostic fingerprints and dynamic threshold trajectory capture mechanisms. Combined with a causal debiasing view, it retrospectively corrects promotional interventions, effectively distinguishing between occasional behaviors and true trends. A cross-domain comparative coding scorer implements semantic consistency constraints, ensuring that generated content strictly conforms to actual trends, enhancing the authenticity and credibility of semantic expression. A closed-loop feedback mechanism enables small-sample empirical back-injection and reweighting of dominant indicators, dynamically optimizing the semantic generation weight distribution to ensure the stability and continuous evolution of the output structure. Finally, a phase-programmable delivery mechanism forms a dynamic control system based on user behavior frequency and content response rhythm, achieving adaptive closed-loop control throughout the entire process from content generation to delivery. In summary, this method effectively improves the authenticity, controllability, and compliance of AI-powered intelligent marketing content, reducing the probability of false advertising and trust risks.

[0044] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An AI-powered intelligent marketing method for generating selling points based on multi-source data, characterized in that: Includes the following steps: In the multi-source data access phase, a unified time-stamped matrix is ​​obtained. Based on the unified time-stamped matrix, an extreme value suppression prior is constructed and a data acquisition synchronization baseline is established. The data acquisition synchronization baseline is used to achieve end-to-end time alignment. Under the constraint of end-link time alignment, extreme value diagnostic fingerprints are obtained, and threshold trajectories are generated based on quantile residual spectra and kurtosis skewness. The threshold trajectory is used as a dynamic benchmark to capture and calibrate sudden extreme values. Under threshold trajectory constraints, a causal debiased view is obtained. The instrumental variable method is used to perform time-series backtracking on promotional interventions. The backtracking results are jointly corrected with extreme value diagnostic fingerprints to generate a true trend mapping. Under the guidance of real trend mapping, semantic consistency constraints are obtained, a cross-domain comparison encoding scorer is constructed, and the selling point description is screened by combining causal debiasing view to ensure that the generated semantic output is consistent with the real trend. Under semantic consistency constraints, a closed-loop feedback loop is obtained, a small sample empirical stream is injected into the weight update stage of the real trend mapping, and the dominant indicator is reweighted to generate a stable weight field. Driven by a stable weight field, a phase-programmable delivery control mechanism is obtained. Through spectrum return traction, time difference micro-injection, and gain trajectory return strategies, a self-triggered dynamic closed loop is formed. In the dynamic closed loop, the generation of selling points is continuously optimized to suppress the phenomenon of reverse exaggeration.

2. The AI-powered intelligent marketing method for generating selling points based on multi-source data according to claim 1, characterized in that, The steps to achieve end-to-end time alignment are as follows: Raw data is collected from multiple data sources, and a set of time fields of multi-source heterogeneous marketing-related data is extracted and uniformly converted into Coordinated Universal Time (UTC) format to form a unified time series with milliseconds as the minimum interval. Based on a unified time series, the behavioral density changes of data from various sources within the same time period are extracted, an extreme value suppression prior is constructed, and a sudden increase interval is identified and a risk label is generated through residual spectrum analysis. Based on a unified time series and risk labels, a synchronous baseline for data collection is established, and time sequence rearrangement and behavior node closure are achieved through sliding window analysis and behavior chain relationship matrix. Based on the data collection and synchronization baseline, a full-link time alignment process is constructed. All incremental data are compared for timestamp validity, behavioral node rationality, and risk label correlation to achieve data standardization and semantic integration.

3. The AI-powered intelligent marketing method for generating selling points based on multi-source data according to claim 2, characterized in that, To obtain the extreme value diagnostic fingerprint under the constraint of end-to-end time alignment, the steps for generating the threshold trajectory based on the quantile residual spectrum and kurtosis skewness are as follows: Based on a unified time series, marketing behavior events within the extreme value suppression prior first-level risk label segment are extracted to construct a behavior density map; Based on the behavior density map, the statistical features of quantile residuals, kurtosis and skewness of time-series behavior data are extracted to construct an extreme value diagnostic fingerprint; Based on extreme value diagnostic fingerprints, the confidence interval boundary of behavior density is calculated through a high-low behavior comparison window, and then connected to form a threshold trajectory that changes dynamically over time. Under the influence of threshold trajectories, behavioral density is compared and sudden extreme value nodes are identified. The behavioral nodes are attributed and classified by combining the event link diagram and the marketing campaign strategy table, and dynamic labels are output.

4. The AI-powered intelligent marketing method for generating selling points based on multi-source data according to claim 3, characterized in that, The steps for generating a true trend map are as follows: Based on the extreme events that overlap with the threshold trajectory screening and promotional behavior, we extract the promotional methods, channel sources, user reach paths and target audience tags; A set of instrumental variables was constructed around the extreme value segment of the intervention, and a time-series backtesting analysis was conducted to obtain the differences in behavioral trends before and after the promotional behavior; The instrumental variable backtracking results are compared bidirectionally with the extreme value diagnostic fingerprint. The baseline backtracking method is used to remove the net effect of promotional intervention and reconstruct the trend path without intervention. A trend mapping map is constructed based on the corrected net effect behavior sequence, marking the response time difference, consistency of change and intensity of correlation between behavioral variables, forming a true trend mapping for semantic optimization.

5. The AI-powered intelligent marketing method for generating selling points based on multi-source data according to claim 4, characterized in that, The steps for constructing semantic consistency constraints and screening selling point descriptions are as follows: A semantic consistency constraint structure is constructed based on real trend mapping, which maps the trend changes of behavioral variables to language expressions one by one, and establishes language boundary standards. Based on the semantic consistency constraint structure, a cross-domain comparison coding scorer is constructed to extract language elements, match trends, and provide cross-scoring feedback on the selling point description text, and output a five-dimensional score table. Based on the scores, all marketing language was screened, and sentences whose expressions were inconsistent with the data trends were semantically rewritten to ensure that all language outputs met the three semantic constraints of reasonable structure, trend support, and sentiment verifiability.

6. The AI-powered intelligent marketing method for generating selling points based on multi-source data according to claim 5, characterized in that, The semantic rewriting process is based on the offset results output by the cross-domain comparison encoding scorer. The automatic replacement operation of the corresponding selling point description is triggered only when the semantic expression offset exceeds the preset threshold and the trend variable does not meet the minimum change range of the modifier.

7. The AI-powered intelligent marketing method for generating selling points based on multi-source data according to claim 5, characterized in that, The steps for generating a stable weight field are as follows: Construct a semantic trend mapping alignment index table to map each expression in the marketing text to the corresponding behavioral variable in the real trend map; Collect user behavior data associated with the mapping expression as a small sample empirical stream, and bind timestamps and behavior paths; Inject small-sample empirical data into the trend graph weight revision stage, and perform weighted adjustments on variable paths based on the intensity of behavioral feedback. Based on the weight revision results, the dominant variable is identified, and a reweighting operation is performed on the dominant variable and its related variables; The revised trend graph structure generates a stable weight field, which is then embedded into subsequent text generation tasks as the basis for sovereign re-input.

8. The AI-powered intelligent marketing method for generating selling points based on multi-source data according to claim 7, characterized in that, The following steps are taken to form a self-triggered dynamic closed loop through spectrum foldback, time difference micro-injection, and gain trajectory foldback strategies, and to continuously optimize the generation of selling points and suppress the phenomenon of reverse exaggeration within the dynamic closed loop: After the stable weight field is constructed, the periodic response features of each behavioral variable in the time dimension are extracted to construct a phase behavior window; Based on the phase behavior window, a spectrum return and traction operation is performed, and a staggered delivery rhythm model is designed according to the frequency structure of user behavior. After the spectrum return-to-traction is completed, a time difference micro-injection mechanism is introduced to bind the phase point and delay offset value to the content based on the semantic strength and user response latency characteristics. After time-difference micro-injection, a gain trajectory reversal strategy is executed, and the path priority of trend variables is adjusted based on the feedback results of delivery behavior to achieve self-correction of content generation direction.

Images