A cognitive AI system for real-time belief transparency and personalized consumer education.

Abstract

A cognitive artificial intelligence system for recognizing persuasive marketing techniques and providing real-time transparency to consumers in digital environments, the system comprising the following: • a digital content monitoring module (101) configured to analyze digital content presented to a user during interaction with online platforms such as e-commerce websites, social media platforms and digital advertising systems; • a persuasion detection engine (102) configured to process the analyzed digital content using artificial intelligence techniques, including natural language processing, sentiment analysis and machine learning classification models, to detect persuasive marketing techniques embedded in the digital content; • a cognitive interpretation module (103) configured to interpret detected persuasion techniques by linking the detected techniques to behavioral science principles stored in a behavioral knowledge base (104); • a user profiling and personalization module (105) configured to generate a dynamic user profile based on user interaction behavior and digital competence characteristics; • and a transparency interface module (106) configured to display explanatory notifications to the user in real time, describing the detected persuasion techniques and their potential influence on the purchase decision.

Description

Application area of ​​the invention

[0001] The present invention relates generally to the fields of artificial intelligence, behavioral analysis, and consumer protection technologies. More specifically, it is a cognitive AI system capable of recognizing persuasive marketing techniques in digital environments and providing consumers with real-time transparency and personalized feedback. Since digital environments increasingly rely on complex algorithmic marketing strategies to influence purchasing decisions, the present invention aims to provide consumers with the necessary awareness of the underlying psychological mechanisms of persuasive marketing techniques. Background of the invention

[0002] In recent years, digitized e-commerce platforms, social media, and online advertising networks have fundamentally changed consumers' lives in terms of how they interact with products, services, and marketing communications. The concept of the new digital age, which revolves around sophisticated artificial intelligence, machine learning technologies, and behavioral analytics tools, is increasingly based on data-driven marketing infrastructures. These aim to achieve impressive results, such as optimizing customer loyalty and increasing conversion rates.

[0003] Because digital systems embed prospecting design techniques into user interfaces and marketing messages, techniques such as limited availability indicators signaling scarcity, timeout signals for reduced offers or emails, social proof in terms of popularity metrics or users' self-talk during purchase, personalized product recommendations, and persuasively designed advertisements or promotions, all based on solid principles of behavioral economics and cognitive psychology—typically related to self-created fallacies in decision-making such as loss aversion, the law of diminishing marginal utility, bandwagon effects, and framing effects—which have been shown to influence human behavior patterns.

[0004] While these persuasion techniques increase the effectiveness of digital marketing systems, they also lead to information asymmetries on digital platforms when persuasion mechanisms are largely hidden within the user interface. Essentially, this type of persuasion offers a range of elements that are ubiquitous but never explicitly disclosed on digital platforms. As a result, average consumers are unaware of the existence and actual application of these persuasion strategies. Based on the algorithmically determined order of displayed information, less discerning consumers may unconsciously influence their purchasing behavior rather than optimizing it—especially through such manipulation.

[0005] Most existing strategies for promoting transparency on the internet through technology and regulation focus on individual aspects of information, such as cookies, marketing identification signals, and algorithms that allow the public to question data processing. However, these strategies do not offer a legitimate and practical way to uncover the complex persuasion processes in digital marketing content or to analyze and differentiate new influences on internet-based media strategies that shape consumers' everyday lives. At best, any current solution extends its practical value to the ability to use authorized tools to draw customers' attention to issues that influence their decision-making.

[0006] Furthermore, existing tools rarely incorporate adaptive learning patterns that could improve consumers' understanding of marketing strategies in a timely manner. Most transparency solutions offer all users static content without considering differences in their knowledge, digital skills, or behavioral patterns. Such systems therefore significantly limit the effectiveness of measures aimed at promoting meaningful consumer awareness and informed purchasing decisions.

[0007] An advanced technological system was needed that could automatically analyze extractable elements in digital texts and explain their behavioral interpretation using questions based on principles of behavioral science. To this end, the system provides users with context-related explanations in a personalized yet easily understandable format. A necessary prerequisite for the success of such a system is its online operation. It functions as an intelligent agent within consumers' digital browsing environment, offering adaptive support to enhance their knowledge of persuasive marketing techniques.

[0008] To overcome these disadvantages, the present invention presents a cognitive AI system that integrates algorithms for recognizing persuasive elements and behavioral analytical models for understanding. The integration of these models with a suggestion and feedback engine enables complete transparency and a high level of consumer awareness in modern digital marketing. Summary of the invention

[0009] The present invention aims to overcome the technical limitations of existing technologies in the field of digital transparency and consumer protection by introducing a cognitive artificial intelligence system that detects manipulative marketing techniques in digital environments and provides its users with real-time interpretive transparency with personalized educational feedback.

[0010] As illustrated here, digital platforms increasingly use algorithms that analyze consumer behavioral data to employ psychological mechanisms for manipulation, thereby subjecting consumers to inferential control. Existing transparency protocols only regulate the disclosure of data confidentiality and the labeling of advertising, but not the detection or interpretation of manipulation strategies within digital interfaces. As a result, unsuspecting consumers are highly vulnerable to the manipulation of their behavior in their randomly perceived digital interactions.

[0011] One embodiment of this embodiment is sufficient to propose a multi-layered cognitive artificial architecture capable of efficiently monitoring digital content, detecting signs of coercion by which a person is manipulated, interpreting the psychological mechanisms by which this works, and reflecting them back to the user via a transparent real-time interface.

[0012] In one embodiment, the present invention provides for the establishment of a digital content analysis engine to evaluate textual, numerical, and visual elements of growing digital content that a user encounters when interacting with various online platforms such as e-commerce websites, social media websites, and other digital advertising systems.

[0013] The invention comprises a mechanism for detecting persuasion attempts, based on AI models of natural language processing, sentiment analysis, pattern recognition, and machine learning classification to identify indicators contained in digital content. These indicators may include, for example, the following: • Indications of scarcity, • Urgency signals, • Popularity indicators • emotional persuasive language and • Recommendation-based influence mechanisms.

[0014] Once persuasive elements are detected, the system activates a cognitive interpretation layer that links the identified persuasion patterns with relevant principles of behavioral economics and cognitive psychology. These include principles such as loss aversion, anchoring heuristics, framing effects, and bandwagon effects. The system analyzes how the identified persuasion technique can influence consumers' purchasing decisions.

[0015] Another aspect of the invention is a personalized consumer education module that tailors the delivery of explanations regarding identified persuasion techniques to the characteristics of the individual user. Depending on the user's digital literacy level, previous interaction with persuasion warnings, and behavioral preferences, the system can dynamically adjust the scope, complexity, and format of the informational feedback.

[0016] Furthermore, this invention proposes a method for displaying transparency indicators live, for example as notifications or pop-ups, alongside the website while the user views digital content. Context-related markers, pop-ups triggered by user interactions, or information fields can be displayed to inform the user about detected persuasion strategies without preventing access or interfering with the content. This preserves the autonomy and functionality of the platform while simultaneously increasing transparency.

[0017] In the long term, the system evolves into a continuously adapting system that serves as a learning platform and helps users sharpen their awareness of persuasive marketing. The invention thus promotes the steady improvement of consumers' digital literacy, the critical evaluation of marketing communication, and more informed decisions in e-commerce.

[0018] Accordingly, this work designs an experimental dramatization that combines artificial intelligence (AI), behavioral science, and adaptive educational interfaces to increase transparency and consumer awareness in the algorithmically driven marketing landscapes of digital advertising.

[0019] The invention can be implemented in various digital infrastructures, including web browsers, e-commerce platforms, mobile shopping applications, consumer protection instruments and digital advertising monitoring systems, without requiring significant changes to existing marketing infrastructures. Detailed description of the invention

[0020] The present invention relates to a cognitive artificial intelligence system capable of recognizing persuasive marketing techniques in digital environments and providing consumers with real-time transparency and personalized feedback. The invention provides a technological framework that automatically analyzes digital content encountered by users during their online interactions and identifies persuasion mechanisms embedded in marketing messages and user interface elements.

[0021] The system is designed for real-time operation as users interact with various digital platforms, including e-commerce websites, social media, online advertising systems, recommendation systems, and mobile commerce applications. The invention's primary objective is to increase transparency within the digital marketing ecosystem, enabling consumers to gain a well-informed understanding of the actual decision-making processes through targeted influence.

[0022] According to Fig.The invention comprises a system for the cognitive transparency of persuasion strategies (100). This system serves to inform the user about the transparency of the currently applied persuasion strategies. The invention 100 comprises a module for monitoring digital content (101), an engine for recognizing persuasion strategies (102), a module for cognitive interpretation (103), a knowledge base on behavior (104), a module for user profiling and personalization (105), a module for a transparency interface (106), a module for consumer education (107), and an adaptive learning module (108).

[0023] The "Digital Content Monitor (101)" class is configured to monitor the digital content that end users see and interact with while browsing websites and using online platforms. This module is used in browser extensions or plugins, mobile applications, and even simple systems, making it particularly suitable for e-commerce platforms. It captures signals in various types of digital content, such as text, numbers, suggestions, and curbs, as well as visual communication from marketing professionals. Depending on the monitoring focus, the content typically includes product descriptions, advertising messages, banner ads, inventory levels, and so on, all of which can be displayed in virtual marketplaces.

[0024] The information extracted from the content is sent to the Persuasion Detection Engine (102), which is configured to identify persuasion techniques embedded in the digital content. For its analysis, the detection engine uses AI techniques such as natural language processing, sentiment analysis, pattern recognition algorithms, and machine learning to classify persuasion attempts. The processes employed in this case can help detect any indications of persuasion attempts, which are often considered indicators of sales promotion. These include, for example, scarcity signals indicating limited stock, signals of urgency through time-limited offers or discounts, the use of buyer or viewer numbers as evidence of demand, emotionally engaging marketing language, and algorithmically generated product recommendations.

[0025] Currently, persuasive elements are detected and the information is forwarded to the cognitive interpretation module (103). This module is responsible for explaining the principles of persuasion strategies using behavioral science theories. The interpretation interacts with the behavioral knowledge base (104). The model is primarily based on facts representing theories of behavioral economics and consumer psychology. Some of these theories explicitly describe the interactions of cognitive biases and decision-making heuristics such as loss aversion, anchoring heuristics, bandwagon effect, and framing effect. Using this knowledge base, the system—instead of relying solely on machine learning—can employ a heuristic inference mechanism to link detected slogans with corresponding psychological mechanisms and explain how these techniques can influence consumer behavior.

[0026] The next innovation is a user profiling and personalization module (105) that creates and maintains a dynamic profile reflecting the user's individual characteristics. This module analyzes users' digital literacy, their previous interactions with advertising messages, their browsing behavior, and their learning preferences. Based on this information, the system adapts the presentation and explanations of persuasion techniques to the user's level of understanding. Simplified explanations for inexperienced users can illustrate certain marketing techniques, while more advanced users receive more detailed explanations of the psychological principles of persuasion.

[0027] The system also integrates a transparency interface module (106) that enables real-time communication between the cognitive AI system and the user. The transparency interface displays contextual messages alongside digital content containing persuasive messages. These messages can be designed using highlights, informative pop-ups, explanatory sidebars, or other visual aids to inform the user about the impact of the persuasive communication. The interface was designed to be unobtrusive, directly increasing transparency without interfering with the content or standard functionality of the digital platform.

[0028] In addition to the comments mentioned here: As explained in the module “Consumer Education” (107), the system conveys an understanding of the marketing paradigm that benefits users exclusively. This mode “teaches” users, through short descriptions, stimulating questions encouraging active participation, and concrete examples, what to look for when evaluating a persuasive film. Through repeated exposure to this learning content, users gradually develop the ability to recognize the tactics employed in digital marketing messages.

[0029] The adaptive learning module 108 included in the invention contributes to a better understanding of the system and improved learning capability. Machine learning algorithms can use it to adapt the persuasion recognition models to the latest observed persuasion patterns, user feedback, and the evolving marketing tactics of digital platforms. Thanks to adaptive learning, the system remains effective despite the continuous development of digital marketing channels.

[0030] In a demo version of this invention, for example, a visit to an online shopping platform could create the impression that only a few items are left and that many other users are interested in the product. Based on the relevant text and numerical indicators captured by the Digital Content Monitoring Module (101), the Persuasion Detection Engine (102) recognizes that the technique of artificial scarcity is being used. This data, along with user statements, is forwarded to the Cognitive Interpretation Module (103). The Cognitive Interpretation Module then accesses the database and confirms that these techniques form the basis for the targeted manipulation of cognitive biases such as loss aversion and bandwagon effect. The Transparency Interface Module (106) then issues a notification explaining the message's actual purpose: to create a sense of urgency and influence social interaction.The user is thus informed about the persuasion tactics used against them. After several interactions with the comments explained above, the consumer education module (107) prepares the user to eventually recognize the persuasive tricks and improve consumer decisions, all in the interest of informed decision-making.

[0031] The invention of the associated technology integrates artificial intelligence, behavioral science, and user training, offering dynamic learning to improve transparency and consumer awareness in digital marketing environments. It comprises a system that operates across a variety of digital infrastructures, such as web browsers, e-commerce platforms, mobile applications, and consumer protection systems, and its implementation does not require extensive changes to existing marketing technologies.

Claims

[1] A cognitive artificial intelligence system for recognizing persuasive marketing techniques and providing real-time transparency to consumers in digital environments, the system comprising: • a digital content monitoring module (101) configured to analyze digital content presented to a user during interaction with online platforms such as e-commerce websites, social media platforms and digital advertising systems; • a persuasion detection engine (102) configured to process the analyzed digital content using artificial intelligence techniques, including natural language processing, sentiment analysis and machine learning classification models, to detect persuasive marketing techniques embedded in the digital content; • a cognitive interpretation module (103) configured to interpret detected persuasion techniques by linking the detected techniques to behavioral science principles stored in a behavioral knowledge base (104); • a user profiling and personalization module (105) configured to generate a dynamic user profile based on user interaction behavior and digital competence characteristics; • and a transparency interface module (106) configured to display explanatory notifications to the user in real time, describing the detected persuasion techniques and their potential influence on the purchase decision. [2] The system according to claim 1 can extract textual, numerical and visual signals from digital marketing content, such as signals related to product or service descriptions, advertising, recommendations / notifications or interfaces. [3] System according to claim 1, wherein the persuasion detection engine (102) detects persuasion strategies, including scarcity cues, urgency messages, indicators of social proof, emotionally persuasive language and recommendation-based mechanisms that influence people at least a little or most of the time. [4] System according to claim 1, wherein the cognitive interpretation module (103) links the identified persuasion strategies with behavioral economic principles such as loss aversion, anchoring effects, bandwagon effects, social pressure effects and framing effects. [5] System according to claim 1, wherein the transparency interface module (106) provides the user with context-related assistance regarding the content through a context-sensitive popup message, an indicator for highlighted content or an information display field next to the digital or multimedia content. [6] System according to claim 1, further comprising a consumer education module (107) configured to provide training information on persuasive marketing techniques to improve users' awareness and digital competence. [7] System according to claim 6, wherein the consumer education module (107) provides adaptive educational content that is based on the user's digital competence level and interaction history and is supplemented by persuasive cues. [8] System according to claim 1, further comprising an adaptive learning module (108) configured to update models for detecting persuasive behavior based on user feedback, newly recognized persuasion patterns and evolving digital marketing strategies. [9] System according to claim 1, wherein the system functions as a browser extension, mobile application module or embedded system within digital trading platforms. [10] System according to claim 1, wherein the system is configured to improve transparency in algorithm-driven marketing environments by enabling users to identify persuasion mechanisms that influence digital purchasing decisions.

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