8 results about "Consumer privacy" patented technology

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

A system for the near real-time exchange of regulatory-compliant and privacy-compliant consumer credit data between two parties (i.e., a credit bureau and a financial services institution) that facilitates the financial services institutions (i.e., lenders) to directly identify audiences of pre-screened consumers (i.e., prospects) to offer credit products without requiring the intervention of a third party, such as an agent of the bureau, employs a data clean room to facilitate the necessary data exchange. The clean room enables and expedites the presentment of firm offer of credit pre-screened offers across authenticated or authenticatable offline and digital online channels, thereby making credit available to consumers in a more timely and efficient manner while protecting consumer privacy.

Methods, computer-readable media, software, and apparatuses may assist a consumer in keeping to their preferred privacy preferences when making a purchase online. Differences between the privacy policy of a vender and the privacy preferences of the consumer may be output for display to the consumer, along with alternative vendor recommendations, including vendors having privacy policies more closely match with the privacy preferences of the consumer.

The application relates to the technical field of homomorphic encryption, and discloses a homomorphic encryption protection system for flower consumer privacy image features, which comprises the following steps: acquiring the historical actual quality of interactive flowers, the apoptosis metabolic rate constant and the absolute elapsed time, constructing a multi-dimensional initial continuous data tensor which is objectively attenuated with time, mapping the tensor into equivalent algebraic potential energy within the limit of the truncation order and performing dimension orthogonal elimination, combining Gaussian bottom noise to be packaged into polynomial ciphertext, using fitness group intelligence optimization to obtain optimal evolution damping, fusing the elapsed time to output stable state ciphertext under the cipher state, then performing orthogonal inner product with external seasonal market ciphertext, and calling a public key to perform algebraic dimension reduction convergence, and finally using an isolated private key to crack and reverse restore into a preference concentration on the terminal side. The application effectively suppresses the cipher state noise divergence caused by multi-dimensional asynchronous metabolism, and realizes the safe evolution and accurate evaluation of the privacy elements according to the natural law.

The invention provides a group meal restaurant behavior data multi-dimensional extraction device and a group meal restaurant behavior data multi-dimensional extraction method, and aims to solve the problems of lack of consumer dining information acquisition means, incapability of well protecting consumer privacy, uneven acquired data quality, data missing and serious errors in the group meal industry. According to the method, data dimension division, data acquisition and cleaning, descriptive statistics, correlation analysis and the like can be performed on dining behaviors and information through multi-dimensional information acquisition, a specific mathematical model and a statistical analysis method under the condition that personal information of diners is not acquired, so that multi-dimensional information extraction and analysis of restaurant operation are realized; potential laws behind data are mined, restaurants are helped to better understand customer behaviors and demands, and service quality and operation benefits are improved.

Provided is a distributed application that shifts certain server-side operations from geolocation analytics platforms to client computing devices to enhance consumer privacy and the collection and use of potentially sensitive, personal data about an individual and their mobile device.

The invention relates to a coscheduling method and a coscheduling system for privacy protection of productive disappears based on group learning and multi-agent reinforcement learning. The coscheduling method comprises the following steps of: constructing a coscheduling architecture between a productive disappears model and productive disappears; based on the architecture, carrying out partial observable Markov game modeling of multi-producer collaborative scheduling under incomplete information, and constructing a group learning-based decentralized privacy protection collaborative scheduling framework; and an optimal transaction strategy is solved in a decentralized privacy protection collaborative scheduling framework through a PS-MAPPO (Privacy Protection State Enhanced Multi-agent Near-end Policy Optimization Algorithm), so as to realize multi-yield disagger privacy protection collaborative scheduling. The method can provide core support for constructing a next-generation virtual power plant and a distributed energy aggregation system which are safe, credible, efficient, cooperative and elastically extensible, and has wide application prospects in the fields of novel power system construction, park-level comprehensive energy management, multi-micro-grid interconnection operation and the like.