Abstract: Systems and methods are disclosed for attributing advertisement exposure in a cinema environment. A ticket event is accessed, indicating a user’s purchase of a movie ticket at a specific location, screening room, and showtime. Advertisement schedule data is accessed to identify ads shown before the movie. The system determines that the user was likely present during the advertisement window using ticket information and supplemental data such as geolocation or concession purchase records. Post-exposure events—such as transactions, store visits, or digital engagement—are analyzed to detect behavioral changes following ad exposure. A correlation engine links the exposure to user behavior, enabling ad attribution, effectiveness scoring, and campaign optimization. The system supports privacy-preserving data handling and applies statistical or machine learning models to improve attribution accuracy.

Abstract: A system and method for calibrating electronic interaction data uses transaction log data to improve behavioral accuracy and reduce reporting bias. The system accesses electronic interaction data, such as digital receipts, and separately accesses transaction log data, such as credit card records, for a given user. By comparing the number of interactions recorded in each data source, the system identifies discrepancies and determines a projection factor that reflects the gap in reporting. The projection factor is applied to the electronic interaction data to generate a calibrated dataset that more accurately reflects user behavior. The system may apply different projection factors by user, merchant, region, or product type. Machine learning models may be used to detect reporting inconsistencies, infer missing data, and assign confidence scores.

Abstract: Systems and methods are disclosed for generating customized advertising audiences using artificial intelligence. The system analyzes a request for a desired audience, input through a natural language chat interface or the like, and automatically converts that input into structured traits and a database query based on the structured traits. A large language model (LLM) is used to analyze the request to extract meaningful characteristics (like age, behavior, or shopping patterns) and generate a structured query language (SQL) command to identify matching users in a database. The system can then deliver content to the matching users users or update marketing strategies based on performance.

Abstract: Systems and methods for generating projected store-level event-based interaction datasets using calibration techniques by accessing event-based interaction data from a plurality of users, where each event reflects a product transaction at a retail location. Supplemental user activity data is accessed from third-party databases. A projection factor is computed based on discrepancies between the interaction dataset and the supplemental data, the projection factor being indicative of underreporting in the observed data. The system applies the projection factor to scale the partial dataset into a projected dataset that estimates full consumer activity at a store or regional level. This projected dataset can be used to enhance targeted content delivery, improve media planning, support marketing mix modeling, or estimate store-level product demand in scenarios where direct point-of-sale data is unavailable. The system enables accurate behavioral insights without requiring data directly from retailers.

Abstract: Systems and methods are disclosed for attributing advertisement exposure in a cinema environment. A ticket event is accessed, indicating a user's purchase of a movie ticket at a specific location, screening room, and showtime. Advertisement schedule data is accessed to identify ads shown before the movie. The system determines that the user was likely present during the advertisement window using ticket information and supplemental data such as geolocation or concession purchase records. Post-exposure events—such as transactions, store visits, or digital engagement—are analyzed to detect behavioral changes following ad exposure. A correlation engine links the exposure to user behavior, enabling ad attribution, effectiveness scoring, and campaign optimization. The system supports privacy-preserving data handling and applies statistical or machine learning models to improve attribution accuracy.

Abstract: Systems and methods related to a RFID-based smart confined environment are provided. A system for localizing an object in a smart home includes a single RFID reader, a plurality of passive RFID tags, each of which is affixed to one of a plurality of objects to be located and contains information of a corresponding object, and a mobile platform including two antennas configured to transmit a carrier wave signal and receive a backscattering signal from a target object. The two antennas are parallelogram-shaped and abutted against each other along a radiation direction.