VideoMining Corporation provides in-store intelligence solutions for consumer product manufacturers and retailers. It offers Test Store Platform that enables retailers to understand and improve various dimensions of in-store experience, including store design, merchandising, measuring customer service level, and measuring out-of-store and in-store marketing strategies. The company also provides automated observational research solutions for manufacturers, including brand insights, category insights, and store insights. VideoMining Corporation was formerly known as Advanced Interfaces, Inc. The company was incorporated in 2000 and is based in State College, Pennsylvania.
Check out our last patent-backed bankruptcy report: SSW Holdings
403 South Allen Street Suite 101State College , PA 16801
SSW Holdings Inc. Debt-Asset Leverage Estimate
|Chapter Type||Case Number||Industry/Description|
|Portfolio Valuation Range||Asset Valuation Range||Total Assets Valuation Range||Liability Range||Leverage Ratio Range|
|$0.75 – 3.0M||$10 – 20M||$10.75 – 23M||$5 – 10M||2 -2.3|
SSW Holdings Inc. Portfolio Summary
The present invention is a method and system for automatically analyzing the behavior of a person and a plurality of persons in a physical space based on measurement of the trip of the person and the plurality of persons on input images. The present invention captures a plurality of input images of the person by a plurality of means for capturing images, such as cameras. The plurality of input images is processed in order to track the person in each field of view of the plurality of means for capturing images. The present invention measures the information for the trip of the person in the physical space based on the processed results from the plurality of tracks and analyzes the behavior of the person based on the trip information. The trip information can comprise coordinates of the person’s position and temporal attributes, such as trip time and trip length, for the plurality of tracks. The physical space may be a retail space, and the person may be a customer in the retail space. The trip information can provide key measurements as a foundation for the behavior analysis of the customer along the entire shopping trip, from entrance to checkout, that deliver deeper insights about the customer behavior. The focus of the present invention is given to the automatic behavior analytics applications based upon the trip from the extracted video, where the exemplary behavior analysis comprises map generation as visualization of the behavior, quantitative category measurement, dominant path measurement, category correlation measurement, and category sequence measurement.
The present invention is a system and framework for augmenting any retail transaction system with information about the involved customers. This invention provides a method to combine the transaction data records and a customer or a group of customers with the automatically extracted demographic features (e.g., gender, age, and ethnicity), shopping group information, and behavioral information using computer vision algorithms. First, the system detects faces from face view, tracks them individually, and estimates poses of each of the tracked faces to normalize. These facial images are processed by the demographics classification module to determine and record the demographics feature vector. The system detects and tracks customers to analyze the dynamic behavior of the tracked customers so that their shopping group membership and checkout behavior can be recognized. Then the instances of faces and the instances of bodies can be matched and combined. Finally, the transaction data from the transaction data and the demographics, group, and checkout behavior data that belong to the same person or the same group of people are combined.
The present invention is a method and system for measuring a set of shopper behavior metrics that represent the strength of a product category or a group of categories in the performance of a store area. A set of rating parameters are defined in order to provide a unified and standardized rating system. The rating system represents the effectiveness of the product category in a store area. The metrics are defined in a manner that is normalized so that they can be used across different types of product categories. The datasets are measured per category or group of categories over time to identify how the strength has varied over time, and to monitor trends in the category performance. The measured datasets are further analyzed based on various demographic groups and behavior segments. The analysis facilitates a better understanding of the strength of the category for different shopper segments, which in turn can be applied for developing better store area optimization strategies.