, barcode scanner 314 is an optical laser type scanner for reading barcodes but can also be visible light optical image detector. Barcode scanner 314, in conjunction with barcode scanners on other shelf screens located nearby provides the ability to read bar codes from any angle so long as the barcode is not physically obstructed.
[0124] SRD system 100 can be used for other functions as well as the dynamic display of information. Some of these functions are described as follows.
[0125] In one embodiment, SRD system 100 can be used for inventory control. In one embodiment, SRD system 100 using various components, such as IR cameras and smart mats, can detect products visually and physically (e.g., by weight). As such SRD system 100 can detect when products or items are added to or removed from a shelf. In one embodiment, inventory is counted when received in case allotments when delivered to the particular store. The product then will be added into a database of the SRD system when placed on the shelf and then removed from the on-shelf inventory when picked up and removed by the customer. In one embodiment, this can trigger the need to have additional product placed back onto the shelf for display and sales purposes. The product can be completely removed from the store's inventory system once scanned at the check out position. The Infrared sensing device, in one embodiment, is capable of determining actual package graphics and brand names, depending on the size of the product itself.
[0126] In inventory control embodiments, the SRD system can complement any current inventory system already in place within a particular retail account, or can be used as a stand-a-lone inventory system for new customers or those looking for a more comprehensive inventory control and shelf-stocking program.
[0127] In one embodiment the SRD system can act as a security system. The SRD system is equipped with motion sensor technology, which is primarily used as a power saving device and automatically lights up the shelf when motion is detected. This system can also be used as a back-up security system, when the store is closed for any period of time using this same technology. While there is no motion in the store, the shelves go on stand by mode and power down. As soon as there is any kind of slight motion (these sensors can be configured to be extremely sensitive) the screens are powered back up instantly and this can trigger an alarm if the store has been closed.
[0128] The security aspect of the system detects the changes in the electrical properties of continuous resistance pressure sensors, caused by the mechanical deformation of its material as a result of product removal. The security aspect of system has a sampling rate of 10 Hz for a shelf pressure profile that comprises 200 elements results in 2,000 pressure data points per shelf per second. A variation in the real-time captured pressure profile will generate an alert. These alerts are controller by a rules based process for alarm escalation.
[0129] Sensor fusion results from combining the outputs from multiple sensors capturing a common event, such that the fused result better captures the event than any single constituent sensor inputs. Its primary objective is to tease out a clean signal, an event or a motion, from the noise generated by these multiple sensors, a mobile device, and an environment in which the sensors and mobile device are located.
[0130] Context awareness involves another layer of algorithms that interpret multiple sensor data to infer higher level information. These algorithms distill a context, for example: the retail user is examining product into a series of characteristic features in multiple sensor data. The presence of these and other features in captured sensor data inform the context detection algorithm if the examining context is valid. People will examine products differently; robust context detection must be abstracted over a representative population of retail users. Sensor fusion, along with embedded processing and connectivity, enables context awareness, and context awareness enables a new world of services. Information is presented to a shopper based on the shopper's context. A shopper's context is determined based on the shopper's actions and/or state. A shopper's actions and/or state can also be referred to as a shopper's texture.
[0131] Context is defined as the circumstances or facts that form the setting for an event, statement, situation or idea. In software programming, context-aware apps examine who, where, when and what, and the software designer uses this contextual information to determine why a situation is happening and then encodes some action in the application.
[0132] Based on this definition, the four most important categories of information to formulate a context-aware action are identity, location, time, and activity. In using contextual information to formulate a deterministic action, context interfaces occur within (and in between) first a human being, then the environment and, lastly, machine and infrastructure elements. The same way that a canvas, a collection of paint tubes and a brush allow an artist to create a masterpiece, context awareness and these interfaces are the tools for enabling a variety of services that otherwise would not be meaningful. This is where incremental improvements in technology can lead to the whole being much greater than the sum of its parts. Sensor can detect and provide information concerning a human beings' emotions. Sensors provide access to the human mindset, making an experience more "personal."
[0133] Through the use of the infrared capabilities of SRD system 100, the system can capture shopper texture during the normal shopping experience. This includes the collection of big data, which can be captured, compiled and sold to manufactures much like scan track and IR data is sold today. Through the use of Sensor Fusion, not only can SRD system 100 capture consumer patterns such as stopping to view a product, picking up a product, putting it back or placing the item in a shopping cart, but we can also view facial expressions, such as frowning, smiling, etc. without actually seeing who the particular person is in order to protect their privacy. This real-time data capturing will also allow interaction with the consumer through a number of reactionary devices (e.g., push/pull devices).
[0134] SRD system 100 captures real-time retail shopper actions (shopping textures) while concurrently displaying dynamic shopper context-based multimedia content. A context is defined as the circumstances or facts that form the setting for an event, statement, situation or idea. Context-aware systems examine where, when, and what that then becomes contextual information which is used to determine why a situation is happening and then encodes some action in the context-aware system. To identify a context-aware action the context-aware system needs to confirm a shopper (human entity), their location, the time of shopper recognition, and shopper activity.
[0135] The interactions and interfaces between the shopper, the SRD system 100, shopping environment and SRD system 100 infrastructure provide real-time data points for determining context awareness, including: motion, posture, strides, reaction to visual stimuli. After input data is collected by the sensing nodes, an embedded node processes the context-aware data and either provides immediate action or passes it on to the cloud which is an elastic network-based processing.
[0136] The SRD shopper texture analytics use a decision tree (binary tree) where each non-leaf node has two sub-nodes. The implementation is used for both classification and regression analysis. For classification, each tree leaf is marked with a class label and where multiple leaves may have the same label. For regression, a constant is assigned to each tree leaf, so the approximation function is piecewise constant.
[0137] In predicting which requires reaching a leaf node and obtaining a response for the input a shopper to product interaction feature vector (shopping texture), the prediction routine starts with the root-node. From each non-leaf node the routine goes to the left selecting the left sub-node as the next observed node or to the right based on the value of a certain variable whose index is stored in the observed node.
[0138] There are two types of variables: ordered variables and categorical variables. An ordered variable is a value that is compared with a threshold that is also stored in the node. If the value is less than the threshold, the routine goes to the left and if greater, it goes to the right.
[0139] A categorical variable is a discrete value that is tested to see whether it belongs to a certain subset of values from a limited set of values the variable can take and this stored in the node. If the categorical variable belongs to the limited set of values, the routine goes to the left and if not, it goes to the right.
[0140] In each node, a pair of entities (variable_index, decision_rule (threshold/subset)) is used. This pair is called a split which is a split on the variable variable_index. Once a leaf node is reached, the value assigned to this node is used as the output of the prediction routine.
[0141] The tree is built recursively, starting from the root node. All training data which are feature vectors and responses are used to split the root node. In each node the optimum decision rule on the best primary split is found based on gini "purity" criteria for classification and sum of squared errors for regression.
[0142] When the tree is built, it may be pruned using a cross-validation routine. To avoid model over-fitting, some of the branches of the tree are cut off. This routine is usually only applied to standalone decision trees. Typically, tree ensembles build trees that are small enough and use their own bounded schemes against over-fitting.
[0143] One salient property of the decision tree algorithm is an ability to compute the relative decisive power & importance of each variable. For a shopper to product interaction feature vector (