Abstract: A system is provided for identifying consumer packaged goods (CPGs).

Abstract: A system is provided for identifying consumer packaged goods (CPGs).

Abstract: A system is provided for identifying consumer packaged goods (CPGs).

Abstract: A system for managing consumer packaged goods (CPGs) is disclosed. The system includes a front end and a back end. The front end includes a mobile, airborne platform equipped with a digital image capturing device, and a wireless communications device. The backend is in communication with the front end via the wireless communications device, and includes a combinational convolutional neural network which derives models based on input data, a convolutional neural network which generates perception scoring utilizing input put from said combinational convolutional neural network, and a recurrent neural network which makes behavior predictions based on input from said convolutional neural network. The front end captures images of CPGs on a shelf, wherein the CPGs are subject to depletion over time, and wherein the backend generates predictions regarding the depletion state of the CPGs on the shelf as a function of time.

Abstract: A system is provided for identifying consumer packaged goods (CPGs).

Abstract: A method is provided for identifying consumer packaged goods (CPGs). The method comprises (a) identifying a set of objects as being a set of CPGs by applying a first level of object recognition to the set of objects, wherein said set of objects has N members, wherein N?2, and wherein applying the first level of object recognition to the set of objects includes applying a first predefined set of criteria to the object; (b) for each of the N objects, (i) performing a second level of object recognition on the object by applying a second predefined set of criteria to the object, (ii) assigning the object to one of a plurality of predefined superclasses S=[Sl, . . .

Abstract: A method is provided for identifying consumer packaged goods (CPGs). The method comprises (a) identifying a set of objects as being a set of CPGs by applying a first level of object recognition to the set of objects, wherein said set of objects has N members, wherein N?2, and wherein applying the first level of object recognition to the set of objects includes applying a first predefined set of criteria to the object; (b) for each of the N objects, (i) performing a second level of object recognition on the object by applying a second predefined set of criteria to the object, (ii) assigning the object to one of a plurality of predefined superclasses S=[Sl, . . .

Abstract: A system for managing consumer packaged goods (CPGs) is disclosed. The system includes a front end and a back end. The front end includes a mobile, airborne platform equipped with a digital image capturing device, and a wireless communications device. The backend is in communication with the front end via the wireless communications device, and includes a combinational convolutional neural network which derives models based on input data, a convolutional neural network which generates perception scoring utilizing input put from said combinational convolutional neural network, and a recurrent neural network which makes behavior predictions based on input from said convolutional neural network. The front end captures images of CPGs on a shelf, wherein the CPGs are subject to depletion over time, and wherein the backend generates predictions regarding the depletion state of the CPGs on the shelf as a function of time.