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 method for obtaining a computed expected facing count (CEFC), including obtaining a sequence of actual facing counts (AFCs) during a specified sample window for each of a plurality of stock keeping units (SKUs), wherein each SKU is associated with one of a plurality of products; identifying a subset of the sequence of AFCs as candidate restock events at which it is assumed the product associated with an SKU has been replenished in a shelving area since a previous AFC observation; selecting a set of restock events that are most likely to represent the intentional restock level (EFC) for that SKU; preparing a plurality of EFC lists, wherein each of said plurality of EFC lists contains the EFCs for all the SKUs in a specified shelf area; periodically updating the plurality of EFC lists, thereby obtaining a plurality of updated EFC lists; and using the plurality of updated EFC lists to compute at least one Key Performance Indicator (KPI) for at least one shelf area.

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

Abstract: A method is provided for assigning a classification to consumer packaged goods (CPGs). The method includes capturing an image of a plurality of CPGs arranged on a shelf; providing the captured image to a CPG detector; identifying all of the CPGs in the image; producing a set of cropped images, wherein each cropped image shows a single CPG as it appears in the image; and for each member of the set of cropped images, assigning a classification to the CPG in the member of the set of cropped images and establishing a confidence for the assigned classification through a process that includes the steps of (a) identifying a first set of reference images of CPGs whose classification is known, wherein each member of the first set of reference images is semantically similar to the member of the set of cropped images, and (b) identifying details in the member of the set of cropped images that differentiates it from a second set of reference images of CPGs whose classification is known.

Abstract: A method is provided for training at least one classifier model used by an artificial intelligence (AI) system to recognize each of a set of objects and to assign each of the set of objects to a class.

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 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 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: Each node and volume in a storage cluster makes a decision whether to reduce power consumption based on lack of requests from client applications and nodes over a time period. Node configuration parameters determine how long to wait until idling a node or volume, and how long to wait while idle before performing integrity checks. A bid value is calculated by each node and reflects how much it will cost for that node to write a file, read a file, or keep a copy. A node with the lowest bid wins, and nodes that are idle have a premium added to each bid to ensure that idle nodes are kept idle. In an archive mode, writes bids are reversed, nodes with less capacity submit lower bids, fuller nodes fill up faster and are then idled, while empty or near empty nodes may remain idle before winning a write bid.

Abstract: Each node and volume in a storage cluster makes a decision whether to reduce power consumption based on lack of requests from client applications and nodes over a time period. Node configuration parameters determine how long to wait until idling a node or volume, and how long to wait while idle before performing integrity checks. A bid value is calculated by each node and reflects how much it will cost for that node to write a file, read a file, or keep a copy. A node with the lowest bid wins, and nodes that are idle have a premium added to each bid to ensure that idle nodes are kept idle. In an archive mode, writes bids are reversed, nodes with less capacity submit lower bids, fuller nodes fill up faster and are then idled, while empty or near empty nodes may remain idle before winning a write bid.

Abstract: Each node and volume in a storage cluster makes a decision whether to reduce power consumption based on lack of requests from client applications and nodes over a time period. Node configuration parameters determine how long to wait until idling a node or volume, and how long to wait while idle before performing integrity checks. A bid value is calculated by each node and reflects how much it will cost for that node to write a file, read a file, or keep a copy. A node with the lowest bid wins, and nodes that are idle have a premium added to each bid to ensure that idle nodes are kept idle. In an archive mode, writes bids are reversed, nodes with less capacity submit lower bids, fuller nodes fill up faster and are then idled, while empty or near empty nodes may remain idle before winning a write bid.

Abstract: Each node and volume in a fixed-content storage cluster makes an independent decision whether to reduce power consumption based on lack of requests from client applications and nodes over a configurable time period. Node configuration parameters sleepAfter and wakeAfter respectively determine how long to wait until idling a node or volume, and how long to wait while idle before again performing integrity checks. A bid value is calculated by each node and reflects how much it will cost for that node to write a file, read a file, or keep a copy of the file. A node with the lowest bid wins, and nodes that are idle have a premium added to each bid to help ensure that idle nodes are kept idle. Normally, nodes with more capacity will submit a lower bid to write a file.

Abstract: Each node and volume in a storage cluster makes a decision whether to reduce power consumption based on lack of requests from client applications and nodes over a time period. Node configuration parameters determine how long to wait until idling a node or volume, and how long to wait while idle before performing integrity checks. A bid value is calculated by each node and reflects how much it will cost for that node to write a file, read a file, or keep a copy. A node with the lowest bid wins, and nodes that are idle have a premium added to each bid to ensure that idle nodes are kept idle. In an archive mode, writes bids are reversed, nodes with less capacity submit lower bids, fuller nodes fill up faster and are then idled, while empty or near empty nodes may remain idle before winning a write bid.

Abstract: Each node and volume in a storage cluster makes a decision whether to reduce power consumption based on lack of requests from client applications and nodes over a time period. Node configuration parameters determine how long to wait until idling a node or volume, and how long to wait while idle before performing integrity checks. A bid value is calculated by each node and reflects how much it will cost for that node to write a file, read a file, or keep a copy. A node with the lowest bid wins, and nodes that are idle have a premium added to each bid to ensure that idle nodes are kept idle. In an archive mode, writes bids are reversed, nodes with less capacity submit lower bids, fuller nodes fill up faster and are then idled, while empty or near empty nodes may remain idle before winning a write bid.

Abstract: Each node and volume in a storage cluster makes a decision whether to reduce power consumption based on lack of requests from client applications and nodes over a time period. Node configuration parameters determine how long to wait until idling a node or volume, and how long to wait while idle before performing integrity checks. A bid value is calculated by each node and reflects how much it will cost for that node to write a file, read a file, or keep a copy. A node with the lowest bid wins, and nodes that are idle have a premium added to each bid to ensure that idle nodes are kept idle. In an archive mode, writes bids are reversed, nodes with less capacity submit lower bids, fuller nodes fill up faster and are then idled, while empty or near empty nodes may remain idle before winning a write bid.

Abstract: Any client application uses a namespace application to resolve its pathname in order to reference a computer file. Computer files are stored in a fixed-content storage cluster and are accessed by retrieving a unique identifier for the computer file using the namespace application. Any type of pathname scheme from any client application is supported by the namespace. The namespace application uses a bindings table to record bindings between objects including the start date and end date for each binding, and direction and separator data used in the pathname scheme. An attribute table in the namespace keeps track of each attribute and its value for each object of the namespace including a start date and an end date for each attribute. The namespace provides syntactic generality in that any pathname scheme of a client application can be resolved to identify a unique computer file in the storage cluster.