Caringo, Inc.

Pensa Systems, Inc.

Tangoe, Inc.

James E Dutton

Laura Arbilla

David Yoakley

Joel Iventosch

Sean D Matthews

David E Langworthy

William Charles Marsh

Richard Schwartz

Pinar Kaprali Gorsev

James B Casey, Jr

James M Morrison

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.

System for managing consumer packaged goods

A system is provided for identifying consumer packaged goods (CPGs). The system comprises an imaging device mounted on a mobile platform; a CPG detector which is equipped with a neural network and which (a) utilizes the imaging device to obtain an image containing a plurality of CPGs, (b) detects a set of CPG superclass features in the image, and (c) generates a CPG feature map which maps the location of CPG features in the image, wherein said neural network has been trained on a set of images of CPGs to recognize which of a set of superclasses a detected CPG belongs to; a region proposal network which accepts the CPG feature map as input and which returns a first set of regions in the image where a CPG could be located; a superclassifier which examines each region in the first set of regions to determine if the region contains an instance of a superclass of a CPG, and which outputs a second set of regions containing a CPG; and a superclass proposal layer which operates on each member of the second set of regions and returns a set of superclass proposals for each region.

System for detecting and classifying consumer packaged goods

System for scaling fine-grained object recognition of consumer packaged goods

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&#x2265;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=[S, . . . , S], wherein j&#x2265;2, based on the results of the second level of object recognition, (iii) applying a bounding box to the object, (iv) capturing an image of the object with an image capturing device, and (v) cropping the image to the bounding box, thereby yielding a cropped image of the object; and (c) for each object in each set S&#x3b5;S, (i) performing a third level of object recognition on the cropped image of the object by applying a set of criteria Cto the object, and (ii) assigning the object to one of a plurality of predefined subclasses B=[B, . . . , B], wherein k&#x2265;2, based on the results of the third level of object recognition.

Method for scaling fine-grained object recognition of consumer packaged goods

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.

Adaptive power conservation in storage clusters

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. In an archive mode, writes bids are reversed meaning that nodes with less capacity submit lower bids, meaning that fuller nodes fill up faster and are then idled, while empty or near empty nodes may remain idle for some time before winning a write bid.

Method for processing a request by selecting an appropriate computer node in a plurality of computer nodes in a storage cluster based on a calculated bid value in each computer node

Method for processing a request by selecting an appropriate computer node in a plurality of computer nodes in a storage cluster based on the least submitted bid value

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. The namespace may be shared between applications because when one application modifies a file or its attributes using the namespace, another application using a different pathname scheme has access to the exact same data and modifications. The namespace provides a near instantaneous continuous backup for computer files in the storage cluster because of the use of start date and end dates for the bindings and attributes.

Shared namespace for storage clusters

Systems and methods for pooling a plurality of telecommunication users into groups is disclosed. One method includes ranking the telecommunication users in an order based on a usage parameter. The usage parameter, for example, may be the average service usage time per billing period. Once ranked, the telecommunication users are pooled into at least two groups, each group comprising at least one telecommunication user within a range of ranks. The method may further include calculating estimated costs for the users to use cost-effective rate plans selected from a group of available rate plans and re-pooling the users into other possible combinations to determine the most cost-effective pooling combination and respective rate plans.

Growing community of inventors

Spicewood, TX, United States of America

James E Dutton