Intellectual Ventures Fund 83 LLC

Other

Thomson Licensing

Abdolreza Abdolhosseini Moghadam

Alexander C Loui

Jiebo Luo

Mrityunjay Kumar

Sara Sohrabi

Xiaoan Lu

Joel Sole

A system and method for dynamic pricing of food items that are configured to apply one or more algorithms to prepare adjustments in the food item ingredient composition (e.g., the recipe of a food item) to accommodate a desired price, caloric content, dietary restriction requirement, and/or nutritional value. The system will process the customer-user's inputs of a modification of the ingredients for the food item. System processes for recipe modifications can be made in real-time, and may be locally processed or remotely processed, e.g., via access to a network server. Optionally, some or all of the system processes for recipe modifications may be preprocessed and stored in a system-accessible database. In some embodiments, a custom designed secure hardware component is deployed in the system in order to protect confidential pricing data, and gives a tool for managing the dynamic sales for a food retail establishment.

Systems and methods for dynamic pricing of food items

Methods and apparatus are provided for video encoding and decoding using a motion matrix. An apparatus includes a video encoder for encoding a picture in a video sequence using a motion matrix. The motion matrix has a rank below a given threshold and a sparse representation with respect to a dictionary. The dictionary includes a set of atoms and basis vectors for representing the picture and for permitting the picture to be derived at a corresponding decoder using only the set. The dictionary formed from a set of reference pictures in the video sequence.

Methods and apparatus for video encoding and decoding using motion matrix

A method for representing a video sequence including a time sequence of input video frames, the input video frames including some common scene content that is common to all of the input video frames and some dynamic scene content that changes between at least some of the input video frames. Affine transform are determined to align the common scene content in the input video frames. A common video frame including the common scene content is determined by forming a sparse combination of a first basis functions. A dynamic video frame is determined for each input video frame by forming a sparse combination of a second basis functions, wherein the dynamic video frames can be combined with the respective affine transforms and the common video frame to provide reconstructed video frames.

Video representation using a sparsity-based model

A method for determining a scene boundary location dividing a first scene and a second scene in an input video sequence. The scene boundary location is determined responsive to a merit function value, which is a function of the candidate scene boundary location. The merit function value for a particular candidate scene boundary location is determined by representing the dynamic scene content for the input video frames before and after candidate scene boundary using sparse combinations of a set of basis functions, wherein the sparse combinations of the basis functions are determined by finding a sparse vector of weighting coefficients for each of the basis functions. The weighting coefficients determined for each of the input video frames are combined to determine the merit function value. The candidate scene boundary providing the smallest merit function value is designated to be the scene boundary location.

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San Jose, CA, United States of America

Abdolreza Abdolhosseini Moghadam