Adobe, Inc.

Shriram Revankar

Balaji Vasan Srinivasan

Ritwik Sinha

Anandhavelu Natarajan

Vineet Gupta

Deepak Pai

Shyam Sundar Rajagopalan

Arsh Sood

Parimi Krishna Chaitanya

Tanya Goyal

Anuj Jain

Anandhavelu N

Balaraman Ravindran

Nikhil Mohan Nainani

Kartik Sreenivasan

David Nicholas Bieselin

Anand Ganesan

Japnik Singh

Bharat Polineni

The collection of social data from social networking services for market research purposes is improved by automating the formulation of a final query rule sets by analyzing relevant keywords captured using a seed query rule set based on a user input. Relevant keywords from social mentions based on a seed query rule set for a brand name and/or product can be extracted to form at least parts of a final query rule set. The relevant keywords can then be disambiguated by using a searchable reference to resolve ambiguities. Any generalities in the relevant keywords can also be resolved by analyzing the co-occurrence of the relevant keywords with relevant keywords from another brand and/or product. The relevant keywords having been disambiguated and having generalities resolved therein, may be used to automatically generate a final query rule set for providing relevant search results based on the user input.

Method to expand seed keywords into a relevant social query

A method and apparatus for prediction of community reaction to a post for an online social community is disclosed. The method comprises receiving a proposed post as input to a generated prediction model prior to the proposed post being posted to an online social community; predicting a community reaction to the proposed post using the prediction model; and displaying the predication, wherein the prediction comprises a sentiment score and at least one of a number of responses, a number of responders to the post, a longevity of the post, or a half-life of the post.

Method and apparatus for prediction of community reaction to a post

Computer-implemented methods and systems are disclosed for making a recommending providing a post on a social media forum. One exemplary embodiment involves utilizing machine-learning techniques to produce a model capable of determining optimal post recommendations from various posting factors. The model may be produced from historical post information regarding various posts made by, for instance, marketers on a social media forum and corresponding community interest responses to the posts made by the community of users associated with the social media forum. The model may be provided to a recommendation engine.

Optimal time to post for maximum social engagement

This document describes techniques for predicting reactions to short-text posts. In one or more implementations, a prediction model for short-text posts is generated from previous posts to a social network and responses to the posts by the social network community. Subsequently, the prediction model can be used to predict the social network community's reaction to a proposed post prior to the proposed post being posted to the social network.

Predicting reactions to short-text posts

Systems and methods are presented for processing sequences of video frames in a distributed video coding environment. Video frames chosen as key video frames are encoded in their entirety on a sending device, and the encodings are forwarded to a receiving device. Non-key video frames are partitioned into blocks of pixels which are individually processed at the sending device. Some pixel blocks are designated for reconstruction at the receiving device using a similar corresponding pixel block from a previous video frame, and the sending device does not encode those pixel blocks. Other pixel blocks are compressed at the sending device into representations that are sent to the receiving device to facilitate reconstruction of the other pixel blocks at the receiving device. Exceptional efficiency and accuracy may be achieved by employing spatiograms and singular value decompositions in processing pixel blocks of the non-key video frames at the sending device.

Efficient encoding of video frames in a distributed video coding environment

Systems and methods are presented for reconstructing sequences of video frames received from a sending device in a distributed video coding environment. Some of the video frames are received as key video frames fully encoded at the sending device. Others are received as non-key video frames partitioned into pixel blocks. Some of the pixel blocks are indicated for reconstruction via a corresponding previous pixel block of a previous video frame, while other pixel blocks are to be reconstructed by decoding encodings of those pixel blocks received from the sending device. Each received encoding of a pixel block is based on contents of the non-key video frame that contains the pixel block and not on contents of the previous key video frame, nor on contents of any other video frame positioned between the previous key video frame and the non-key video frame in the sequence of video frames.

Reconstructing efficiently encoded video frames in a distributed video coding environment

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Bengaluru, India

Shriram Revankar