Adobe, Inc.

Harsh Jhamtani

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Natwar Modani

Midhun Gundapuneni

Shubham Varma

Siddhartha Kumar Dutta

Cedric Huesler

Laurie Marie Byrum

Calvin K C Wong

Gaurush Hiranandani

Anandhavelu Natarajan

Moumita Sinha

Shriram Venkatesh Shet Revankar

Payal Bajaj

Sanket Vaibhav Mehta

Anandhavelu N

Jonas Dahl

Vineet Gupta

Rishiraj Saha Roy

Shriram V S Revankar

Devesh Varshney

Deepam Kedia

Shweta M Karwa

Systems and methods provide for expanding user-provided content. User-provided input content is received via a user interface. Content that is relevant to the user-provided input content is identified from a repository of previously-generated content. The identified relevant content is divided into content sub-segments. From the content sub-segments, one or more pieces of candidate content are identified based on each content sub-segment's relevance to the received input content. At least one piece of identified candidate content is provided for display. A selection of one or more pieces of identified candidate content is received, such that the selected piece(s) of identified candidate content is appended to the received input content, thereby expanding the user-provided content.

Expanding input content utilizing previously-generated content

Disclosed are various embodiments for automatically creating on a computer analytics tags for different object types of website objects in web pages with analytics tracking capability in a dynamic tag management system. In one implementation, user input is received identifying a website object for tagging in the web pages and keywords are identified based on the user input. Based on the keywords, multiple occurrences of the website object in the web are identified, wherein the multiple occurrences of the website object correspond to multiple object types. The computer automatically creates analytics tags for the website object corresponding to object types. Based on the website object, an expansion object is identified and the computer automatically creates an analytics tag for the expansion object.

Methods and systems for tag expansion by handling website object variations and automatic tag suggestions in dynamic tag management

A method for generating summaries includes selecting a first subset of text units of a text composition to incorporate into a first summary of the text composition using a weighting of the text units that indicates for each text unit a relative importance of including the text unit in summaries of the text composition. The weighting of the text units is modified to reduce the relative importance of each text unit in the first subset based on the text unit having been selected for the first subset. A second subset of the text units is selected to incorporate into a second summary of the text composition using the modified weighting of the text units. At least one of the first summary and the second summary are provided to a user device.

Probabalistic generation of diverse summaries

Certain embodiments involve creating multimedia content with text and media assets that illustrate the text. Multiple sentences are ranked on various features. A sentence ranking is determined based on, for example, the presence of important phrases in the sentence, the degree to which informational content of the sentence can be represented through a media asset, the presence of one or more sentiments associated with the sentence, and the readability of the sentence. In some examples, the ranked sentences are analyzed to determine similar information content, and the sentences are re-ranked based on this analysis. A subset of the ranked sentences with higher ranks are analyzed to determine similarities between content in the sentence and text descriptions of media assets. This analysis can be used to select appropriate images or other media assets. Multimedia content is generated in which the selected media assets are positioned near the set of sentences.

Generating recommendations for media assets to be displayed with related text content

Techniques and systems are described to determine levels of competency of users as part of an online community and control generation of subsequent digital content to be used interaction of the online community with the users based on this determination. In one example, determination of the level of competency is based on relevance to topics of the online community. In another example, a determination is made as to whether the topic of the online community is stable before using user competency scores to control generation of subsequent digital content. In a further example, users of the online community are identified as exhibiting dormant or non-dormant behavior and used as a basis to control generation of subsequent digital content. In yet another example, user competency scores are adjusted based on a decay factor to address dormancy of users over a period of time.

Levels of competency in an online community

In embodiments of identifying the end of an on-line cart session, an analytics application captures user click inputs on pages of a Web site, where the user click inputs include adding one or more items for purchase to an on-line cart associated with the Web site. The analytics application then utilizes a predictive model, as well as user and session features of the on-line cart session, to predict whether a previous user click input is the last user click input associated with the on-line cart session, indicating an end of the session. A notification can then be provided that the on-line cart session has ended based on the prediction of the last user click input associated with the on-line cart session. The analytics application or the marketer can then retarget a user associated with the on-line cart session, such as with a message pertaining to the on-line cart session.

Identifying the end of an on-line cart session

A method for propagating changes from master content to variant content includes receiving, from a user interface, a modified master text composition that incorporates changes to a master text composition. Each of the master text composition, the modified master text composition, and a variant text composition assigned to the master text composition are divided into text units. A correspondence model is constructed that includes mappings between the text units of the master text composition, the text units of the modified master text composition, and the text units of the variant text composition. Proposed changes to the variant text composition are determined that correspond to the changes to the master text composition based on the mappings. At least one of the proposed changes is integrated into the variant text composition.

Propagation of changes in master content to variant content

A text sentence is automatically converted to an image sentence that conveys semantic roles of the text sentence. This is accomplished by identifying semantic roles associated with each verb of a sentence, any associated verb adjunctions, and identifying the grammatical dependencies between words and phrases in a sentence, in some embodiments. An image database, in which each image is tagged with descriptive information corresponding to the image depicted, is queried for images corresponding to the semantic roles of the identified verbs. Unless a single image is found to depict every semantic role, the text sentence is split into two smaller fragments. This process is the repeated and performed recursively until a number of images have been identified that describe each semantic role of each sentence fragment.

Converting a text sentence to a series of images

This document describes techniques for identifying suggestive intent in social posts. In one or more implementations, a topic is received and social posts to one or more social networks that are related to the topic are collected. Then, one or more suggestive intent posts expressing suggestions towards the topic are identified from the collected social posts. In one or more implementations, a set of related topics are received and social posts to one or more social networks that correspond to the related topics are collected. Then, aspects corresponding to the related topics are identified, and a sentiment score is generated for each aspect of each related topic that can be used to compare aspects of the related topics. In one or more implementations a suggestive intent model, usable to identify social posts expressing suggestive intent, is built from a training corpus of annotated social posts.

Growing community of inventors

Kanpur, India

Harsh Jhamtani