Adobe, Inc.

Ankur Jain

Mohit Garg

Balaji Vasan Srinivasan

Tanya Goyal

Sanket Vaibhav Mehta

Vivek Kumar

Pawan Goyal

Deepak Kumar

Suvrat Chaturvedi

Varun Dua

Frank Jennings

Smitha V

Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitate identification of security policies for documents. In one embodiment, content features are identified from a set of documents having assigned security policies. The content features and corresponding security policies are analyzed to generate a security policy prediction model. Such a security policy prediction model can then be used to identify a security policy relevant to a document.

Tagging documents with security policies

Electronic form creation techniques are disclosed which automatically recommend candidate form objects to include in an electronic form being created. In some examples, a method may include receiving a request to create an electronic form, identifying a preceding N form objects created in the electronic form, identifying a candidate form object based on the identified preceding N form objects and one or more relationships between pre-existing form objects, and recommending the candidate form object for creation in the electronic form. The pre-existing form objects are included in multiple pre-existing forms. The method may further include identifying the one or more relationships between pre-existing form objects. The pre-existing forms may be selected, for example, based on information associated with the request (e.g., form type, an ID indicating identity of author creating the form, and/or ID indicating identity of a group to which the form author belongs).

Intelligent form creation

Techniques are disclosed for automatically constructing a human-friendly and meaningful URL based on the content, context, or both, of the resource. An alternate URL can be constructed by analyzing and extracting keywords or other portions of content from a webpage automatically. The content of the webpage or other resource is obtained and analyzed using keyword extraction filters to derive one or more keywords that uniquely represent the content of the resource. An alternate URL is constructed based on the keywords. The keywords can be extracted from any portion of the content or from dictionary definitions or similar meanings of the content. The alternate URL is constructed such that it is unique among existing URLs. The alternate URL meaningfully represents the content, permitting the user to use or share the alternate URL on social media and elsewhere.

Automatic uniform resource locator construction

Techniques are disclosed for automatically detecting anomalies in the content of a scheduled social media post, alerting a user to the presence of such anomalies before the content is posted and recommending a course of action when an anomaly is detected. A set of keywords is extracted from a scheduled post using an ontological classification technique. At predetermined time intervals, the keywords are compared with information obtained from one or more data sources to determine if an anomaly is present. If an anomaly is detected, the scheduled post is classified into one of at least three categories: supporting the post, neutral, or opposing the post. Once the anomaly is detected and the scheduled post is classified, the author of the post is alerted to the anomaly along with the categorization. Subsequently, the author may reschedule the post to an earlier or later time, delete the post, or change the post.

Automatic anomaly alerts for scheduled posts

This document describes techniques for associating social data with user profiles. In one or more implementations, a social post is targeted to a population of users on a social network. The social post includes a link to a resource, such as a website. The resource is then monitored for user access via the link. When a user accesses the resource, social data corresponding to the social post is associated with a user profile of the user. The social data can include targeting parameters of the social post, such as an age, sex, or location of users to which the social post is targeted. The social data may also include other parameters related to the content of the social post, such as one or more topics of the social post.

Growing community of inventors

Noida, India

Ankur Jain