Abstract: A method is provided in one example and includes receiving network traffic associated with a first user and a second user; evaluating keywords in the network traffic in order to identify a topic of discussion involving the first and the second users; determining a first sentiment associated with a first data segment associated with the first user; determining a second sentiment associated with a second data segment associated with the second user; and generating an argument map based on the first data sentiment and the second data sentiment.

Abstract: A method includes receiving a request over a network from a user to mount a shared folder, which is configured to store electronic data to be selectively accessed based on a policy. The method also includes evaluating whether the user is authenticated. The method also includes generating a random mount point for mounting the shared folder, and redirecting the user to the random mount point if the user is authenticated.

Abstract: A method is provided in one example and includes receiving video data propagating in a network environment; maintaining the video data in a buffer having a predetermined size; generating meta-information associated with the video data; and receiving a recording request from an end user participating in a video session associated with a video data. The recording request triggers the video data to be recorded in the network environment remote from the end user.

Abstract: In one implementation, speech or audio is converted to a searchable format by a speech recognition system. The speech recognition system uses a language model including probabilities of certain words occurring, which may depend on the occurrence of other words or sequences of words. The language model is partially built from personal vocabularies. Personal vocabularies are determined by known text from network traffic, including emails and Internet postings. The speech recognition system may incorporate the personal vocabulary of one user into the language model of another user based on a connection between the two users. The connection may be triggered by an email, a phone call, or an interaction in a social networking service. The speech recognition system may remove or add personal vocabularies to the language model based on a calculated confidence score from the resulting language model.

Abstract: A method is provided in one example and includes receiving data propagating in a network environment and separating the data into one or more fields. At least some of the fields are evaluated in order to identify nouns and noun phrases within the fields. The method also includes identifying selected words within the nouns and noun phrases based on a whitelist and a blacklist. The whitelist includes a plurality of designated words to be tagged and the blacklist includes a plurality of rejected words that are not to be tagged. A resultant composite is generated for the selected nouns and noun phrases that are tagged. The resultant composite is incorporated into the whitelist if the resultant composite is approved.

Abstract: An example method is provided and includes analyzing a digital advertisement configured for display on a digital sign; identifying a tag event that includes a keyword provided in the digital advertisement; identifying a time interval associated with the tag event; and recording the tag event and the time interval in a log. In more specific embodiments, the tag event further comprises an identification of a speaker in the digital advertisement. The digital advertisement can be identified by a token that may be associated with a hash having a time derived attribute.

Abstract: A method is provided in one example and includes evaluating network traffic; identifying data within the network traffic that is associated with a uniform resource locator (URL); identifying at least one condition associated with the URL; and classifying the URL as a transitional web page or as a valuable web page based on the condition.

Abstract: A method is provided in one example and includes receiving network data from a plurality of users; identifying a data file within the network data; determining whether a particular user associated with the data file is authenticated for a communications platform; identifying an access right associated with the data file; and providing the data file to a video portal, wherein the access right associated with the data file is maintained as the data file is provided to the video portal.

Abstract: A method is provided in one example and includes receiving network traffic associated with a first user and a second user; evaluating keywords in the network traffic in order to identify a topic of discussion involving the first and the second users; determining a first sentiment associated with a first data segment associated with the first user; determining a second sentiment associated with a second data segment associated with the second user; and generating an argument map based on the first data sentiment and the second data sentiment.

Abstract: A method is provided in one example and includes receiving network traffic associated with a particular user; developing a personal vocabulary for the particular user based on the network traffic; determining areas of interest for the particular user based on the personal vocabulary; determining associations for the particular user in relation to additional users; and generating a feed based on a portion of the network traffic. The feed is delivered to a subset of the additional users.

Abstract: In one implementation, speech or audio is converted to a searchable format by a speech recognition system. The speech recognition system uses a language model including probabilities of certain words occurring, which may depend on the occurrence of other words or sequences of words. The language model is partially built from personal vocabularies. Personal vocabularies are determined by known text from network traffic, including emails and Internet postings. The speech recognition system may incorporate the personal vocabulary of one user into the language model of another user based on a connection between the two users. The connection may be triggered by an email, a phone call, or an interaction in a social networking service. The speech recognition system may remove or add personal vocabularies to the language model based on a calculated confidence score from the resulting language model.

Abstract: An example method includes receiving network traffic associated with an end user and evaluating the network traffic to identify network activity associated with the end user. The method includes generating a presence status associated with the end user based on the network activity, the presence status is deduced automatically without involving active participation from the end user. In more specific embodiments, the presence status is integrated into a directory in which presence status for a plurality of end users is stored. Initially, the end user can be marked in an unavailable state, where authentication to a network causes the presence status to change to an available state. In other embodiments, calendar events and telephone events can be tracked for the end user in order to update the presence status. Additionally, at least one parameter associated with the presence status is hidden based on configurable end user privacy preferences.

Abstract: A method is provided in one example and includes receiving data propagating in a network environment and separating the data into one or more fields. At least some of the fields are evaluated in order to identify nouns and noun phrases within the fields. The method also includes identifying selected words within the nouns and noun phrases based on a whitelist and a blacklist. The whitelist includes a plurality of designated words to be tagged and the blacklist includes a plurality of rejected words that are not to be tagged. A resultant composite is generated for the selected nouns and noun phrases that are tagged. The resultant composite is incorporated into the whitelist if the resultant composite is approved.

Abstract: In an example embodiment, an apparatus comprising a communication interface configured to be in data communication with another device, and processing logic that is operably coupled to the communication interface. The processing logic is operable to process a packet received via the communication interface, the packet comprising a header and a payload. The processing logic is configured to acquire information about the contents of the payload from the header. For example, the processing logic can determine from the header of the packet whether the payload contains sensitive data; contains analytic, video, and/or audio data; and/or whether the payload is encrypted.

Abstract: In one example embodiment, a method includes receiving at a server during a first media file streaming session to a user over a network a request from the user to pause streaming the media file at a selected time stamp point in the file. The server receives a request from the user to suspend the session. The server stores information in a memory information corresponding to the suspended streaming of the media file based on the request to suspend. The server may also resume streaming the media file to the user in a second media file streaming session from the specified streaming suspension point.